Hype Cycle for Healthcare Pr 314257

This research note is restricted to the personal use of [email protected].

Hype Cycle for Healthcare Providers, 2017 Published: 14 Ju July ly 20 2017 17

ID: G00314257

Analyst(s): Laura Craft, Vi Craft, Vi Shaffer

This Hype Cycle Cycle provides critical input for strategic planning. It helps CIOs to communicate with boards, executives and stakeholders about the future directions of health he alth IT, and to identify, understand and prioritize significant investments in applicatio applications, ns, analytics and "medical IT" advances. Table T able of Contents Analysis................... Analysis.......... ..................... ......................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... ........................3 ...........3 What You Need to Know........... Know........................ ......................... ......................... .......................... .......................... .......................... .......................... .......................... ............. 3 The Hype Cycle.............. Cycle........................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... ....................... ..........3 3 Additions and and Adjustments to This Year's Hype Cycle............ Cycle......................... ......................... ......................... .......................... ............... 4 The Priority Matrix............ Matrix......................... .......................... .......................... .......................... .......................... .......................... ......................... ......................... .......................6 ..........6 Off the Hype Cycle............ Cycle......................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... .................... ....... 9 On the Rise.............. Rise........................... .......................... .......................... ......................... ......................... .......................... .......................... .......................... .......................... ................. .... 9 Digital Speech Speech Analysis for Clinical Diagnoses........... Diagnoses........................ .......................... .......................... .......................... .......................... ..............9 .9 Health Data Convergence Hub.......... Hub....................... ......................... ......................... .......................... .......................... .......................... ....................... .......... 10 AI-Enabled AI-Enabl ed Diagnostic Imaging Interpretation..... Interpretation.................. .......................... ......................... ......................... .......................... ....................12 .......12 Precision Medicine........... Medicine....................... ......................... .......................... .......................... .......................... .......................... .......................... .......................... ...............13 ..13 Healthcare Healt hcare Algorithmic Marketplace......... Marketplace...................... .......................... .......................... .......................... ......................... ......................... .................15 ....15 Blockchain in Healthcare....... Healthcare.................... .......................... .......................... .......................... .......................... ......................... ......................... ...................... ......... 17 Patient Decision Aids............ Aids........................ ......................... .......................... .......................... .......................... .......................... .......................... ....................... ..........19 19 AI for Healthcare Providers.......... Providers....................... .......................... .......................... .......................... .......................... .......................... .......................... ............... .. 21 3D Bioprinted Organ Transplants......... ransplants...................... .......................... .......................... .......................... .......................... .......................... .................... ....... 23 Critical Condition Surveillance Systems.......... Systems....................... ......................... ......................... .......................... .......................... ........................25 ...........25 Healthcare Analytics Architecture for Providers..... Providers.................. ......................... ......................... .......................... .......................... ................. .... 27 Real-Time Healthcare Costing........... Costing........................ .......................... .......................... .......................... .......................... .......................... ...................... ......... 28 Customer Engagement Hub.......... Hub....................... .......................... .......................... .......................... .......................... .......................... ......................... .............. 30 Healthcare Consumer Persuasion Analytics.......... Analytics....................... .......................... .......................... .......................... .......................... ................ ... 32 Nanomedicine...................... Nanomedicine......... .......................... .......................... .......................... .......................... .......................... .......................... ......................... ...................... .......... 34



Value-Based Performance Performance Management Analytics................ Analytics............................. .......................... .......................... .......................... ............. 36 At the Peak....................... Peak................................... ......................... .......................... .......................... .......................... .......................... .......................... .......................... ....................37 .......37 IoT — Hospitals............ Hospitals......................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... ................. ....37 37 Unique Uniq ue Device Identification (UDI)........ (UDI)..................... .......................... .......................... .......................... .......................... ......................... .................... ........ 39 AI Healthcare Advisors................ Advisors............................. .......................... .......................... .......................... .......................... .......................... .......................... ................41 ...41 RTHS Operations Dashboards.... Dashboards................. .......................... .......................... ......................... ......................... .......................... .......................... .................43 ....43 Eldercare-Assistive Robots............ Robots......................... .......................... .......................... .......................... .......................... .......................... ......................... .............. 45 Cloud-Generation Clou d-Generation Healthcare ERP............. ERP.......................... .......................... .......................... .......................... .......................... .......................... ..............47 .47 Healthcare CRM/Contact CRM/Contact Center Systems........... Systems........................ .......................... .......................... .......................... .......................... ................. ....49 49 3D Printed Printed Surgical Implants.......... Implants....................... .......................... .......................... .......................... ......................... ......................... .......................... ..............51 .51 Population Health Health Management Solutions.......... Solutions....................... .......................... .......................... .......................... ......................... ...................53 .......53 Genomics Medicine............ Medicine........................ ......................... .......................... .......................... .......................... .......................... .......................... .........................55 ............55 PHI Consent Management.......... Management....................... .......................... .......................... .......................... ......................... ......................... .......................... .................57 ....57 Sliding Into the T rough................... rough................................ .......................... .......................... .......................... .......................... .......................... ......................... ................... ....... 59 3D Bioprinted Bioprinted Human Tissue................ Tissue............................. .......................... .......................... .......................... .......................... .......................... ................... ...... 59 Real-Time Physician Physician Documentation Improvement.... Improvement................. .......................... .......................... .......................... ........................ ........... 60 OpenNotes............... OpenNotes.. .......................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... .....................62 ........62 Patient T hroughput hroughput and Capacity Management.......... Management...................... ......................... .......................... .......................... ........................ ........... 64 3DP Presurgery Presurg ery Anatomical Models........... Models........................ .......................... .......................... ......................... ......................... .......................... ...............66 ..66 Generation 3 Enterprise EHR Systems (Non-U.S.)........ (Non-U.S.).................... ......................... .......................... .......................... ...................... ......... 68 Track-and-T T rack-and-Trrace and Serialization........... Serialization........................ .......................... .......................... .......................... .......................... .......................... ................. .... 70 Climbing the Slope........... Slope........................ .......................... .......................... .......................... .......................... ......................... ......................... .......................... .................... ....... 72 Accountable Care Organization.......... Organization....................... .......................... ......................... ......................... .......................... .......................... ...................... ......... 72 Computer-A ssisted ssisted Coding (Hospital)......... (Hospital)...................... .......................... .......................... .......................... .......................... .......................... ............. 74 Entering the Plateau........... Plateau........................ .......................... .......................... .......................... .......................... .......................... .......................... .......................... ................. .... 76 Advanced Advance d Clinical Research Information Systems......... Systems...................... .......................... .......................... .......................... ..................... ........76 76 Healthcare Real-Time Real-Time Location System.......... System....................... .......................... .......................... .......................... .......................... ...................... ......... 78 Generation 3 Enterprise 3 Enterprise Patient Financial Systems (U.S.)............................. (U.S.).......................................... .......................... ................ ... 79 Patient Portals............ Portals......................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... ................... ...... 82 Appendixes.................... Appendixes....... .......................... .......................... .......................... .......................... .......................... .......................... ......................... ......................... ...................... ......... 83 Hype Cycle Phases, Benefit Ratings and Maturity Levels.......... Levels....................... .......................... .......................... ...................... ......... 85 Gartner Recommended Recommended Reading.......... Reading....................... .......................... .......................... .......................... .......................... ......................... ......................... ................... ...... 86

List of Tables Table T able 1. Hype Cycle Phases............ Phases......................... .......................... .......................... .......................... ......................... ......................... .......................... ........................ ........... 85 Page 2 of 88

Gartner,, Inc. | G00314257 Gartner This research note is restricted to the personal use of [email protected].


Table T able 2. Benefit Ratings............ Ratings......................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... ................. .... 85 Table T able 3. Maturity Levels.............. Levels........................... .......................... .......................... .......................... .......................... .......................... .......................... .......................... ............... .. 86

List of Figures Figure 1. Hype Cycle for Healthcare Providers, 2017............................... 2017............................................ ......................... ......................... ...................... ......... 6 Figure 2. Priority Matrix for Healthcare Providers, 2017............. 2017.......................... .......................... .......................... .......................... .......................8 ..........8 Figure 3. Hype Cycle for Healthcare Providers, 2016............................... 2016............................................ ......................... ......................... .................... ....... 84

Analysis What You You Need to Know This Hype Cycle tracks a total of 40 speci�c applications, analytics and system advances that are germane to healthcare delivery organizations (HDOs), including hospitals and integrated delivery systems. Each technology pro�le provides a de�nition of the technology technology,, a justi�cation for its position and adoption speed, and actionable advice. It also includes a bene�t rating and an assessment of the technology's market penetration pe netration and relative maturity. There is an explosion of "digital era" innovations across business and clinical domains, with particular emphasis on arti�cial intelligence (AI) and consumer engagement. Nearly half of the pro�les fall between the Innovation Trigger Trigger and the Peak of In�ated Expectations. These provide an indication of the appetite for healthcare transformation. Despite the challenges, one thing is certain: Ful�lling the extremely dif�cult and noble missions of best medicine, best care and best health will be enabled through broader ecosystems, information and communication technologies, and working digitally with others.

The Hype Cycle This Hype Cycle tracks the most signi�cant IT directions in applications, analytics, and systems combining applications and analytics. The implications affect enterprise architecture, business, clinical and IT operations in terms of capabilities, structures and accountabilities. Major take-aways from this Hype Cycle overwhelmingly point to the roles of real-time capabilities, AI, data and analytics, as highlighted below: 1.

There is no slowdown in emergent technologies and innovation: The left side of the Hype Cycle is crowded. is crowded. Pro�les on real-time capabilities, data, analytics and AI dominate. Together Together,,

these will will create a force multiplier that will accelerate industry disruption and change. 2.

AI is on the on the cusp of being a major transformative transformative force: There are several AI-related

technologies at the "on the rise" stage of early adoption a doption and maturity. Their presence signals

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continued evolution toward the era of precision medicine and other medical advances. It is as much the explosion in available data — both the volume and the variety — as it is the technologies that are causing this. The big and promising revolution is the ability to use AI and return the data in real time for both current and anticipatory actions. 3.

The rapid pace of digital advancement is pulling the research and clinical care delivery worlds closer together: HDOs and their physicians will be upended by efforts to pull exploding medical discoveries into practice pra ctice faster. Pro�les on Genomics Medicine, Precision Medicine and

the related use of AI collectively emphasize IT's need to create the real-time health system that permits collaboration, real-time data and decision support exchanges between physicians, researchers and domain knowledge clouds. 4.

Progress lies in leveraging the data, but the data and analytics have gotten far more complex, requiring new solutions: CIOs need to implement a new analytics architecture and

think innovatively in order to truly be able to consume, manage and translate into value the mass quantities of data being generated. The line between applications and analytics has faded, and real time is becoming a dominant force in clinical care and megaprocesses. Emergent and on-the-rise pro�les include Blockchain in Healthcare, the new Health Data Convergence Hub and the new Healthcare Analytics Architecture for Providers, which rede�ne and fortify the data and analytics infrastructure.

Additions and Adjustments Adjustments to This Year's Year's Hype Cycle Nine technology pro�les have been added to this year's Hype Cycle: ■

Digital Speech Analysis for Clinical Diagnoses: This pro�le tracks technologies that use

speech analysis to detect clinical abnormalities. ■

Health Data Convergence Hub: This pro�le tracks the emergence of vendors that speci�cally

address healthcare's data integration and exchange complexities. ■

AI-Enabled Diagnostic Diagnostic Imaging Imaging Interpretation: Interpretation: This pro�le recognizes the application of deep and machine learning in the �eld of radiology for interpretation as well as diagnostic ef�ciency

and accuracy acc uracy.. ■

AI for Healthcare Healthcare Providers: Providers: This umbrella pro�le acknowledges that AI is gaining serious

traction in healthcare, and is on the cusp of being a powerful transformative force. ■

Healthcare Analytics Architecture for Providers: This pro�le represents the next-generation

analytics capabilities that an HDO must have to remain competitive under population health and value-based care. ■

Real-Time Physician Documentation Improvement: This pro�le tracks the real-time AI

capabilities that are being used to improve coding accuracy at the point of care. ■

3DP Presurgery Anatomical Models: This is the newest technology pro�le in the domain of

medical uses of 3D printing, and tracks the technology used to create 3D models of targeted patient anatomy of surgical sites.

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IoT — Hospitals: This pro�le tracks the collection of "things" in and around the hospital that

form the foundation to the real-time healthcare system. ■

Healthcare Real-Time Location System: Previously, this was the Wireless Healthcare Asset Management technology pro�le on the "Hype Cycle for Real-Time Health System Technologies,

2016." It has been renamed, repositioned and added to this Hype Cycle. In addition, four pro�les have had name changes: ■

Blockchain in Healthcare (previously Healthcare Distributed Ledger and Blockchain): The

name was changed to simplify and better align with the terminology that is commonly used in the healthcare industry. ■

■

AI Healthcare Advisors (previously Smart Machines — Healthcare Sages): The name change most importantly re�ects a move away from the use of the term "smart machines" to AI, in order to be consistent with core research de�nitions. 3D Printed Surgical Implants (previously 3DP-Aided Hip/Knee Implants): The name was

changed to enable us to expand our evaluation across a broad array of 3D implant innovations that need to be considered in setting up health system innovation labs and vendor partnerships. ■

Cloud-Generation Healthcare ERP (previously Postmodern Healthcare ERP): The name

change was recommended in order to be consistent with core Gartner research on this topic.

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Gartner, Inc. | G00314257 This research note is restricted to the personal use of [email protected].


Figure 1. Hype Cycle for Healthcare Providers, 2017

Source: Gartner (July 2017)

The Priority Matrix The Priority Matrix is a companion to the Hype Cycle and maps a technology's bene�t to its time to maturity. It is generated directly from the bene�t rating and time-to-plateau values for each technology. The Priority Matrix summarizes two key Hype Cycle take-aways: ■

How much value will there be from a particular technology?

■

When will the technology be mature enough to deliver that value at a manageable risk?

Investments that potentially have a high impact and a reasonable level of maturity are located at the top-left sections. Those that have lower bene�t and a longer time to value are situated on the matrix's lower-right sections. Broadly speaking, if it's red, it's hot; if it's gray, it's not. This matrix reminds CIOs that their recent priorities — electronic health records (EHRs), integrated clinical/business enterprise data warehouse and patient portals — may become passé.

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More than 60% of these new technologies reach the Plateau of Productivity in �ve to 10 years — with the vast majority having a high or transformational bene�t. CIOs must take great care in advising executives when picking priorities based on achievement of the potential impact. Sequencing of execution must also be carefully deliberated because there are interdependencies — particularly the pervasive need for data, analytics and real time.

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Figure 2. Priority Matrix for Healthcare Providers, 2017

benefit

years to mainstream adoption less than 2 years

transformational

2 to 5 years

5 to 10 years

more than 10 years

3D Printed Surgical Implants

Generation 3 Enterprise EHR Systems (Non-U.S.)

3D Bioprinted Organ Transplants

Accountable Care Organization

Genomics Medicine

AI for Healthcare Providers

Advanced Clinical Research Information Systems

Health Data Convergence Hub IoT – Hospitals

Blockchain in Healthcare Precision Medicine

Nanomedicine Population Health Management Solutions RTHS Operations Dashboards

Computer-Assisted Coding (Hospital)

high

3D Bioprinted Human Tissue

Unique Device Identification (UDI)

AI Healthcare Advisors AI-Enabled Diagnostic Imaging Interpretation Customer Engagement Hub Digital Speech Analysis for Clinical Diagnoses Healthcare Algorithmic Marketplace Healthcare Analytics Architecture for Providers Healthcare Consumer Persuasion Analytics Healthcare CRM/Contact Center Systems Patient Throughput and Capacity Management Track-and-Trace and Serialization Value-Based Performance Management Analytics

moderate

Generation 3 Enterprise Patient Financial Systems (U.S.) Healthcare Real-Time Location System Patient Portals

OpenNotes PHI Consent Management

3DP Presurgery Anatomical Models

Patient Decision Aids

Cloud-Generation Healthcare ERP Critical Condition Surveillance Systems Eldercare-Assistive Robots Real-Time Healthcare Costing Real-Time Physician Documentation Improvement

low As of July 2017

© 2017 Gartner, Inc.


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Off the Hype Cycle Eight pro�les have been removed from this year's Hype Cycle: ■

■

Logical Data Warehouse: This pro�le has been deleted and incorporated into the Healthcare Analytics Architecture for Providers pro�le. Natural-Language Processing (Healthcare Delivery Organization): Natural-language

processing (NLP) only has value in the context of its use; therefore, we have removed this pro�le, and instead call out NLP within other pro�les that leverage NLP technology, such as many of the AI and analytics applications that also have NLP as a critical component (such as Real-Time Physician Documentation Improvement). ■

Population Health Management Platforms: This pro�le has been deleted and incorporated

into Population Health Management Solutions. ■

Provider Population Health Analytics: This pro�le has been deleted and incorporated into

Population Health Management Solutions, since it is a core component of population. ■

Care Coordination and Management Applications: This pro�le has been deleted and

incorporated into Population Health Management Solutions, since these are rarely sold to HDOs as stand-alone solutions. ■

Integrated Clinical/Business Enterprise Data Warehouse: This pro�le has been deleted and

incorporated into Healthcare Analytics Architecture for Providers. ■

Computer-Assisted Clinical Documentation Improvement (Hospital): This pro�le was

replaced with Real-Time Physician Documentation Improvement. ■

LCST App Platforms: This was replaced with two new pro�les: IoT — Hospital and Healthcare

Real-Time Location System.

On the Rise Digital Speech Analysis for Clinical Diagnoses Analysis By: Thomas J. Handler, M.D. Definition: Digital

speech analysis for clinical diagnoses evaluates an individual's linguistic variables such as pitch, tone, pauses, word choices and speech rate in order to noninvasively detect clinical abnormalities.

Position and Adoption Speed Justification: The manner in which we speak and the word choices

we make can be evaluated to accurately detect a growing list of clinical conditions, especially behavioral health issues (including depression, psychosis, dementia and PTSD) and even coronary artery disease. Startup companies and researchers are leveraging technologies to �nd ways to detect abnormalities sooner and less invasively. These language-based assessment systems often use arti�cial intelligence and machine learning to analyze speech patterns. Patients are asked to

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read paragraphs or describe a picture. The systems then analyze the audio clips in order to suggest a diagnosis. These tools can be used to replace manually intensive processes that require a great deal of expertise on the part of clinicians. Some have been shown to signi�cantly reduce patients' "test anxiety," which has been suggested can lead to erroneous results. Early work has been very impressive and while these solutions are not yet on many provider CIOs radar, we believe that before very long these solutions will be ubiquitous. What is exciting is the promise that these tools can reduce the reliance on highly specialized and often hard-to-�nd clinicians and thereby increase early treatment of what can be very debilitating illnesses. There is also the potential, with careful regard to regulatory constraints and privacy implications to ha ve these system run in the background (say when a patient calls to schedule an appointment or possibly even during any clinical encounter) so diagnoses can be made much earlier — perhaps even before the patient or clinician becomes concerned. Despite impressive early results and the potential of these solutions, the barrier to adoption remains large. Barriers include the need for more evidence regarding the ef�cacy of the products, the possible need for regulatory clearance, necessary cultural change in both clinicians and patients, legal concerns and �nancial issues. For these reasons, we believe that it will take closer to 10 years than �ve years for these systems to reach the plateau. User Advice: Provider CIOs need to work with clinical leaders to understand these solutions and to

identify potential use and business cases. Given the very early nature of the solutions, all but the most aggressive academic medical centers should proceed cautiously. Start with small pilot projects to demonstrate both ef�cacy and practicality of using these tools. It is essential to involve risk management early in any decision to implement these solutions — even in a pilot phase. Business Impact: Digital speech analysis can provide signi�cant clinical bene�ts by allowing for

more accurate, rapid and earlier detection of disease and by freeing valuable clinical time. However, it is possible that using software in place of highly trained professionals can result in lower payments, especially if clinician sign-off becomes unnecessary. Legal rami�cation of the systems missing or making an incorrect diagnosis must be factored into any business case. Benefit Rating: High

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: Beyond Verbal; Sonde Health; WinterLight Labs Recommended Reading: "Business Drivers of Technology Decisions for Healthcare Providers,

2017" "Implementing and Managing EHRs, Telemedicine and Other Care Delivery IT Primer for 2017" "Maverick* Research: Endangered! How Technology Will Cause Extinction of the Primary Care Tier of Medicine"

Health Data Convergence Hub Analysis By: Laura Craft Page 10 of 88



Definition: The

health data convergence hub is the orchestration platform that brings together data from across the consumer/citizen/patient health and wellness continuum and prepares the data for delivery to downstream consumption platforms, applications, ana lytics and "things." It automates the ingestion of data — both structured and unstructured — from all identi�ed and permissioned sources; provides tracking and traceability; and manages identity, compliance and security. It may process algorithms and deliver the output to the correct modality.

Position and Adoption Speed Justification: This technology pro�le acknowledges that the vast

amounts of data that are becoming essential to sustain health and wellness, contain healthcare costs, and make sure the customer/patient/person is engaged and satis�ed are becoming an enormous and daunting integration undertaking. The assessment of health and health risk is now relying on data sources that historically have been beyond the reach of the healthcare delivery organization, such as social determinants of health and genomic pro�le data. In addition, delivery of care is coordinated across an integrated community network and to help facilitate patient compliance, which includes broadening services like coordination of transportation and delivery of food. The result is an expanding ecosystem of care coordination and data exchange. The data needed to support all the healthcare actions is often needed in real time and is typically an aggregation of many data points to provide the holistic picture of the patient. This broadening ecosystem of data collection, sharing and delivery taxes traditional data exchange integration methods (HIEs and ETLs) that have been implemented for purpose-speci�c reasons and are less effective to manage to new data demands. Healthcare delivery organizations that are truly executing population health management will quickly feel the gaps, bottlenecks and delays created by poor data movement. The health data convergence hub, which is distinctly different from an HIE (but may rely on one as a source of information) and ETL tools, is also critical to HDOs' real-time and digital healthcare strategies. There are two approaches emerging in the industry. The �rst emerges from existing analytic and population health platform players that historically have been the integrators of bringing the disparate data together. Gartner has noted that some of the large, well-established population health/analytic vendors like Health Catalyst are re-tooling their platforms to have more powerful ingestion and data management strengths. The second approach is a new, emerging class of vendors. These vendors may have tangential solutions to support population health and analytics, but have as the primary focus of the solution data ingestion, indexing and distribution. This is their core differentiator and strength. One example is Imat Solutions, which aggregates, normalizes, indexes and codi�es all patient data from across the continuum of care to make it consumable. DataMotionHealth is another example. The compelling difference of these vendors is the expertise brought to this highly challenging layer of the architecture. We position health data convergence hubs at post-trigger 10%, a s the solutions are still early attempts to build a hub that effectively transacts, normalizes and orchestrates real-time patient/ consumer/citizen data regardless of origin or destination. Limitations driven by the lack of better industry standards may impact progress as well as adoption. However, Gartner believes that within �ve to 10 years, this will be a powerful (and standard) layer of the HDO's enterprise architecture. User Advice: Healthcare CIOs must make sure they do not become the cause of integration and

data challenges and become a bottleneck to organizational progress and transformation:

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■

■

Get ahead of the need by proactively understanding what the data integration demands across the healthcare delivery organization will be over the next three to �ve years. These needs can be culled from information in the organization's strategic plan and through other deliberate shortand long-term visioning exercises. Create requirements. Map out the patient, provider and administrative journeys, and document the ideal movement of data across the enterprise. Update the enterprise and information architectures to re�ect the future state. Develop your roadmap. Evaluate your existing EHR, population health, analytics and/or integration vendor to see if they have a roadmap and strategy to meet your demands. Understand the specialty vendors in the market and track their evolution.

Business Impact: Successful deployment of a comprehensive health data convergence hub is a

foundational component of the real-time health system, conducting digital healthcare, and the ability to execute population health and community care management. There is no question that how successful an HDO is in optimizing the use of its data has a strong correlation to achieving successful outcomes. The health data convergence hub facilitates the exchange of information and insight from origin to destination at the right time. In the U.S., continuing healthcare reform, including MACRA and MIPS, raises the stakes; globally, healthcare reform and e-health initiatives will also rely on more industrial-strength capabilities to share and exchange data. Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: DataMotion Health; Halfpenny Technologies; Health Catalyst; Imat Solutions;

Phemi

AI-Enabled Diagnostic Imaging Interpretation Analysis By: Thomas J. Handler, M.D. Definition: AI-enabled

diagnostic imaging interpretation uses deep learning techniques and categorization technology on very large sets of medical images in order to create algorithms that allow for faster and more accurate reading of X-ray studies, MRI exams and CT scans.

Position and Adoption Speed Justification: The number of diagnostic imaging studies that are

being performed has increased substantially over the last decade. With the aging of the population, the expectation is that the number of studies will continue this growth. In many countries, there are too few imaging specialist to handle current demands. One reasonable solution is to leverage technology to improve imaging accuracy and productivity and in some cases even replace the need for a radiologist of cardiologist interpretation of images. Several companies are now leveraging arti�cial intelligence and machine learning to provide diagnostic support for imaging specialists. In some cases, these solutions propose diagnoses and in others identify areas of concern to allow clinicians to quickly focus their attention where it is most necessary. The end result is faster and

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more accurate diagnoses. The technology is relatively immature but very promising. What is needed is more evaluation and demonstrable proof of accuracy. There is also a need for careful determination of possible legal rami�cations. At some point, it might be considered a malpractice to not use these systems, but perfection is not possible leaving the question outstanding — who will take the responsibility when the systems inevitably make a mistake. Until some of the questions are answered, the adoption of and value provided will be limited. Other challenges that will need to be faced include regulatory hurdles and cultural acceptance by both patients and clinicians. User Advice: Healthcare provider CIOs should work with clinical leaders in the imaging specialties

as well as with risk management department to carefully evaluate and determine the best course of action in regard to the adoption of these solutions. Given the relative immaturity of these products, it would be wise to proceed cautiously. Regulatory clearance may be required in some countries. Once any regulatory issues are addressed, those HDOs that are already experiencing shortages of imaging specialists or long lag times between study completion and �nal interpretation should consider piloting some of these solutions sooner rather than later. Be prepared to address patient and clinician concerns about the ef�cacy of these solutions. Carefully draft the business case, taking into account possible payment issues (for example, will there be a reduction in payments for computer-aided diagnoses?). Business Impact: AI-enabled diagnostic imaging interpretation solutions can improve image

reading accuracy and improve clinician productivity. By aiding image diagnosis, the incidence of missed or inaccurate diagnoses should decrease. Furthermore, these solutions can help and alleviate the projected severe and growing shortage of imaging specialists and this in turn should lead to improved clinical outcomes, increased patient satisfaction and enhanced brand loyalty. While these solutions can increase imaging throughput, one issue still to be addressed is the �nancial impact. Still to be addressed is what will be the impact on payments if imaging specialists are aided or even replaced by computer systems. Benefit Rating: High

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: DiACardio; HealthMyne; MedyMatch; Qure.ai; Zebra Medical Vision Recommended Reading: "Business Drivers of Technology Decisions for Healthcare Providers,

2017" "Implementing and Managing EHRs, Telemedicine and Other Care Delivery IT Primer for 2017"

Precision Medicine Analysis By: Vi Shaffer; Mark E. Gilbert Definition: Precision

medicine is an emerging approach for disease diagnosis, treatment and prevention that takes into account individual variability in genes, physiology, environmental

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exposures and lifestyle. In the context of this Hype Cycle, it combines advances in genomics medicine, wearables and the Internet of Things with enhanced data collection in electronic health record (EHR) systems, environmental sensors, wearables and mobile device applications. Position and Adoption Speed Justification: Precision medicine, at its core, is linking patient

observations from many domains with the prevalence of health and with the risk, prevention, onset, diagnosis and treatment of disease. It starts with a complete collection of data surrounding patient history, physical exam and routine laboratory studies. To those routine data elements, precision medicine adds genomic sequencing and microbiomic (skin, respiratory tract and intestinal bacteria) sampling to perform diagnosis and treatment. Over the next few years, precision medicine initiatives will collect real-world data (RWD) on patients' preferences, behavior (habits and lifestyle) and environment (what exposures to environmental agents, such as chemicals and radiation, patients may be having). It will take years to capture these RWD elements, standardize their recording and analysis and develop real-world evidence (RWE), correlating RWD with outcomes. Mobile phone applications and attached devices, wearable sensors, sensors and devices in the home and workplace, and publicly available data on the patients' environment will likely all be involved. Many of these technologies are in use today, but it will be more than 10 years before these levels of RWD and RWE are part of routine medical practice. Healthcare industry use of the term "precision medicine" (PM) has been growing for some time. You can spot many vendors with "precision medicine" on their websites or in mission statements. PM is closely related to, but is a larger concept than, genomics medicine because it incorporates personalized knowledge about the patient beyond the molecular/clinical viewpoint. PM got a particular boost in the U.S. when former President Obama launched the Precision Medicine Initiative in 2015. In late 2016, Congress continued this direction with the 21st Century Cures Act. However, the amount of research funding focused on precision medicine in the 2018 budget is still an open question as signi�cant cuts are in President Trump's �rst proposed version. HCA's Genospace, and Canadian startup Sequence Bio partnered to build a broad-scale government precision medicine initiative for Newfoundland and Labrador. Genomics companies are partnering with EHR vendors and integrated delivery networks: ActX with Epic, Allscripts with its 2bPrecise service line and NantHealth, Claritas Genomics with Cerner, Syapse with Intermountain Healthcare, Philips' cloudbased genomics platform with Illumina sequencing technology and BaseSpace Sequence Hub. User Advice: Top executives, medical and service line leaders in life sciences, healthcare delivery

and health insurance must all stay engaged with the advances under the precision medicine umbrella. Central to the precision medicine initiative is gradually collecting more detailed data about patients. For CIOs, chief medical informatics of�cers (CMIOs) and other IT and clinical informatics leaders, precision medicine has a succession of IT needs for its support. Socialize and adopt a forwardlooking healthcare analytics architecture. CIOs must lead the move toward the enterprise architecture, work�ow/decision support design, and new partnerships that enable genomic analysis and recommendations for medication prescribing and cancer care. To advance your population health management efforts, CIOs may plan to engage a data broker or medical hub partner to collect and standardize clinical data from patient wearables and home devices. CIOs should include exploration of mobile applications for patients to collect chronic condition symptoms, medication effects and healthcare preferences. All of these platforms will ultimately be extended to capture the Page 14 of 88



data needed and transformed into real-time use for precision medicine. Early experiences in cancer and certain chronic conditions will build the medical collaboration and competence for future precision medicine. Business Impact: Between now and the decade-plus ful�llment of precision medicine's promise,

there are ample opportunities for entrepreneurs; life science companies; academic medical centers; healthcare, health knowledge, cloud and decision support companies; EHRs and the like, to capitalize on its many emerging commercial tentacles. Precision medicine, based on RWE, re�ective of the unique RWD of each patient, will replace evidence-based medicine based on a cohort of patients similar to the patient at hand. Arti�cial intelligence in healthcare will use precision medicine datasets across millions of patients to suggest diagnoses a nd treatments. This transformation should yield signi�cantly reduced incidences of medical diagnosis error, reduction in treatment variability and ultimately reduced total cost of care. It should improve population health and further emphasize predictive and preventive actions. Precision medicine is needed to transform central medical decision making to personalized consumer engagement, which will drive the entire engine of healthcare delivery by 2030. Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience Maturity: Embryonic Sample Vendors: ActX; Allscripts; Claritas Genomics; HCA-Sarah Cannon (Genospace); Illumina;

NantHealth; PathGroup; Philips; Quest Diagnostics; Syapse Recommended Reading: "Prepare Your Healthcare Delivery Organization for the First Wave of

Genomics" "Business Drivers of Technology Decisions for Healthcare Providers, 2017" "Cool Vendors in AI for Healthcare, 2017" "Arti�cial Intelligence Primer for 2017"

Healthcare Algorithmic Marketplace Analysis By: Laura Craft Definition: An

algorithmic marketplace is a technical infrastructure that facilitates the publication and consumption of reusable algorithms. Some algorithmic marketplaces are used within an organization to support internal sharing of prebuilt a lgorithms among data scientists, while most are deployed externally to promote free or paid algorithms, sometimes including the sharing and monetization of datasets.

Position and Adoption Speed Justification: We introduced this pro�le in 2016 to recognize the emergence of algorithmic marketplaces speci�cally for healthcare and to track adoption. In the �rst

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year, we pro�led only one vendor, Apervita, which was a pioneer in promoting the democratization of healthcare analytics by offering an extensive library of measures, pathways and algorithms that have already been tested and validated. This year, Foxtrot Code entered the market. Similar to Apervita, they have a development environment as well as a marketplace. Foxtrot Code also offers public and private group workspaces for collaborative development and sharing, but does not yet have the extensive catalog of healthcare-speci�c industry algorithms that Apervita offers (which has the potential to be a huge accelerator for bringing advanced analytics into the healthcare delivery organization [HDO] without having the headache of a development process). There is some movement that other vendors, such as Epic and Health Catalyst, are moving toward a marketplace approach to algorithmic development and sharing, but whether these will be simply just catalogs or libraries of analytics, as opposed to a true marketplace, is still unknown. While the concept should be revolutionary, the use is still largely seen only in the academic medical centers by researchers. There has been less adoption by HDOs that could truly bene�t from the use of a marketplace as a component of their analytics strategy. HDOs should welcome the ability to easily procure predictive algorithms for CMS-driven quality indicators lik e readmissions, length of stay and early detection of sepsis. We nudge the positioning forward from 25% to 30% post-trigger, which is slower than anticipated and re�ects the stalled adoption, but are giving it some traction due to the entry of Foxtrot Code into the market. Healthcare typically lags other industries in adoption of analytics, and this is no different. Other industries are also seeing slower adoption than would have been expected, but we are still optimistic and expect marketplaces to gain traction and move through the Hype Cycle within �ve to 10 years. User Advice: Advice from last year has changed little since the market is still young. CIOs, CDOs

and CMIOs should: ■

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Lead enterprise awareness of the marketplace potential and investigation of the advantages that the marketplace offers. Develop a strategy for how the HDO can leverage and bene�t from the analytics open market, and understand how this �ts into/alters relationships with current analytic vendors. Be aggressive about this — marketplaces offer huge opportunity not only to advance deployment of necessary algorithms (such as readmission), but also to accelerate innovation in the HDO. Assess your information governance and analytics infrastructure to ensure that data can be easily extracted and exchanged to support the "plug and play" agile intent of the algorithms. Understand your monetization strategy. Establish, with your CFO, a standard methodology for measuring key information assets' actual and potential economic value to your organization that can be used when assessing market value and participation in the marketplace.

Business Impact: There is potential for the analytics marketplace to be disruptive and radically

change the nature of the current market for healthcare analytic vendors and how HDOs invest in analytic capabilities. The availability of advanced algorithmic models via an open marketplace facilitates access to algorithms that are beyond the development reach of many HDOs (because skills and large training datasets are not readily available to all HDOs), and provides options for how CIOs and CDOs can evolve their analytic portfolio — altering the way an HDO can think about delivering analytics to the organization by introducing a new choice into the equation. With the marketplace, HDOs will have the ability to select the algorithms best suited to their needs without Page 16 of 88



being tethered to a single vendors' library, multiple vendors or the internal developme nt costs. This can also signi�cantly accelerate the HDO analytic agenda with much faster time to deployment and value. The vast majority of HDOs are not prepared to create and deliver a sophisticated portfolio of the predictive and prescriptive models that will enable population health management, success under value-based care delivery model, and the delivery of precision medicine. Most organizations do not have the skills required from data scientists and clinical informaticists; nor would the healthcare analytics vendors be able to match the diversity the open marketplace promises. Benefit Rating: High

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: Apervita; Foxtrot Code Recommended Reading: "The Emergence of Precision Algorithms in Healthcare"

"Cool Vendors in Healthcare Providers, 2016" "Predictive Algorithms That Healthcare Delivery Organizations Are Using to Improve Outcomes"

Blockchain in Healthcare Analysis By: Gregg Pessin Definition: Blockchain

is a type of distributed ledger in which transactions are sequentially grouped into blocks. Each block is chained to the previous block and immutably recorded across a peer-topeer network, using cryptographic trust and assurance mechanisms. Depending on the implementation, transactions can include programmable behavior. Healthcare blockchain encompasses all practical applications of this technology within the industry.

Position and Adoption Speed Justification: Healthcare CIOs are abuzz about the potential

business processes that could be streamlined or replaced by blockchain-enabled alternatives. Early blockchain implementations in �nancial services have spurred the healthcare hype. Top of the list are processes that are subject to fraud, or that depend on interoperability and high degrees of security. However, essential underpinnings like data standards and structures are major impediments. Issues of scalability, health-data-speci�c security and adherence to existing regulations further cloud the picture. Nonetheless, because most blockchain initiatives are based on open-source technology, innovation and evolution will happen quickly. In 2017, it's clear the healthcare blockchain is still in its infancy. Although blockchain is in the top 10 search list by clients at Gartner, fewer than 5% of healthcare provider CIOs and only 12% in the payer industry have blockchain in their roadmaps, and even fewer are actually working on funded projects, today.

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Healthcare clients are unsure of how to best pursue a blockchain strategy, considering there are so few viable commercial models. Clients express uncertainty about blockchain's �t into their mature exchange, maturing supply chain, evolving data sharing and customer engagement strategies, and are unsure when blockchain will be ready. There are several governance styles for blockchain that are still unfolding within healthcare: (1) a public-permissionless model (such as bitcoin technology stack); (2) a private-permissioned environment that leverages a consensus model; (3) a hybridpermissioned environment, where the consensus model is provided by a third party; and (4) a private permissioned environment where the consensus model is operated by an institution for it and its partners. In the payer and provider worlds, the speculation is focused on streamlining transactions and data sharing among all the major players in the healthcare value chain for everything from contracting, credentialing and claims payment, to health data aggregation and analysis, and population health management. Also, longitudinal medical records could be the source for precision medicine and population health studies. Today, there are very few vendors, IT consultant �rms, and sponsor organizations that understand block chain and have healthcare industry experience and deep business domain expertise (e.g., supply chain, R&D, claims adjudication or EHR/EMR). User Advice: Distributed ledger concepts are complex and are not well-understood by the

healthcare CIO community. Existing production examples of blockchain such as bitcoin are useful to understand and explain the concepts and underlying technologies. Progress is being made by several vendors to develop distributed ledgers for medical information storage. At least eight startups are underway for this purpose. Tracking those vendors is a smart step. To fully keep abreast of this emerging technology: ■

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Assign personnel to research and identify applicable use cases as potential candidates for pilots or proofs of concept. Recognize that the terminology surrounding blockchain is in �ux. This uncertainty masks the potential suitability of technology solutions to meet business use cases. Use caution when interacting with vendors that have ill-de�ned/nonexistent blockchain offerings. As you plan, ensure you are clearly identifying how the term "blockchain" is being used and applied, both internally and by providers, in order to better understand the costs and returns compared to existing "proven" technologies. Proactively explore the four styles of governance as part of your organization's strategic planning efforts, especially as they relate to speci�c business use cases and operational risk assessments. Monitor the evolution of blockchain, including related initiatives, such as consensus mechanism development, sidechains and distributed ledger.

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Monitor the vendors that are marketing their solutions. Vendors are currently peddling concepts with few active POCs.

Business Impact: Blockchain and distributed-ledger concepts are gaining traction with healthcare

businesses because they hold the promise of transforming both architectures and operating models. However, the business case for block chain in healthcare is still an open book. Now is the time for industry stakeholders to learn from and then build upon existing models as they evolve. The potential of this technology to radically transform economic interactions should also raise critical questions for health value chain, regulators, suppliers, patients and consumers, for which there are no clear answers today. As healthcare companies get more serious about blockchain, it will become critical to ensure that the right type of governance is applied in order to drive innovation, collaboration and more ef�cient supply chains. The bene�ts, if the technology can be applied correctly, are very clear. Block chain will enable ef�ciencies for reaching new customers, extending relationships with supply chain partners, and offering better quality and more complete links between events. It should expand the boundaries of healthcare businesses. Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience Maturity: Embryonic Sample Vendors: Blockchain Health; Brontech; Gem; Guardtime; Hashed Health; HealthCombix;

MedRec; PointNurse; PokitDok Recommended Reading: "Top 10 Mistakes in Enterprise Blockchain Projects"

"Practical Blockchain: A Gartner Trend Insight Report" "The Bitcoin Blockchain: The Magic and the Myths"

Patient Decision Aids Analysis By: Mark E. Gilbert; Thomas J. Handler, M.D. Definition: Patient

decision aids are complex interactive systems that use computational logic to help individuals/patients make better-informed health decisions. These aids may include triage, diagnostic and treatment options, end-of-life choices, and eventually �rst-pass genetic counseling. Disease management applications or personal health management tools do not �t this de�nition, nor do sites providing patients with access to educational content without decision rules.

Position and Adoption Speed Justification: Getting individuals more engaged in their own care by

providing information, tools and empowerment is more important than ever. Patient decision aids contribute to the shared decision-making process and engagement. The aids improve patient's knowledge and accuracy of risk perceptions, and they increase the congruency between informed

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evidence and care choices. Positioning on this Hype Cycle is based on averaging the �ve functions described in the de�nition. Triage and therapeutic assistance are much farther along than the other functions. Genetic counseling is barely even at the Technology Trigger point. Although patient decision aids have broad bene�t for engaging and empowering patients, there has been minimal progress along the Hype Cycle. Based on inquiry calls, there has been increased interest in the concept, especially as it becomes clear how ill-equipped people are to make wise healthcare decisions. Nevertheless, barriers to adoption include relatively immature products, healthcare providers' competing priorities, payment issues and legal concerns. The systems remain more of a research interest, rather than a commercial interest. It is still the case that only very progressive organizations are taking steps to leverage technology to improve patient decisions. Most are doing so either as part of academic research or in trials within at-risk providers. Factors that are inhibiting this market include questions regarding the content and its delivery. Is there enough evidence to help patients make these complex decisions? Will clinicians accept content from other sources, demand the ability to vet that content, or reject any content they didn't author? The tools also require complex integrations. They will need ready access to clinical data, which means that the current technical limitation of interoperability is hindering advancement of these tools. There is also uncertainty regarding whether patients will accept and use these systems and discussions about which healthcare sector is best-positioned to provide this support (payers, providers, retail pharmacies or consumer services). Providers will also need to embrace interactions with empowered patients and their informed role in decision making. Although this technology has potential, and there is more talk about its importance, essentially, minimal movement has occurred during the past 12 months. User Advice: Healthcare CIOs need to help business leaders determine when the maturity of these

products has passed a minimum threshold. Until then, early adopters might consider small pilots of this technology focusing on patient engagement objectives and speci�c clinical services. Initial technologies could include the creation of clinical procedure videos and a policy of mandatory distribution to patients. Clinicians should be coached on how to communicate with patients to understand their informed preferences. However, CIOs must recognize and mitigate the risks involved (including the possibility of medical or legal rami�cations) because these tools remain unproven. We advise mainstream and late adopters to wait for these products to mature. Although patient decision aids may start as stand-alone systems, we believe that physicians ultimately are unlikely to support these systems until they are tightly linked with an enterprise electronic health record system — in part because they will not want to interact with multiple systems, but more importantly because they seek access to information within the context of a speci�c patient. Business Impact: Successful roll out of patient decision aids is likely to help with patient

engagement, loyalty and empowerment. Use of the aids should reduce variability in care, improve outcomes, and increase the satisfaction of patients. For these reasons, we have increased the bene�t rating from low to moderate. Some studies have shown that the use of patient decision aids results in a decreased number of surgeries and lower costs for treating patients. Other studies have shown patient decision aids help patients reach a decision faster, resulting in quicker treatment. However, note that these trials may not re�ect the potential impact within each provider's environment. We believe each provider should be conducting tests to determine potential business effects.

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Benefit Rating: Moderate

Market Penetration: Less than 1% of target audience Maturity: Embryonic Sample Vendors: Emmi; Health Dialog; Healthwise; Maldaba; Optum Recommended Reading: "Leading HDOs Focus on 12 Categories of Patient Engagement

Technology" "Healthcare Payer CIOs Should Make a Safe Bet in Uncertain Times by Driving Analytics Innovation" "Business Drivers of Technology Decisions for Healthcare Providers, 2017" "Healthcare Provider CIOs Must Begin the Pivot to Putting the Patient at the Center of IT Strategy"

AI for Healthcare Providers Analysis By: Laura Craft Definition: AI

is not de�ned by a single technology. Rather, it includes many areas of study and technologies behind capabilities like voice recognition, natural-language processing, image processing and others that bene�t from advances in algorithms, abundant computation power, and advanced analytical methods like machine learning and deep learning. These rapidly advancing capabilities are behind new business models based on data.

Position and Adoption Speed Justification: Arti�cial intelligence for healthcare providers is being

introduced to track the adoption of AI methods and capabilities for healthcare delivery and care. Gartner predicts a very active and somewhat confusing healthcare AI market over the next several years. Part of the confusion stems from the lack of clarity in terminology (see "Arti�cial Intelligence Primer for 2017" to understand the breadth of AI capabilities) and the fact that almost every healthcare analytic vendor is exploiting the hype of AI's status as the new bright shiny object. This hype was evident at the 2017 Healthcare Information and Management Systems Society (HIMSS). The most publicized early AI effort in healthcare has been IBM's Watson for Oncology advisor, which uses a combination of AI capabilities to help optimize cancer diagnosis and treatment. Other early efforts have also gained traction. Revenue cycle management is using machine learning and NLP management to help with coding accuracy and clinical documentation improvement and optimization. Machine learning and cognitive computing are being applied to radiology to increase productivity and ultimately aid in diagnosis. There is a breadth of opportunity across many healthcare processes — both clinical and administrative. AI for healthcare providers is positioned right at the midway point between the trigger and peak and in the middle of AI-related pro�les. This positioning re�ects the rapidly growing interest and what we predict to be a fairly rapid ascent to Peak of In�ated Expectations before reality gets tested and

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there is a rough slide into the Trough of Disillusionment before AI's true value in healthcare matures. We don't expect AI to reach full productivity and stability for at least another 10 years. User Advice: AI will be transformational for healthcare delivery, but capabilities need to be

introduced carefully, as trust is established within users. CIOs should begin to evolve an institutional AI strategy by doing the following: ■

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Lead an AI educational process with senior leadership. Build institutional knowledge and competency. Cut through vendor hype and identify realistic use cases. Fortify analytic capabilities. Conduct an assessment of platforms, skills and processes to make sure AI endeavors can be successful. Craft an analytics reference architecture that has an analytics workbench and data science laboratory so that advanced AI methods can be introduced and tested. Introduce bimodal. Encourage your imaging department to consider an AI pilot as a starting point. Bring forward market options and actively engage with the radiologists to create evaluation criteria. Make sure to understand how you will measure and demonstrate value. Break down any organizational or cultural barriers between research (e.g., labs — including IT think tanks) and clinical care delivery and practice by cultivating a culture of trust and shared interest. Advances in genomics and precision medicine rely on strong collaboration between research, IT and clinical care.

Business Impact: There are many areas that will bene�t from one form or another of AI capabilities.

We will continue to track new uses cases, maturity and value-add. The current use cases we are tracking include: ■

AI-Enabled Diagnostic Imaging Interpretation: Companies like Zebra Medical Vision use

deep learning techniques and categorization technology on very large sets of medical images in order to create algorithms that allow for faster and more accurate reading of X-ray studies, MRI exams and CT scans. ■

Revenue Cycle: Companies, such as 3M Health Information Systems and its 3M 360

Encompass System, leverage NLP to support CAC, CACDI and RTPDI with proprietary rules and medical ontologies. This is combined with advanced data analytics and deep machine learning to deliver real-time queries, and can surpass typical revenue cycle functions such as reducing A/R days, increasing complex coding accuracy, creating revenue cycle ef�ciencies and reducing revenue leakage. ■

Virtual Personal Health Assistants: Virtual personal health assistants (VPHAs) are healthcarespeci�c virtual assistants and combine voice automation and arti�cial intelligence in order to

follow voice (or text) commands to provide information and assistance. ■

AI Health Advisors: In addition to IBM Watson Oncology, there are vendors like CloudMedx

and Deontics that are leveraging cognitive computing to create personalized care paths and to generate real-time clinical insight at the point of care. ■

Genomics and Medical Diagnostics: A unique array of vendors is using AI to help make

medical diagnostics more accurate and accessible. Companies include those such as Deep Page 22 of 88



Genomics, which uses deep learning to predict what will happen within a cell when DNA is altered by genetic variation, and Medial EarlySign, which uses basic medical data to predict months, even years, out an individual's predisposition to certain cancers and metabolic and chronic diseases based on simple medical data. Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: CloudMedx; Deep Genomics; Deontics; IBM Watson Health; Medial EarlySign;

Next IT; Zebra Medical Vision Recommended Reading: "Arti�cial Intelligence Primer for 2017"

"Hype Cycle for Smart Machines, 2016" "How to De�ne and Use Smart Machine Terms Effectively" "Cool Vendors in AI for Healthcare, 2017"

3D Bioprinted Organ Transplants Analysis By: Vi Shaffer Definition: 3D

bioprinted organ transplants produce living tissue products that function like human organs. Product components include imaging data, design software a nd 3D printing devices. The two main opportunities for these are in life science R&D and human transplants. The latter is the subject of this pro�le. Progress for life science use cases is a contributing factor. This is one of the ultimate transformative breakthroughs of healthcare's digital business e ra.

Position and Adoption Speed Justification: There is a broad array of current and potential 3D

bioprinting applications with very different challenges and many years of different time frames for their adoption. The creation of human-transplantable organs is certainly one of medical 3DP's biggest dreams, but it is still a dream to grasp. Creating transplantable organs has all the complexities of human tissue bioprinting and many others, such as functional vascular systems. Challenges include how to test how well the organs will integrate in the body (avoiding rejection issues) and how to test and prove the long-term viability and effect of the organs. Very complex scienti�c, technical, regulatory and adoption issues are also in play. Very profound potential business, funding, ethical and personal health impacts also arise when all of the other challenges are �nally conquered. This is likely to require more advanced/ different types of 3DP technology than we have today, designed speci�cally for printing of organs, not adapted from off-the-shelf commercial 3D printing technology. We evaluate this category based on progress in either transplantable kidneys or livers. Kidneys account for about 80% of transplant needs, with high fatality rates for waiting patients. According to

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experts, kidney transplants are on the "simpler" end of this complicated journey. Although we will see a complete organ viably grown and implanted within 10 years, early volumes will be low. One of the most dif�cult clinical issues is creating a sustained vascular system in the organ. The hepatocyte/livers in dishes are still not expressing all the right cytochrome P450s. We have moved this pro�le ahead only very slightly this year. The viability, real-world availability for use in humans and adoption rates of actual organ products are still highly speculative. The Harvard Wyss Institute announced a potential breakthrough in October 2016. "We are pushing the boundaries of three-dimensional printing by developing and integrating multiple functional materials within printed devices," said Wyss' Jennifer Lewis, Sc.D., who is core faculty at Wyss. The research team developed six different inks that integrated soft strain sensors within the microarchitecture of tissue. It then 3D printed those materials onto a cardiac microphysiological device — a heart on a chip. The Wyss team has also developed organ chips that mimic the microarchitecture and functions of muscle, tongue, lung, intestine, kidney and bone marrow (see "First Entirely 3D-Printed Organ-on-a-Chip With Integrated Sensors" ). User Advice: CIOs supporting life science companies and academic medical centers that lead in

these investigations must support R&D efforts to develop 3D bioprinting techniques. This area is more a major emerging life science technology than "classic" healthcare IT. However, as both testing R&D uses and ultimately organ manufacturing businesses emerge, CIOs should be on the lookout for new data exchanges, applications, and storage and device support requirements. Healthcare provider CIOs, CMIOs, and medical leaders should monitor 3D bioprinting and other technologies for precision medicine and "rebuilding the body" to model a new construct for the scope of IT services. Be prepared for expanded responsibilities over the next decade beyond supporting medical tasks, work�ows and clinical decision making. Early instantiations of 3D bioprinting organ transplants are likely to be delivered by remote manufacturing services. Even this scenario involves exchanges of design parameters (patient and imaging data transmitted in a different context), unique tracking in the supply chain and documenting transplanted custom organ data of a different nature in the EHR. Business Impact: Implants of inert polymers and metals are regularly performed today. We expect

that simple 3D bioprinted tissue grafts will be next, followed by complex tissue regeneration. The ability to build new and precisely personalized human tissues substantially expands the array of therapeutic options for a wide range of patient injuries and diseases. Bioprinted organs using the patient's own cells are hoped to have the bene�t of avoiding the rejection of implanted/transplanted tissue, and the costs of a lifetime of anti-rejection drugs. Many of the emerging u se cases are for limited or rare scenarios. However, in aggregate over time, the total imp act on medicine is transformational. There is an additional — indeed earlier — market for the production of tissues and organs for life science research and development. This business is providing some of the needed early revenue for startups targeting human tissue and organ 3D bioprinting. Benefit Rating: Transformational

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Market Penetration: Less than 1% of target audience Maturity: Embryonic Sample Vendors: 3D Bioprinting Solutions; Creative Machines Lab; EOS; Organovo; Regenovo

Biotechnology; TeVido BioDevices; The University of Iowa; Wake Forest Institute for Regenerative Medicine; Wyss Institute Recommended Reading: "3D Printing Opportunities and Uses Primer for 2017"

"Healthcare Provider CIOs Should Quickly Advance to a Bimodal IT Model" "Business Drivers of Technology Decisions for Healthcare Providers, 2017"

Critical Condition Surveillance Systems Analysis By: Mike Jones Definition: Critical

condition surveillance systems monitor the electronic health record (EHR) or clinical data repository (if more than one EHR) in real time for signs that a patient (usually an acute care patient) may be slipping toward a signi�cant clinical decompensation that could be lifethreatening or warrant urgent transfer to a higher care unit. Surveillance can also be applied to multiple patients at once, such as in the early detection of a Staphylococcus infection outbreak. Upon discovery, such systems then alert responsible providers.

Position and Adoption Speed Justification: Advanced healthcare delivery organizations (HDOs)

are already calculating early warning scores based on vital signs, clinician charting and laboratory results in their megasuite EHRs, setting this technology midway between trigger and peak. The megasuite EHRs cannot all handle complex algorithms and many implementations do not have access to previously processed non-EHR data used by dedicated surveillance platforms. Licensing is less expensive when building the scoring system in the EHR, but it requires testing and updating. Dedicated surveillance systems may achieve superior results by including non-EHR data, or by comparing current patient characteristics with a large store of previously analyzed patient data where the ultimate clinical outcomes are known. Work�ow may be more convenient in dedicated systems than in EHRs. Remote ICU systems have had embedded deterioration algorithms for years, but adoption of those systems has plateaued. Although EHR warning systems and stand-alone surveillance systems are currently in use, it is likely that over time, their algorithms will become better predictors of clinical deterioration. Having real-time feeds of clinical data to the algorithms is straightforward and not an inhibitor of adoption, nor does it confer an inherent advantage to scoring within the EHRs. Clinical surveillance systems are one type of analytic systems that enable the realtime healthcare system by increasing situational awareness and rapid response. Widespread acceptance of these systems will require a better signal-to-noise ratio so that there are fewer false positives. Cloud-based machine learning on large and complex datasets, with clinical and possibly nonclinical data, may result in superior predictive ability. If so, then mainstream use will be pushed to the far end of �ve to 10 years. User Advice: HDO CIOs and CMIOs should:

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Evaluate the capability of dedicated surveillance vendors versus what they can accomplish in their own megasuite EHRs. Render the clinical data repository capable of real-time data exporting to the surveillance platform. Inquire of other clients about false positive rates, which can be fatiguing for clinical staff. Track and reduce false positives in your own critical surveillance systems using clinical governance mechanisms already established for patient safety and clinical practice performance. Demand that surveillance system alerts be entered into the acute care EHR because clinicians will need to document that they were alerted by the surveillance system and how they evaluated the patient.

HDO chief medical of�cers should: ■

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Ensure that acute care facilities have high-functioning rapid response teams, nurse managers or assigned hospitalists to respond to the frequent alerts that these systems issue. Evaluate the bene�ts of surveillance systems by tracking unplanned transfers to the intensive care unit, mortality and hospital length of stay. Examine the system for the ability to adjust the weighting and scoring of the embedded algorithms. This is a two-edged sword as customized scoring may improve the signal-to-noise ratio, but may require time-consuming clinical testing.

Business Impact: Intervening early on deteriorating patients can dramatically improve their survival.

As more patient observations are captured in the clinical data repository, we can expect that algorithms will continuously increase reliable detection and will provide earlier alert of worsening patient status. Vendor products may have the advantage of previous clinical trials with peerreviewed �ndings. In the absence of direct comparison with stand-alone surveillance platforms and with the extra expense, some HDOs may try to get most of the bene�t by con�guring rules in their EHR to calculate early warning scores leading to clinician alerts. It remains to be seen whether EHR data, non-EHR data and analytics produce superior clinical outcomes to scoring based on only clinical data typically found in a megasuite EHR. This trade-off and uncertainty place the bene�t rating of this technology at moderate. Benefit Rating: Moderate

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: Bernoulli; PeraHealth; Royal Philips; VigiLanz Recommended Reading: "Predictive Algorithms That Healthcare Delivery Organizations Are Using

to Improve Outcomes" "Top 10 Strategic Technology Trends for 2017: Arti�cial Intelligence and Advanced Machine Learning"

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"Industry Vision: The Real-Time Health System" "Developing the IT Strategy for the Real-Time Health System Primer for 2017"

Healthcare Analytics Architecture for Providers Analysis By: Laura Craft Definition: The

healthcare analytics architecture for providers represents the next generation of enterprise analytics as providers seek to meet the goals of population health and value-based care, and deliver more value. The architecture supports the integration of the vast amounts and variety of health data; enables data science; and functions in real time deploying insights directly back into the operational and clinical work�ows.

Position and Adoption Speed Justification: We introduce this new technology pro�le in 2017 as a

replacement for both the Enterprise Clinical and Financial Data Warehouse (EDW), which had reached the Plateau of Productivity, and the Logical Data Warehouse (LDW). The healthcare industry has seen rapid digitization of data and with that an acceleration in analytic maturity. Gartner has been observing for several years the organic adoption by leading HDOs of the logical data warehouse approach to their analytics architecture (which expands the ability to work with new types and sources of data). In addition, Gartner has seen healthcare analytic vendors signi�cantly updating their architecture to support more complicated data needs and advanced analytic demands. The healthcare analytics architecture for providers is designed along the principles and best practices of the logical data warehouse, but it is a responsive real-time driven ecosystem that is optimized to ingest massive amounts of data across the clinically integrated network, rapidly process it, and leverage it for operational and clinical decision making. We introduce the pro�le at prepeak 40% to re�ect the emergent adoption. User Advice: CIOs, CDOs and CAOs, and others responsible for the enterprise analytics strategy

and roadmap must assess the fortitude of their analytics capabilities and architecture against the intuitional demand for data and information. Traditional legacy EDWs simply are not suited to manage the data or deliver on the diversity of healthcare analytics use cases we see today. As an industry, we are moving away from description analytics and structured retrospective data being the core to predictive and real time. Many healthcare delivery organizations (HDOs) are extending their EDWs and moving the LDW by default to include these capabilities. Gartner recommends that the architecture be thoughtfully and deliberately recon�gured to effectively prepare for the next generation of analytics — which will include any number of arti�cial intelligence capabilities (such as machine learning and cognitive computing). Many vendors are also upgrading their platforms so the exercise is not necessarily a rip and replace, but rather a redesign and re-engineering as necessary. It is really less about the vendor than the HDOs individual analytics architecture and blueprint. The assessment should include the following types of questions that focus on differentiating capabilities: ■

Data integration: Is the data integration layer robust enough to manage the integration of data across the clinically integrated network and inclusive of: claims, patient-reported, remote monitoring and wearables, genomics and other omics, behavioral, social determinants, community, other?

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Data management: Is my data management architecture able to support structured data? Unstructured data? Streaming data? Does it support an effective data lake strategy? Real time: Is the analytics environment optimized for delivering real-time analytics back into the clinical and operational work�ows? Advanced analytics: Is the analytics environment positioned to be able to support innovations with the use of capabilities such as machine learning, cognitive computing? Genomics: Is there a strategy to be able to bring genomics data into the analytics environment and clinical work�ows? Data governance: Do I have a data governance strategy that is strong enough to support the increased complexities and decisions that need to be made around data? Innovation: How does my analytics environment support innovation and Mode 2 delivery?

Business Impact: The maturity and effectiveness of the HDOs analytics strategy and architecture is

paramount to succeeding and doing well. As the use of analytics becomes more pervasive in in�uencing actions, often in real time, across operational, clinical and business processes, a modernized architecture to enable this becomes a necessity. The analytics architecture becomes the epicenter of the enterprise architecture and gets elevated to a Tier 1 capability for disaster recovery — no longer a nice-to-have but an essential component of delivering care. Gartner has put the bene�t rating at high because the technology alone does not create transformation, and healthcare providers have been woefully inadequate at realizing bene�ts from analytics. For the HDOs that truly leverage the architecture and the analytic capabilities it will have a transformational impact. Benefit Rating: High

Market Penetration: 5% to 20% of target audience Maturity: Emerging Sample Vendors: Arcadia; Cerner; Epic; Health Catalyst; IBM Watson Health; Oracle Recommended Reading: "Healthcare Provider Analytics Needs a Bimodal Delivery Mindset"

"How to Implement a Modern Business Intelligence and Analytics Platform" "2017 Planning Guide for Data and Analytics"

Real-Time Healthcare Costing Analysis By: Vi Shaffer Definition: Real-time

healthcare costing provides concurrent and predictive visibility into patientlevel direct variable costs for clinical or operations decisions. It collects administrative and clinical data, develops algorithms, performs calculations, and provides alerts or reports for real-time cost

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visibility and adaptive decision making around patient care and resources like labor and supplies. This is different from a cost accounting system. Position and Adoption Speed Justification: Real-time direct costing is not a new concept in

manufacturing, although it is not widely adopted and is still rare. Value-based care, cost control and waste elimination efforts (like lean programs) can be aided by real-time costing information. We project this will eventually become an important tool of real-time health system (RTHS) management paradigm, but it is currently in its infancy. Early development efforts are underway, and we advance this pro�le just slightly more than last year based on speci�c vendor activities, still assessing that it is in an embryonic state. IllumiCare has emerged as a specialized competitor in this area, rolling out an offering to its �rst customer in 2015. In 2016, the company teamed with the Texas Hospital Association (THA) to build out a THA Smart Ribbon version of IllumiCare's offerings. For this effort, the partners received the 2016 Innovator Award from Hospitals & Health Networks, the journal of the American Hospital Association. This shows increasing interest. Infor's TrueCost component of its performance management suite is also under development. More live sites and more bene�ts documentation will be needed for vendors to ignite this market. A notable new vendor move is the entry of Allscripts, which has become a player with its acquisition of startup company RealCost, characterized by Allscripts as "a next-generation decision support and analytics �rm." The company was bundled into a 2016 deal to reinvigorate its EPSi cost accounting/budgeting solution by bringing back EPSi founder Tim Rutledge and his RealCost cofounders Ralph Keiser (also formerly EPSi) John Gragg to serve as EPSi CEO and COO, respectively. They're focused �rst on shoring up some EPSi problem areas, but then look for them to aggressively position innovations in real-time costing. Comprehensive enterprise activity-based costing is an important management tool for healthcare and a need that is reheating under valuebased care accountability. Allscripts could leverage its EPSi and EHR client bases. Either Infor or Allscripts EPSi, with their large customer bases, could turn the combination of ERP/ TrueCost or EPSi and RealCost into a new market differentiation in the longer term, but it's too early to project winners. User Advice: CIOs should expect their CFO to express interest in exploring and possibly implanting

real-time costing. We recommend CIOs become proactive in assessing the progress of these tools. It's a good opportunity to engage the CFO with population health management effort. It also helps illuminate just how important the "real-time health system" paradigm of visualization, situational awareness and contextualized decision making can be. Direct-costing tools are only valuable to the degree that business and clinical personnel use them to improve real-time decision making: ■

Healthcare CIOs, running IT more as a business focused on enterprise value, must become savvy in all aspects of costing. They must also model the goals and requirements of real-time cost information into their enterprise architectures.

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CMIOs and CNIOs should team with the business side in championing real-time cost (and quality) information as such solutions become available. Most need further education on �nancial, budget and total cost-of-care systems, and how to link them with quality improvement in a real-time management context.

Two education and development programs we recommend are: ■

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The Intermountain Healthcare Institute for Healthcare Delivery Research courseware, which includes a 4.5-hour session on "Quality Controls, Cost & Tracking Healthcare Costs" The Denver Health Lean Academy, which teaches lean methodology and practices as they can be applied speci�cally in healthcare

Business Impact: The purpose of real-time costing is to supplement experience and instinct with

cost facts to enable adaptive decisions about patient care and resources like labor and supplies. The actual value of direct costing on healthcare processes and variance has yet to be proven, but conceptually should enable moderately improved conformance with budgets and a moderate improvement in total operating cost for complex hospital environments. It has great potential impact, along with the savings from other visibility and decision management systems such as workforce scheduling/management, patient throughput and capacity management, and location and condition-sensing technology platforms. This capability provides content for the "business pathway" complement to clinical pathways, using algorithms to prompt a clinician to make the right decisions with what would best optimize the overall performance. Through 2020, bene�ts will most likely be realized by high-performance healthcare delivery organizations (HDOs) that have already gained the �rst levels of accountable care model maturity and are embracing real-time health system management for hospital operations. Benefit Rating: Moderate

Market Penetration: Less than 1% of target audience Maturity: Embryonic Sample Vendors: Allscripts (EPSi); IllumiCare; Infor Recommended Reading: "Developing the IT Strategy for the Real-Time Health System Primer for

2017" "Healthcare Provider CIOs Must Drive Value-Based Performance Mana gement Into the HDO Analytics Portfolio" "The Emergence of Precision Algorithms in Healthca re"

Customer Engagement Hub Analysis By: Michael Maoz; Olive Huang

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Definition: The

customer engagement hub (CEH) is a technology and process concept that uses integration and event monitoring of multiple systems to optimally engage the customer. A CEH includes proactive and reactive communication, and allows personalized, contextual engagement with customers across all interaction channels, and reaches/connects all departments. This allows for the synchronization of marketing, sales and customer service processes.

Position and Adoption Speed Justification: During the next three years, 60% of large

organizations will extend their CRM technology goals by tying together disparate systems in a more holistic approach that pivots around the needs of the customer. For example, the customer service contact center has evolved into a customer engagement center with the goal of supporting social media and consumer messaging applications, as well as online community activities. Other departments (such as marketing, digital commerce and sales) will join with IT leaders to develop plans for the CEH — and yet, by 2019, only 30% of organizations will select the correct technologies to make it work. The need to support the anytime-anywhere customer (including on mobile devices, smart devices and social networks), alongside the need for heightened business awareness, makes this a top issue among customer service managers in both the private sector and public sectors (for example, healthcare, government, communication and media). User Advice: IT application leaders responsible for customer experience and/or integration who are assisting customer care directors, chief marketing of�cers, digital commerce leaders and heads of

telesales (since the CEH can be used by all departments, for all inbound and outbound customer interactions) should conduct an inventory of the key processes that not only drive customer satisfaction, but that foster customer engagement. Test the �tness of CRM-oriented applications to ful�ll the needs of the engaged customer. Look at vendor roadmaps and assess the vendor's and organization's readiness to evolve customer engagement processes and technologies. Identify areas for improvement before approaching IT for support or funding. Business Impact: An emerging CEH will foster personalized engagement with customers across all

interaction channels (including social) and will reach across all departments in the enterprise. The CEH will support the transition from transactional economics to a more comprehensive view of customer relationship economics. The issue of customer engagement and care will become a more systemic theme across enterprises of many types — from retail to healthcare, for example — as CIOs strive to hit their goal of retaining customers as a way to grow revenue and in�uence consumer buying and behavior. Thus, a CEH will be required to support these initiatives. It is not yet clear how this will impact the software market, but software vendors that neglect the shift will lose market share. At this stage, most of the components of a CEH are not bundled as a suite. There is a 40% likelihood that the CEH will remain a system of systems and never evolve into a product. Vendors tend to focus on what can be mass-produced and easily sold, rather than on products that can transform a business but require complex buying centers and change management. This limits the feasibility of a true CEH. However, the issue of siloed customer engagement is gaining the attention of business and IT leaders, as well as software vendors. We anticipate that large CRM software vendors will increase new capabilities to bridge this gap through acquisitions, partnerships with system integrators, and their own R&D efforts. There is a chance that, in the next three years, one large CRM software vendor will offer a complete CEH solution to its clients. Such a solution could emerge to target the large enterprise space with a strong focus on hybrid architecture. It will

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be the result of advances in service architectures, tying together smaller suites. It could also emerge to target the midsize enterprise market with a cloud-only approach. Benefit Rating: High

Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: Microsoft; Oracle; Salesforce; SAP Recommended Reading: "The Elusive CRM Magic Quadrant"

"CRM Technologies for the Emerging Customer Engagement Hub" "Ten Steps for Planning Your Customer Engagement Hub"

Healthcare Consumer Persuasion Analytics Analysis By: Mark E. Gilbert; Vi Shaffer Definition: Healthcare

consumer persuasion analytics utilizes patient and consumer clinical, experiential, engagement, social/environmental and behavioral data to derive and understand key motivators and in�uencers of individual health behaviors and outcomes. This also includes continuing analysis of what engagement and behavior modi�cation strategies are effective for speci�c nuanced subsegments of consumers. Uses include population health management, personalized health promotion/decision aids, behavioral change and precision marketing.

Position and Adoption Speed Justification: Healthcare consumer persuasion analytics uses data

from sources such as social networking, mobile devices, EHRs, and insight into the needs and preferences of consumers. It combines this data with advanced analytics technologies and data sciences to identify techniques and tactics to persuade consumers to undertake actions that bene�t their individual health. This is a speci�c advanced area of analytics in healthcare that is still in its very early stages, especially compared to other consumer-oriented industries such as retail. Learning how to motivate uninformed, unmotivated or biased individuals to change the behaviors that in�uence their healthcare outcomes remains one of the biggest 21st century hurdles in building healthier communities and public/private insurance bene�ciaries. We have nudged the positioning ahead again this year to just past the trigger-peak midpoint, to recognize that this arena is getting more attention. Interest continues to follow an uptick in executive sponsorship of initiatives within patient experience and around "patient engagement" tools such as portals. We have seen some progressive HDOs hire leaders to direct be havioral change initiatives. We're also hearing considerable noise among vendors that want to capitalize on the expanded interest in the science and art of behavior modi�cation. Sanctioned, funded patient engagement innovation centers are surfacing among HDOs, but currently these tend to be more focused on consumer engagement tools (e.g., portals and wearables) or on patient experience feedback than on persuasion analytics. The HDOs that are

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already the most successful with patient portals and/or patient-centered medical home and valuebased accountable care-type models, plus those regional HDOs and ministries of health with a progressive vision for consumer engagement, will be the earliest adopters of healthcare consumer persuasion analytics. They are better prepared with the right leadership, the right questions and the ability to act on the information. They will demonstrate the value of this approach within �ve years, but a plateau will be achieved — even considering just the HDOs with EHRs already in place — at the far end of our �ve- to 10-year projection. User Advice: HDO population health management leaders, including their CIOs, CMIOs and chief data of�cers, should: ■

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Begin collaboration by engaging with of�cials in payer and emerging public health medical informatics of�cer (PHMIO) roles around best targets for persuasion analytics investigation. Encourage CIOs and CMIOs to continue prodding senior leadership to develop trials focused on exploiting short-term opportunities while establishing the longer-term potential for personalized engagement and persuasion techniques. Begin engaging vendors with both the data and the data scientists who can bring epidemiology and patient behavior insights. Team with marketing staff around education and in�uence campaigns, establishing closer relationships and conducting joint planning efforts.

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Consider new investments in this arena within a three- to �ve-year strategic plan window.

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Keep their radars tuned on academic studies for early clues to effective targeted actions.

Business Impact: Estimates vary, but approximately 40% of the determinants of health are

associated with behavior, with as little as 10% associated with medical care. Being responsible for population health and risk management and tackling chronic problems such as obesity and diabetes require effective consumer behavior interventions and modi�cations — a so-far elusive realm. HDOs and public health agencies will have to become at least as sophisticated as other consumer/retail industries in analyzing a variety of data that helps uncover root causes of human behavior. The ability to in�uence behavior will be the key to transformative long-term management of cost and quality outcomes. Benefit Rating: High

Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: Emmi; Fitbit; Google; IBM Watson Health; Indegene; In�uence Health;

NextHealth; Pro-Change Behavior Systems; Saama; Welltok Recommended Reading: "Industry Vision: Mass Personalization of Consumer Hea lthcare

Engagement"

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"Business Drivers of Technology Decisions for Healthcare Payers, 2016" "Leveraging IT for Effective Healthcare Consumer, Member and Patient Engagement Primer for 2016" "Transitioning to Value-Based Healthcare: Building Blocks for Effective Analytics" "Healthcare Payer CIOs Should Make a Safe Bet in Uncertain Times by Driving Analytics Innovation"

Nanomedicine Analysis By: Vi Shaffer Definition: Nanomedicine

is the application of nanotechnology/nanoscale materials to medical diagnosis or therapy. Nanotechnology is the manipulation of matter on a n atomic, molecular or supramolecular scale.

Position and Adoption Speed Justification: Medical technology advances are rarely appear on Gartner's IT-focused Hype Cycles. When the category is very signi�cant, the connection to IT advances is tight and speci�c signi�cant issues for medical informatics professionals arise, they are

included. Nanomedicine is one of those categories, as is the medical use of 3D printing technologies. Once again, this pro�le is advanced slightly due to: (1) continued substantial nanomedicine research funding (e.g., through groups such as the European Commission's Horizon 2020/EU Framework Programme for Research and Innovation and the U.S. National Institutes of Health [NIH]); and (2) the advancement of R&D in �ne-tuning and pursuing speci�c viable theranostics use cases. Also in 2016, AstraZeneca, whose continued efforts are signi�cant, published what it calls a "thought leading-strategy for development of effective nanomedicines ... [focused on] building informative data packages using focused design and decision-making strategies like the AstraZeneca 5R framework" (see "Lessons Learned From the Fate of AstraZeneca's Drug Pipeline: A FiveDimensional Framework" ). Potentially dramatic medical advances such as nanomedicine traverse a path "from bench to bedside." This path involves initially framing potential use cases, experimentation by highly trained researchers that yields many failures, and eventually, practical uses �nally leading to mainstream adoption, often occurring over many years. Meticulous clinical trials, regulatory approach and approval, and payer acceptance can be huge hurdles to leap. Nanomedicine has all these challenges as well as its own unique issues related to manageability, toxicity and the environmental impact of nanoscale materials. (A nanometer is one-billionth of a meter, and a nanoparticle is 10 to 100 times smaller than a body's cells and a little larger than a molecule.) NIH calls it a "monumental challenge" to characterize the molecular components inside cells at a level of precision that leads to re-engineering intracellular complexities. Examples of �ne-tuned nanomedicine use cases/R&D include: ■

Nanoparticular use to add precision in the detection of cancer cells

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Nanomaterials engineered with a glucose-responsive coating to act as an insulin depot Nanocoating to prolong the survival of transplanted pancreatic islets and protect them from antibodies Cardiovascular applications to help create imaging modalities for currently unpredictable plaque eruptions, navigating through blood vessels to deliver payload Clinically applicable gene correction technology to treat single-gene disorders (e.g., sickle cell anemia) Restoration of vision (retinitis pigmentosa)

User Advice: An important component of the transformation of nanomedicine to the real-world

evidence base will be the ability of healthcare IT and clinical professionals to handle nanomedicine in EHR-based orders. Such orders include clinical decision support and documentation, and ultimately representing, reacting to and analyzing nanomedicine-related data. CIOs, chief medical informatics of�cers (CMIOs) and clinical system vendors should keep an eye on nanomedicine for its potential explosion of new clinical data management challenges, IT system performance and storage issues, and new "health knowledge cloud" support of real-time precision medicine. When "knowledge interoperability," decision support and storage become requirements, they will appear �rst in the EHRs of leading-edge vendors with research/academic medical center clients. Nanomedicine tracking also has to be addressed in the supply chain, along with the question of whether it is part of unique device identi�cation (UDI) and tracking mandates. CIOs will also need to consider how to incorporate the capture of potential, currently unknown "safety events" from particles in the environment or other scenarios. The issue of safety is a global concern that will require regulatory review and new policies. In Europe, for example, the Scienti�c Committee on Emerging and Newly Identi�ed Health Risks has published reports on this topic. Informed consent, risk assessment, toxicity and human enhancement are just a few of the ethical concerns. Business Impact: Nanomedicine will be transformational across a wide range of theranostics. It will be yet another boost to healthcare's scienti�c quest for ever-more-precise and highly personalized

medicine. Nanotechnology promises sensitive and e xtremely accurate tools for in-vitro and in-vivo diagnostics. In terms of therapy, the most signi�cant impact of nanomedicine is expected to be realized in drug delivery and in regenerative medicine. With ful�llment of its broad potential over many years, Nanomedicine will reinvent a number of the diagnosis and treatment approaches used in medicine and healthcare today, and will potentially require substantial remodeling of the healthcare industry. The likely impacts will occur use case by use case over a very long period of time, with providers and payment models adjusting to these various clusters of innovation. Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience

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Maturity: Embryonic Sample Vendors: AstraZeneca; CytImmune; Liquidia Technologies; NanoCarrier; NanoMedTech; Nanoscribe; P�zer; PFSweb; Selecta Biosciences; Vivonetics Recommended Reading: "The Six Forces That Will Shape Business and Technology in 2030"

Value-Based Performance Management Analytics Analysis By: Laura Craft Definition: Value-based

performance management analytics complements population health analytics and includes contract modeling, forecasting and performance monitoring; �nancial and clinical risk strati�cation; clinical integrated network performance analysis; and cost/quality optimization analytics. Some vendors additionally offer performance management planning, work�ow and accountability management.

Position and Adoption Speed Justification: Last year, we introduced value-based performance

management analytics to spotlight our expectation that these tools will quickly become a critical capability to be successful under new payment and delivery models. The 2016 U.S. election injected considerable uncertainty into the Affordable Care Act and future payment models, and even though recent events have steadied the course a little, the future still remains unclear. Gartner's advice has been and remains that, regardless of reform measures, value-based care will be a component of healthcare going forward, and recommends healthcare delivery organizations (HDOs) to stay the course and invest in the tools and capabilities needed to manage contracts and �scal integrity under value-based payment models. Value-based payment models continue to get considerably more complex for the HDO and are now a solid part of the fabric of most HDOs. The market saw a moderate uptick in buying activity in 2016, and more population health vendors are adding performance management analytics to their platforms. We move the placement up to prepeak 20% to recognize growing market recognition and maturity, although industry adoption is (at best) 5% of target audience. User Advice: To make sure the HDO is not falling behind in the technology needed for managing

performance under value-based contracts CIOs must: ■

Bring these tools to the attention of the CEO and CFO: It's very possible that the CEOs and

CFOs are unaware of the power and potential of tools like those from WhiteCloud and Cedar Gate Technologies to help drive network performance excellence, and they are the likely sponsors and buying authority for performance management tools. If these tools are not part of strategic planning discussions and included in current year planning and budget assumptions, make sure they are. Hospitals must be able to deliver more sophisticated trending/modeling with greater variables — as they have to know what levers can improve/achieve performance. Some providers are pro�ling providers. ■

Review where your population health vendor is in building these capabilities: Value-based

performance management is adjacent to and 100% synergistic with population health management. As population health management solutions mature, broader capabilities will be included. However, be cautious. The unique characteristic of value-based performance Page 36 of 88



management is the ability to do contract modeling, forecasting and performance monitoring, and the most mature vendors in this space have added performance management accountability into the clinical and operational work�ows. Business Impact: Impact: Performance measurements and analytics must be made available so that there

is ongoing insight and transparency in how the healthcare delivery organization is performing and what variables impact the outcome. Without performance management, it's impossible to expect to change behavior and in�uence results. The stakes are high and the environment is changing. cha nging. Valuebased performance management analytics is of high bene�t and essential for an HDO. Benefit Rating: Rating: High

Market Penetration: Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: 3M; Arcadia Solutions; Cedar Gate Technologies; Forward Health Group; Health

Catalyst; Indegene; Oracle; WhiteCloud Recommended Recommend ed Reading: "Market Guide for Healthcare Provider Value-Based Performance

Management Analytics" "Healthcare Provider CIOs Need to Stay on Course and Procure a Population Health Solution" "Healthcare Provider CIOs Must Drive Value-Based Performance Mana gement Into the HDO Analytics Portfolio" "Developing the Healthcare Enterprise Analytics Strategy Primer for 2017"

At the Peak IoT — Hospitals Analysis By: By: Gregg Pessin; Barry Runyon Definition: The

Internet of Hospital Things (IoHT) is a collection of "things" — including devices, applications, equipment, appliances, buildings — that possess the intelligence and technology to connect, communicate and interoperate with other "things" using standards within the healthcare provider IT ecosystem of smart things. IoHT is foundational to the real-time health system (RTHS).

Position and and Adoption Speed Speed Justification: IoHT is a new Hype Cycle entry this year yea r. The concept of IoT technologies speci�cally for use by hospitals has been growing in hype and is being

introduced on the cycle pre-peak. The hype will grow as more data collection devices within the hospital environment evolve forward into IoT edge devices. This evolution will include data connectivity and communication changing to follow industry standards. This will allow data from multiple vendors and device types to be easily consumed and used by common data collection and analytic platforms. This evolution is expected to plateau in the next �ve to 10 years.

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The individual categories of IoT devices for hospitals will advance, eventually garnering their own places on this Hype Cycle, each moving at their own pace. Tracked edge device categories are: facility devices (security, building management and e nvironmental control), patient monitoring (medical devices, clinical monitors, smart patient rooms and virtual care devices) and real-time location services (asset tracking, patient tracking, employee tracking and visitor tracking). User Advice: Since IoHT projects touch so many ma ny different roles in the organization, these

stakeholders will need to respond to IoHT opportunities in different ways. Use the following points to guide your actions: ■

Start small, experiment and look to other industries and ecosystem partners for ideas.

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Build business cases with ROI extending across core business processes.

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Engage your customers in the solution development. Use prototypes to help explore opportunities. Ensure the architecture teams are ready to incorporate IoHT across IT and operational technology (OT) technology stacks. Increase your capabilities to leverage big data costeffectively. Plan to invest in skills and a nd technology to support IoHT platform and IoHT software integration, data and analytics, as well as managed security solutions. Select your technology and service providers based on their technology stack and their ecosystem of partners. Ensure there is end-to-end compliance of your IoHT solution with health information protection legislation. Adapt your business processes and culture culture in lock-step with IoHT implementations to ensure successful outcomes.

Business Impact: Impact: IoHT is foundational to digital business and, as such, will transform care delivery

as it evolves and matures. ma tures. IoHT projects will positively impact the hea lthcare providers' ability to deliver care more ef�ciently and cost-effectively. Connected things will drive revenue and improve operational ef�ciency and asset utilization. Other bene�ts include: ■

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Improved operations: Better productivity and increased ef�ciency, logistics and coordination Optimized assets: Asset utilization, health monitoring, reliability, predictive maintenance and asset performance management

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Enhanced services: Remote monitoring — telehealth/virtual care/proactive maintenance

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Increased engagement: Improve the experiences of patients, care providers and others

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Improved well-being: Wellness, longevity and care delivery for a better quality of life

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Enhanced security: Protection of physical assets as well as safety of people to reduce risk

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Better use of resources: Energy ef�ciency and long-term pollution reduction

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Benefit Rating: Rating: Transformational

Market Penetration: Penetration: 1% to 5% of target audience Maturity: Emerging

(TempSys); Oracle; Philips; Sample Vendors: Connexall; IBM; Lenovo Health; Mesa Labs (TempSys); ThoughtWire; Vivify Health Recommended Recommend ed Reading: "Developing the IT Strategy for the Real-Time Health System Primer for

2017" "Healthcare Provider CIOs Need to Address IoT's Security Risks Now" "Preparing, Planning and Architecting for the Internet of Things"

Unique Device Identi�cation (UDI) Analysis By: By: Andrew Stevens Definition: A

unique device identi�cation (UDI) is an alphanumeric or numeric code assigned to a medical device and associated devices to make tracking and identi�cation as easy as possible across the supply chain. A UDI needs to be on the label of a device or, for reusable products, as a direct marking on the device itself.

Position and and Adoption Speed Speed Justification: UDI has emerged as a result of discussions and

initiatives across industry and healthcare associations globally over the last 10 years. Its use was driven primarily by patient safety with wider business enablement opportunities proposed, discussed and reported as part of the �nal requirements. The strategic importance of product life cycle management (PLM) for managing and tracking medical devices has also had an in�uence on requirements, emerging solutions and projected supply chain bene�ts. Globally, members of the International Medical Device Regulators Forum (IMDRF) agreed on foundational harmonization standards to ensure uniqueness of a device wherever it is located or used across the world. The 2013 U.S. Food and Drug Administration (FDA) UDI �nal rule regulations are the only active UDI regulations currently, currently, and so global harmonization harm onization standards are currently nonenabled. Emerging solution providers have predominantly focused efforts on ful�lment for the U.S. legislation requirements for management and submissions of multiple supply chain data elements and speci�c device attributes to the U.S. Global Unique Device Identi�cation Database (GUDID). Other solutions utilize PLM sets of capabilities leveraging the data management and submissions as a broaderbased enterprise supply chain solution. While the U.S. regulation remains the only existing framework of reference on which providers can develop solutions, evolution to global mature solutions may be slow. Other regions and countries (especially U.K., Europe and China) are expected to follow with formalized requirements across this year and across next year with expected adoption aligned to the IMDRF harmonization standards. It is expected that many more new and innovative products will become categorized as medical devices requiring UDI, as life science companies, consumer products and healthcare providers try to deliver more differentiated healthcare products and services to patients and consumers.

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User Advice: Supply chain leaders in medical device, life science or consumer products companies

tasked with meeting compliance, and IT leaders responsible for data management and governance need to be aware a ware of UDI requirements and capabilities c apabilities to support them. Additionally, across healthcare providers supporting digital, mobile and software applications for diagnostics and monitoring are also coming into scope as being classi�ed as medical devices in their own right, and they will also need to comply to unique identi�cation regulations. Solutions presently focused exclusively on U.S. requirements will need to scale to meet developing global legislation. Expectations are of similar modes of operations to those of the U.S. with some country-speci�c or regional differentiation. Supply chain and IT leaders need to ensure continued dialogue with solution providers to ensure their scalability and �exibility to meet existing and future updates to U.S. requirements as well as diversi�cation as other country legislation comes into force. Diversi�cation of medical device products is expected to increase further further,, with pipeline products tailored to patient pa tient outcomes and the digitalization of the supply chain. In a period of change in the industry industry,, UDI solutions must adapt to new requirements and redesigned supply chains targeting value-based medical outcomes, differentiated distribution models and future opportunities presented from digital c apabilities. Companies must deploy solutions that can adapt and scale to the global dynamics of industry change as well as deliver robust data management and governance for reporting, submissions, and supply chain integration purposes. c an offer potential bene�ts Business Impact: Impact: UDI regulations and solutions supporting them can leveraged from the value of captured data, system integration and interoperability across supply chain stakeholders. The business bene�t and enablement that UDI presents is formalized in the U.S. regulations and is expected to feature prominently in developing solutions. The FDA makes a special mention of the bene�ts of a UDI system for manufacturers, consumers and healthcare providers, which include: ■

Improved postmarketing surveillance

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Data integration to electronic health records (EHRs)

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Reduction of medical errors via better data accessibility for supply chain stakeholders

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Reduction of, and better control of, medical device recalls

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Harmonization of international and national governing networks of device registries

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Reducing counterfeiting and diversion activities

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Managing health reimbursement claims and �nancial tracking

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Optimizing supply chain processes, and system integration and expansion through the �exibility of automatic identi�cation and data capture (AIDC) options, such as RFID, data matrix bar codes and quick response (QR) codes Contributing to enhanced supply chain cha in maturity through interoperability across supply chain stakeholders and electronic connectivity to patients

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Benefit Rating: High

Market Penetration: 5% to 20% of target audience Maturity: Emerging Sample Vendors: Dassault Systèmes; Inspirage; Kodit; MatrixIT; PTC; Sparta Systems Recommended Reading: "Strategic Assessment of Unique Device Identi�cation for Medical

Device Companies and Healthcare Providers" "Assessment of Unique Device Identi�cation Requirements for Medical Device Companies, Worldwide"

AI Healthcare Advisors Analysis By: Laura Craft; Vi Shaffer Definition: Arti�cial intelligence

(AI) healthcare advisors enhance human performance on cognitive tasks such as diagnostic or therapeutic decisions as collaborators or substitutes, and ultimately support the clinician or the consumer. They ingest real-time data, examine and augment it with natural-language processing and external knowledge/algorithms to output decision support or even decisions. They are often cloud services.

Position and Adoption Speed Justification: We rename this Hype Cycle from smart machine —

healthcare sages for more consistency with coverage of emerging AI capabilities. AI healthcare advisors are a central gear in the radical transformation to precision medicine. Largely delivered as "domain knowledge clouds," they become a key means by which diagnostic and therapeutic advancement can be rapidly and pervasively translated into practice. Such cognitive value is extremely hard to develop, deliver and maintain. It changes entrenched ways of doing things among professional communities — physicians and pharmaceutical/medical device companies — and it evolves the fundamental job of a physician from near-complete cognitive control into something else. AI healthcare advisors will also be able to offer direct-to-consumer decision support and we are starting to see some ministries of health and private healthcare providers incorporate more of this direct engagement into their long-term plans. Success here requires a clearer line of sight from clinical research to medical practice, and melding into clinician work�ows, levering emerging standards. The hype and hope are high among developers and medical visionaries; however, outside of some progress in use for cancer and radiology, healthcare advisor cognitive support is largely experimental. Taken broadly, this pro�le — more than any other in this Hype Cycle — signals the advent of the next generation of cliniciancomputer relationship, which we previously described as Generation 4 of the EHR: "The Colleague." The Generation 3 relationship ("Helper") was represented by most current electronic health records (EHRs), rules/work�ow, and decision support capabilities and content. Results from early contracts and deployments signal struggle with ROI and practical application. IBM Watson for Oncology advisor is still the most publicize d player here. AI advisors for imaging

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diagnostics are emerging. However, even within oncology it is still dif�cult to deploy and confusing about where the "bang" will come from, and the recent breakup of IBM Watson for Oncology advisor and The University of Texas MD Anderson Cancer Center was evidence of this. This is the rationale for nesting this concept closer to the peak of hype and inching it up only slightly from last year's position at pre-peak 15%. This placement acknowledges both market enthusiasm and development progress, but should be viewed as a red �ag about readiness. A recent surge in healthcare advisors for diagnostic radiology will also need to be closely monitored for application and adoption. User Advice: CIOs and chief medical informatics of�cers (CMIOs) should expect increased vendor

marketing. There will be new applications, and many startups eager to get a foot in the door. Be prepared to help educate executives, and evaluate when and how they — particularly the chief medical of�cer — should get involved. Gartner research (see Recommended Reading below) can help de�ne and explain both the importance and long path to value. Innovative medical research and healthcare delivery center leaders should continue to explore AI healthcare advisors and consider teaming with (or investing in) platform or content development. Note that a good part of IBM Watson Health plan involves partners leveraging the platform. Look to your core clinical systems and clinical content providers to lay out how they will incorporate and/or commercially leverage healthcare advisors and cognitive computing in their business models and architectures. We've already seen movement on that front, such as Epic's (nonexclusive) cognitive computing partnership with IBM. Business Impact: Gartner projects that the AI/smart machine era will be the most disruptive in the

history of IT. Given advances in genomics, immunotherapy, nanomedicine, sensors and so many other major innovations on a similar time trajectory, disruption will hit healthcare — and especially its already overstressed physicians — very hard. Eventually, these advances will rede�ne what it means to be a physician and a patient. Many smart machine uses will be controversial for years to come. We believe the greatest impact of AI healthcare advisors may be on medical diagnosis error, the leading cause of medical malpractice claims and the cause of 80,000 to 100,000 signi�cant injuries or deaths annually in the U.S. alone. Enhancing, or sometimes displacing, the foibles of human performance with smart machines offers the prospects of dramatically impacting the nature and structure of organizations and how they achieve competitive advantage. However, HDO leaders should also note that there is new risk and danger from broken algorithms and failure to follow or document variance from AI Healthcare Advisors. Benefit Rating: High

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: Ayasdi; GenieMD; Genospace; Hearst; IBM; Johnson & Johnson (Sedasys); Paci�c Knowledge Systems (PKS); Stanson Health; Welltok; Zebra Medica l Vision

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Recommended Reading: "IBM Watson Health's Smart Truven Buy Amasses Clients and Data"

"Industry Vision: Mass Personalization of Consumer Healthcare Engagement" "Entering the Smart-Machine Age" "How to De�ne and Use Smart Machine Terms Effectively" "Smart Machines See Major Breakthroughs After Decades of Failure"

RTHS Operations Dashboards Analysis By: Gregg Pessin; Laura Craft Definition: RTHS

operations dashboards are decision management tools that collect, synthesize and contextualize real-time operations and clinical data. Their purpose is operations standardization and adapting to variable demand by creating a holistic view of enterprise operations across departmental settings and roles. This allows more uni�ed and dynamic operations and responses to changing conditions. They use BI/big data platforms, in-memory computing, predictive algorithms, complex-event processing and advanced visualization to deliver these services.

Position and Adoption Speed Justification: The advent of RTHS operations dashboards is an

important and salient indicator that we have entered the era of the real-time health system. It represents the quest for precision and proactive healthcare. We will measure advancement of this pro�le based on the number of hospitals who routinely use integrated dashboards across at least two in-hospital settings (for example, ED, surgery, ICU and wards) , supporting at least two roles (such as physicians, nurses, housekeeping and bed transport) and that trigger real-time adaptive operations decisions rather than purely medical decisions. Progressive health systems have been using more limited operations dashboards for years. This pro�le characterizes forward movement in the use of dashboards as both real-time and predictive. They are used to provide a common information source for situations and optimized decision making across a hospital. There are an increasing number of dashboard examples emerging, which is why we place this near the peak; much more development is underway — both self-developed by healthcare providers and by vendors (including but not limited to full-patient throughput and capacity-management solutions). Displays for discrete situational awareness (e.g., bed status, ED wait times, "low risk monitor" scores in the ICU) represent elements and precursors to integrated dashboards. These represent early proofs of concept of what will become a future modus operandi across healthcare. User Advice: To prepare to reap the bene�ts of advancing dashboard capabilities, hospital provider

leadership should: ■

Assemble hospital operations or patient throughput steering committees. CIOs, and CNIOs, in particular, should help raise awareness and evaluate opportunities across the provider's facilities. Use cost and experience metrics and benchmarks to create a shared sense of urgency and accountability, and to help agree on leading indicators.

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CIOs, CMIOs, CNIOs and chief data of�cers will need to play a substantial role in guiding their executives and medical/clinical directors through the realization that they need to embrace these new tools. Select a small IT/informatics innovation team to become more familiar with detailed hospital operations, especially critical paths and potential �ashpoints. Select pilot opportunities where there is strong medical/nursing leadership and pour all the needed resources into it to make it a success. Keep in mind that methods/skills like lean and process engineering are needed to orchestrate the successful use of real-time operational dashboards. Invest in hiring or educating staff in advanced data visualization techniques (such as Edward Tufte courses ) and push for this skill set in your key vendors.

CIOs and CMIOs should note that there is an IT management analog to this kind of operations dashboarding — e.g., IT departments are evolving to use monitoring and dashboarding to a nalyze and proactively manage IT system performance and end-user experience. Some of these will provide valuable real-time operations and clinical management insights. Business Impact: Integrated real-time operations and medical/clinical dashboards — and their

high-impact use — are the ultimate manifestation of the real-time health system paradigm. The pervasive use of predictive dashboards and adjusting operations in real time is transformational for hospital management. The quest for better hospital demand, capacity and patient throughput management, as well as the drive for operational processes ef�ciency and cost control, is palpable in many countries. This is especially true where hospital capacity is strained and where accountability models like ACOs and bundled payment boost the pressure to eliminate unjusti�ed variance in processes, quality and cost. As with all decision support tools, impact will vary depending on the pressure to perform, but more so on the quality of leadership of the healthcare provider. As with more traditional analytics, hospitals with the poorest performance and most variance have the highest theoretical yield, but it's the higher-performing healthcare providers that will realize the greatest bene�ts. However, as these become common, they rede�ne the hospital management paradigm and inevitably provide positive impact across the industry. Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: Cerner; Dimensional Insight; Epic; Exepron; GE Healthcare; IBM (Cognos

Analytics); Intelligent InSites; McKesson; SAP; TeleTracking Technologies Recommended Reading: "Industry Vision: The Real-Time Health System"

"Developing the IT Strategy for the Real-Time Health System Primer for 2017"

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"The Stages of Maturity in the Journey to the Real-Time Health System" "How to Move Analytics to Real Time"

Eldercare-Assistive Robots Analysis By: Vi Shaffer; Nagayoshi Nakano; Kanae Maita Definition: Eldercare-assistive

robots are smart machines designed to help human patient/caregiver clients. They move or navigate in an autonomous or semiautonomous manner, perform tasks, and sense or in�uence their environments. This pro�le includes physician support, psychological support (motivation and companionship) and healthcare delivery services/therapeutic support for observations, monitoring, coaching or emergency action. This exc ludes simpler vacuuming/tugging robots and uses of AI/NLP for healthcare advice (such as oncology).

Position and Adoption Speed Justification: R&D for eldercare-assistive robotics continues to

make slow progress. A major driver is the aging population with functional limitations, and the shortages of skilled and home workers to support it. By 2020, 726 million people, or 9% of the global population, will be 65 years of age and older ("Predicts 2017: Identify Your Buyers to Seize Their Personal Technologies Purchases"). The U.S. and many European countries, as well as Asia/ Paci�c regions, face similar trends. China's exposure is growing as the government's one-child policy and urban migration have left the country with a disproportionately aged population, especially in rural areas. These all combine to fuel interest in robotics and telemedicine. Because of the extreme imbalance of elderly to younger citizens and the extensive robotics savvy in Japan, we look to that country as a bellwether of how these technologies may play out. There, the disproportionate elder population has increased social welfare budgets for nursing care. Currently available/announced personal assistant robots for the home fall into four groups — household/daily care assistant robots, physical assistant robots, multipurpose standstill (or static) PARs, and multipurpose movable (or moving-capable, nonstatic) PARs ("Market Trends: Personal Assistant Robots for the Home"). All of these have potential relevance for the healthcare and related personal needs of elderly people. Some key signs of progress have occurred on this long journey to solutions, and we've highlighted a few below that caused our small advance of this technology this year: ■

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The Japanese government is investing in 10 robotics development centers, run by staff experienced in nursing care and robotics technologies as part of "Japan's Robot Strategy." This is a government-led initiative to expand robot applications to a wide range of �elds. IBA (a robot like the character in "Big Hero 6"), covered with soft material, uses high-precision, �exible and tactile sensing devices called "smart rubber" sensors to quickly detect an elderly person's weight from touch alone. It learns the elderly person's features and needs, and adjusts its operations accordingly. Meijo University (Nagoya City, Japan) will develop the a dvanced version, Robear. Parlo is already used for elderly care solutions in assisted living facilities in Japan. With performing installation of physical exercise as well as dancing and singing attraction for elderly

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people in the facility, it helps reduce the burden on facility staff, especially in the face of constant shortages of caregivers. Adoption is growing throughout Japan. Parlo also recognizes individuals' faces and remembers the names of individuals, and uses this knowledge and what it learned in the conversations with those individuals. User Advice: Robotics will be an expanding �eld in healthcare and socially assistive applications: ■

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Leading-edge healthcare delivery organizations (HDOs) and their medical and population health leaders should become involved in experimentation in the use of robots as more uses become viable. As with other such devices, input and output of data and how "virtual caregiver" technologies will receive and execute daily task lists and patient engagement protocols from primary care physicians and care navigators are intriguing. Government social welfare programs will also want to encourage innovation, monitor progress and participate in trials. As assistive robots reach functional ability and viable price levels, leading-edge health system CIOs should prepare for mobile robots to appear as new endpoints in healthcare IT networks — and possibly for their needs to be represented in IT systems as "virtual" human/provider end users of IT, with unique identi�ers, work�ows and information needs, as well as being unique devices with speci�c deployment and support requirements.

Business Impact: In 2017, we still classify the bene�t as moderate until more value cases are

documented, but the potential impact is truly exciting. We foresee that smart machine robots will someday be essential in delivering home healthcare. They will address worker shortages, decrease delivery costs of healthcare services, and improve quality of life and service for user and caregiver. Assistive robot applications will impact functions that directly supplement the healthcare and social service labor force, or provide innovative, new healthcare and personal services. Consistent engagement of the elderly with smart machines and robots is starting to pan out as improving their mental state through psychological interactivity and physical movement. Nevertheless, while the ultimate impact could be high, for now, we rate the likely early emerging uses as of moderate value. R&D focused on robotics for the elderly is yielding a far more sophisticated and detailed understanding of their everyday needs, and this is expanding the exploration of technology assistance like small personal home drones to fetch, carry and open a pill bottle. Benefit Rating: Moderate

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: AIST (Japan); Fujisoft; GeckoSystems; iRobot; INF Robotics; Kawasaki Robotics;

NEC; Osaka Institute of Technology; The Robotics Institute at Carnegie Mellon University Recommended Reading: "Market Trends: Personal Assistant Robots for the Home"

"Predicts 2017: Smart Cities Must Create the Right Data Orchestration for Sustainable Citizen and Business Services"

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"Arti�cial Intelligence Primer for 2017"

"Predicts 2017: Identify Your Buyers to Seize Their Personal Technologies Purchases"

Cloud-Generation Healthcare ERP Analysis By: Vi Shaffer Definition: Cloud-generation

healthcare ERP is both a next-generation architecture and solution developed organically for, and delivered and managed via cloud services. This category includes modernized versions of the full suite of classic ERP applications for managing enterprise �nancial management, materials/supply chain management and the human capital management suite ("HCM"), including HR, talent management and payroll. Time and attendance and nurse/staff scheduling may be included in vendor portfolios, but are not included in this de�nition.

Position and Adoption Speed Justification: Cloud-generation ERP and the eventual sunsetting of

some legacy ERP products and suites is sneaking up on healthcare executives at a time when enterprise standardization and cost optimization are critical than ever, given government policy and payment uncertainties. Mergers, acquisitions and defederations fuel the need for a highly scalable enterprise system that creates a cohesive operation. While cloud-based ERP is taking off in other targeted industries, the pace for healthcare's speci�c needs is slower. However, there are signs of the �rst real ERP market disruption in 15 years, at least in the U.S. For early adopters, the path includes choosing among new cloud suites, all of which are currently incomplete with limited numbers of implementations. Approaches today, of necessity, will include deconstruction of the traditional ERP suite into a loosely coupled combination of new and legacy applications, sometimes provided by a mix of new and legacy vendors. Certainly, each vendor's plan is to complete the suite and turn this deconstruction into a permanent reconstruction in its favor. Oracle and Infor have the advantage of selling the next-generation ERP to a large U.S. customer base (PeopleSoft and Infor Lawson S3). With supersize HDOs created through M&A acting more like other large corporations, SAP's architecture and scalability might pla y well. However, the most disruptive force is Workday, grabbing healthcare ERP-experienced personnel, and already persuading HDOs like Trinity Health (over 100,000 staff) to trust them with their strong play in HR/ payroll. Public company Premier, long involved with supply chain and business performance benchmarking, has entered the ERP fray and garnered some deals (focused �rst on supply chain and �nancial management). Development progress has been made this past year, and we're tracking early procurements, but (and it's a big caveat) these con�rm that vendor suites in general are still incomplete, with limited case for compelling new value. Thus, we advance this category but keep it in that development section of the Hype Cycle for promising but not yet proven and readily implementable solutions. We do project the buying pace will perk up substantially by 2020.

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User Advice: In 2017, more CEOs, CFOs and CIOs are sensing the press of vendor cloud-

generation promotion, but may not recognize the vendor landscape change going on at the same time: ■

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HDO executives should conduct a strategic review of incumbent vendor performance and product plans. If you can wait, focus on harvesting more value from the software you already have, while monitoring the market as these systems can generate high value. If there is a need to buy or change, make this an open procurement. Don't let "lazy loyalty" possibly constrain your future. Be aware that vendors have modules in alpha/beta stage. If you are an early adopter, expect implementation methods, training, customer support and enhancement management to be immature. Note that outside consulting experience with these systems in healthcare is growing, but limited. Be sure to scope how the IT department will support cloud-based applications and the different approach to upgrades and enhancements.

Business Impact: Strong, scalable ERP functionality is critical to healthcare, as well as critical to operating a cohesive health system. We rate the bene�ts of postmodern ERP as moderate because there is only limited evidence of bene�ts from early deployments. While there are some innovations,

many of the demos feel like old apps have just been cloud-washed. Achieving high value from ERP is highly dependent on aggressive HDO leadership pursuing successive waves of impact and acting like a truly integrated organization. If more innovation evidence emergences, this may shift to a "high" value rating, a level that last-generation ERP delivered. Those 10% of HDOs that are leveraging the current ERP system very well will, ironically, gain more value from these nextgeneration approaches, even though underperformers have more theoretical opportunity to improve. Highly effective HDO leaders are frequently the management centerpoint of newly merged larger organizations and will, therefore, lift up the performance of acquired entities. A second part of the hoped-for bene�ts from this generation of ERP solutions is they will be more economical and simpler for IT to operate (leveraging cloud advantages such as around-release management) and more agile in their ability to update and enhance their functionality without resource-intensive new release projects. Benefit Rating: Moderate

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: Infor; Oracle; Premier; SAP (ERP); Workday Recommended Reading: "Predicts 2017: Healthcare Providers Take a Step Toward Digital

Business" "Business Drivers of Technology Decisions for Healthcare Providers, 2017" Page 48 of 88



"Identify When ERP Renovation Must Turn Into Replacement to Support Business Goals" "Business Drivers of Technology Decisions for Healthcare Providers, 2017" "Transforming ERP to Postmodern ERP Primer for 2017" "Survey Analysis: Hybrid ERP Strategies Dominate, and CIOs Must Address the Impact" "Hype Cycle for Postmodern ERP, 2016" "Healthcare Provider CIOs Should Avoid the Four Major R isks of Megasuite Vendor 'Lazy Loyalty'" "SWOT: Workday, Human Capital Management, Worldwide 2016" "Magic Quadrant for Cloud HCM Suites for Midmarket and Large Enterprises" "What You Need to Know About Infor CloudSuites and the UpgradeX Program"

Healthcare CRM/Contact Center Systems Analysis By: Vi Shaffer Definition: Healthcare

CRM/contact center systems are software applications that combine inbound/outbound call center/patient access service management with a single view of the patient that captures "the voice of the customer." These systems are complementary to the patient scheduling, check-in/check-out and admitting/discharge/transfer functions found in patient administration systems, physician practice management systems and integrated electronic health record (EHR) megasuites.

Position and Adoption Speed Justification: Although a modern, scalable approach to CRM and

digital marketing that's more like consumer industries is recognized as desirable, progress is slow. Many health systems still approach this as a niche-by-niche set of smaller vendor decisions. Healthcare integrated delivery systems (IDSs) are now multichannel enterprises with an increasing array of on-site and online services. However, they are behind in their thinking about patient access and administration as key touchpoints for engagement and population health management. Multichannel customers already expect the retailer to sustain basic interactions with them that are connected, continuous, convenient, contiguous and consistent. For the coming decade, customers additionally and increasingly expect collaborative and personalized interactions with the retailer's brand. Healthcare consumers are already multichannel customers in other aspects of their lives. HDO executives have traditionally de�ned their top customers as referring physicians. Thus, CRM for referring physicians is more common than CRM for patients. HDOs will be challenged to manage both points of view, which is one of the essential juggling acts of the transition to value-based care and patient engagement. Because of the many barriers to its adoption and the relatively slow pace of enterprise sales, we nudge this category ahead only slightly this year. Still-open questions:

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Will major players in CRM like Salesforce succeed in their nascent efforts to really deliver, sustain and enhance healthcare-speci�c solutions, not just CRM platforms requiring customization at every site, or healthcare payer versions with a tweaked veneer for providers. Computer-telephony integration is crucial to smooth, personalized handling of patients' calls. The patient's phone number brings up not only the patient's demographic record for call routing, but also the patient's health record when the call is transferred to clinical personnel. How strategic will CRM/contact center vendors become for healthcare delivery executives? HDOs' most strategic partners, their EHR vendors, are rapidly expanding their systems to perform population health management, which includes care management and patient engagement. The dividing line is blurring between mostly nonclinical patient interaction handled in the CRM system, patient scheduling/administration, and care-pathway-driven engagement handled in a dedicated care management system or megasuite EHR?

Measurement of progress is de�ned as the percentage of hospitals and health systems using CRM for both of the categories included in the de�nition. We predict that most larger HDOs will begin gravitating toward established vendor solutions for CRM for patient access/contact center toward the tail end of �ve years, but it will take closer to 10 for retail-equivalent CRM-enabled techniques to �nd their way to the mainstream. Therefore, smart use of CRM systems interwoven with patient access and EHR systems will be a �nancial differentiator for about 10% of HDOs through 2020. User Advice: ■

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CIOs must carve out the time to drive an enterprisewide review of needs and selections, even while investing heavily in other strategic vendor relationships. The CIO should establish a stronger planning relationship with chief marketing of�cers to discuss multichannel integration. Together, they can help speed up their focus on an enterprisewide approach that considers both CRM and digital marketing as the companion to patient administration, care management and portal capabilities. If there are senior executives in charge of population health and/or patient engagement, add them to this task force. Engage with the major vendor players to understand how they are de�ning healthcare-focused solution packages that deal with the multiple ways consumers interact with an HDO. The real potential of CRM in healthcare requires top integrated delivery systems to collaborate intensely with the best CRM companies to de�ne and encourage the industry-speci�c R&D and business-unit-like accountability to make this work. Press these vendors to apply lessons they learned from retail CRM to healthcare. Make sure to review vendor terms and internal policies and design to ensure CRM candidates understand and comply with patient privacy rules.

Business Impact: The potential business impact of a scalable system to handle HDO

enterprisewide CRM is substantial and includes the following: ■

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Economies of scale from consolidated basic operation of the patient access center for managing both inbound and outbound telephone and electronic communications Increased revenue generation from easier appointment making, reminders and follow-up

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Improved patient satisfaction scores/rewards and reduced complaints to public/private payers and large employers Improved chronic disease management patient outcomes (and in the U.S. accountable care metrics) by assuring appropriate-interval visits and so on Rich source of encounter and "voice of the customer" data for precision analysis of consumer sentiment, patient engagement and more personalized management


Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: Avaya; Evariant; Mazik Global; Microsoft; Oracle; Pega; PowerObjects;

Salesforce; SpiceCSM Recommended Reading: "Industry Vision: Mass Personalization of Consumer He althcare

Engagement" "Leveraging IT for Effective Healthcare Consumer, Member and Patient Engagement Primer for 2017" "The Elusive CRM Magic Quadrant" "Magic Quadrant for the CRM Customer Engagement Center" "The Eight Building Blocks of CRM: Overview"

3D Printed Surgical Implants Analysis By: Vi Shaffer Definition: This

technology covers the use of 3D printing (3DP), tissue engineering and spacer technology to replace existing medical implant approaches and create new specialized ones. The method matches each patient's unique shape to a surgical solution. It creates a 3DP design from computed tomography ( CT) scans and uses proprietary algorithms to print the implant and offer individualized surgical guides and/or instruments. This pro�le replaces 3D-aided hip/knee implants.

Position and Adoption Speed Justification: Incorporating 3DP into the design, manufacture, and

implementation of implants of all types is gaining advocates and market awareness within the medical community, as more successes are publicized. Commercial vendor healthcare business units have formed as prestige research institutions invest in the technology. Mayo Clinic completed its �rst 3DP hip replacement, but advanced to performing a 3DP-aided face transplant in 2017. Embody (spun out of Imperial College London) has become a leading innovator in integrating models and implants with individualized surgical tools.

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New 3DP transplant and repair successes continue to pop up across many patient circumstances, but are still largely the domain of top clinical research institutions. Based on a small pick-up in mainstream HDO interest, vendor investments, growing awareness and e xpanded vendor support, we place this just past the peak in 2017. We anticipate expanded uses of 3DP implants will be among the faster paces of adoption experienced in medical innovation. We also note that use cases will continue to expand, and technology capabilities will evolve for years. Note that presurgery anatomical models are on a faster adoption trajectory, and the overlap of vendors means that sites applying that approach will be increasingly exposed to implants and tools as integrated solutions. While material science is starting to ramp up in this space, different microstructures, materials, meshes and chemistries are going into the implants themselves. Although this complexity adds to the capabilities, it also adds to the complexity of translating into practice. Although regulatory approval has been waived in emergency situations, it is also a hurdle. Payer endorsement/ reimbursement is an even trickier topic because of the highly customized approach. Physicians report that insurance is covering hips and knees at the same rate as traditional implants, with the caveat that the patient may have to pay for scans for design rather than for diagnosis. Regional 3DP creation/distribution centers are developing with appeal for smaller markets. Examples of 3D printed implants: ■

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3D printed titanium sternum (see "Further Advances in Healthcare With Implant of 3D Printed Titanium Sternum" ) Stanford bladder reconstruction (see "Stanford Scientists Create Three-Dimensional Bladder Reconstruction" ) Beijing Third University Hospital spinal implant (see "Walking Again! Patient in China Credits Cure to 3D Printing & Spinal Implant Surgery" ) 3D printed ankle bone at hospital in Hong Kong (s ee "Ankle Bone Replicated With 3D Printing Tech in Medical First for Hong Kong" ) Facial transplant and reconstruction by Mayo Clinic (see "Mayo Clinic Announces Successful Face Transplant on Wyoming Man" )

User Advice: It might surprise some CIOs that Gartner suggests medical uses of 3DP as a topic for

CIO leadership. However, like telemedicine and virtual care, 3DP is an expanding area with a wide range of current, emerging, and visionary medical uses and users, and a need for an enterprisewide orchestra leader. A good approach is to lend staff to investigating the state of the art and teaming with surgeons especially to create a proposed approach and strategic roadmap. Early leaders have created an innovation lab where technologies can be acquired/accessed for pilots across multiple use cases. Remember that an increasing number of CIOs also bear responsibility for clinical engineering and biomed technicians. CIOs and IT leaders can also assist in identifying and negotiating 3DP and imaging vendor relationship for on-site technology and manufacturing suppliers. Business Impact: Implants are a big business, and 3DP will have a big medical impact. Hip and

knee replacements are among the most common hospital procedures around the world — more than 1 million are performed annually just in the U.S., and around 160,000 per year in England and Page 52 of 88



Wales. Thus, leveraging 3DP technology for implants is a big boost for 3DP's importance and will be one of the darlings of 3DP publicity. The total potential impact for patients of precision 3D printed implants and related items (precise and personalized models, instruments, and surgery plans) is stunning. We have moved its bene�t rating from high to transformational because of the breadth of potential impact on life and quality of life. Benefit Rating: Transformational

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: 3D Systems; Arcam; ConforMIS; Embody Orthopaedic; GE Healthcare; Lithoz;

Materialise; Royal Philips; Siemens Medical Solutions; Stratasys

Population Health Management Solutions Analysis By: Laura Craft Definition: Provider-based

population health management solutions (PHMS) represent a set of IT capabilities and related services that enable provider organizations to manage populations of patients and achieve speci�c quality, cost and experience goals. Gartner has de�ned "population health management" to include the following components: performance management, care management and coordination, patient and consumer engagement, data, analytics, and infrastructure.

Position and Adoption Speed Justification: Investing in population health management solutions remains a top priority of CIOs in the U.S. and worldwide. This technology pro�le combines both

population health management platforms and population health management analytics, as the vendors have combined functionality within their solutions. The market remains active with a constant stream of new entrants and surprising stability in the pool of existing players, although many vendors have reported long buying cycles. As Gartner predicted, the megasuite vendors, Epic and Cerner, are getting more traction in the market, with many clients reporting they are using the Epic or Cerner population health solutions, which may be in�uencing the delayed procurements other vendors are seeing. As the vendors are tested by the early adopters, we are seeing mixed degrees of satisfaction. User conferences are beginning to showcase real successes, while a 2017 roundtable discussion at Gartner's Data and Analytics Summit highlighted some real challenges. Because of this, we position population health solutions just before its beginning slide into the Trough of Disillusionment at post-peak 10%. However, Gartner expects a quick fall and a rapid ascent to productivity within �ve to seven years. We bump adoption up from adolescent to early mainstream to re�ect the uptick in industry implementations. User Advice: CIOs, chief data of�cers (CDOs) and population health management leadership who

have not yet in invested in a population health management solution need to: Deliver to your senior leadership a population health management architecture and a roadmap for how you will incrementally deliver strategic capabilities: Implementation of the

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data, analytics and technology requires time, thought and investment, and must be in place to support evolution of the operational components across the HDO and the extended network. Maturity with using them will take time so foundational capabilities need to be implemented now. The CIO needs to align the roadmap with the PHM business strategy that outlines what they need, expect and must have. The value-add of these products is less risky and ambiguous, and more concrete in how they are being used to meet the challenges of PHM. Refresh your understanding of the market and the vendors since 2016 marked a year of significant vendor progress: A refresh of the vendor space is critical, because in 2016, we saw a

notable improvement in the leading megasuite vendors' population health capabilities (Epic and Cerner), as well as credible progress with leading stand-alone solutions. Buyers now have a richer and more mature market from which to select. Those buyers that have been waiting to see if their EHR vendors would mature their capabilities may have some new opportunity. Stand-alone vendors have become more experienced with integrating with foundational EHRs making their solutions seem less siloed. It's interesting that in interviews and focus groups, Gartner has found little evidence that there is a differential bene�t to leveraging the EHR vendor. Vendors should also have more credible reference accounts to support the selection and evaluation process, so the selection process can be more informed. Business Impact: PHMSs will be a major piece of enabling technology in the successful

transformation and delivery of care under value-based models. The convergence of analytics with care management and coordination, and patient engagement, broadly distributes the audience that may interact with a PHMS across the HDO. This is a critical reason PHMSs are needed for integrated analytics and management across owned and nonowned facilities and services such as a CIN. The impact can be pervasive if used as intended to identify and target important populations, proactively treat those patients with integrated and informed tools, engage patients in their health and wellness, and evaluate and improve both clinical and �nancial outcomes. However, success is reliant on leadership, governance and accountability outside of IT, and PHMSs have the greatest chance of success when folded into the HDO's population-health-executive-level strategy and program leadership. For now, current market instability is limiting pene tration and the lack of widespread success stories for HDOs to model their implementations after. The market will be looking for results from early adopters of risk-based partnerships with the vendors. Benefit Rating: Transformational

Market Penetration: 5% to 20% of target audience Maturity: Early mainstream Sample Vendors: Allscripts; Arcadia Healthcare Solutions; Cerner; Enli Health Intelligence; Epic;

Forward Health Group; Health Catalyst; IBM Watson Health; Optum; Wellcentive Recommended Reading: "Population Health Management Maturity Model, Version 2.0, Lays Out a

Future Path for Healthcare Providers" "Gartner Population Health Management Framework for Healthcare CIOs" "Healthcare Provider CIOs Need to Stay on Course and Procure a Population Health Solution" Page 54 of 88



"Market Guide for Healthcare Provider Population Health Management Platforms"

Genomics Medicine Analysis By: Vi Shaffer Definition: Genomics

medicine and its IT support enables the translation of genomics into patient diagnosis, treatment, and personalized health/disease risk prediction and management. This includes genomics-speci�c data capture (family history) EHR work�ows, testing and clinical decision support/knowledge management, data storage and analytics, and many �elds, such as functional genomics, proteomics, epigenomics and pharmacogenomics.

Position and Adoption Speed Justification: Genomics medicine is one of the most important advances ever in medical science. Such an advance is as dif�cult to translate into bene�cial

practice as it is to uncover, and has required decades and extensive research to deliver. There are weighty barriers, including development and clinical trial and regulatory approval of new tests, drugs and therapies. It is equally challenging to make this knowledge actionable by physicians within their work�ows (as well as for consumers). Experiences to date in oncology and across other disease areas are setting expectations high that genomics will solve many medical mysteries, driving substantial government and private research funding. Overall, however, and especially measuring against the opportunities beyond oncology, adoption is in the 1% to 5% range. However, there is substantial convergence on the technologies, test c osts, decision support and processes for applying raw sequencing data. These signi�cant events are new signposts of increased momentum and the need for HDOs to solve the genomics delivery dilemma: ■

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The U.S. Food and Drug Administration's April 2017 announcement "allowed marketing of 23andMe personal genome service genetic health risk (GHR) tests for 10 diseases or conditions … the �rst direct to consumer (DTC) tests authorized for information to consumers on an individual's genetic predisposition to certain medical diseases or conditions." (See "FDA Allows Marketing of First Direct-to-Consumer Tests That Provide Genetic Risk Information for Certain Conditions." ) The Institute for Molecular Medicine Finland announced in 2016 it is building a platform that integrates genomic and health outcome data from Finnish national population biobanks into a globally unique and valuable big data repository. (See "Finnish Genomes Empowering Personalised and Predictive Health." ) HCA, one of the world's largest health systems, in 2017 acquired Genospace to deploy its genomics decision support across its large-volume Sarah Cannon cancer services across the U.S. and the U.K. (See "Sarah Cannon and Genospace, LLC Announce Merger to Enhance Cancer Care Through Personalized Medicine Platform." ) Geisinger Health System (U.S.) announced its "Springboard Healthy Scranton" program will prescribe healthy foods and sequence tens of thousands of citizens to study the genetic versus behavior origins of their high chronic disease prevalence.

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In addition, for the longer term, Illumina, the largest maker of DNA sequencers, is launching a DNA sequencer with new architecture that it says "could push the cost of decoding a human genome from $1,000 to $100" — although that decrease will not come for years. (See "Illumina Promises to Sequence Human Genome for $100 — But Not Quite Yet." ) User Advice: Healthcare provider CIOs, CMIOs, and medical and population health leaders: ■

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Understand the evolving market. Become educated on different approaches and options. Plan for a rich combination of tools and knowledge/decision support services to incorporate genomics medicine in the disease domains they serve. Scrutinize EHR vendors for their plans to support genomics medicine needs, such as the ability to record, store, secure, and access genetic marker data from patients, and their ancestors and family members, within the individual patient's record. Understand that enhanced genomics decision support for diagnosis and treatment will likely come from a combination of traditional evidence-based content vendors, government sources, genomics data banks and bioinformatics providers. Plan to leverage FHIR for continuous connections between genomics and analytics results. Functional interoperability of this rapidly advancing knowledge with individual patient data in the EHR and clinician work�ows will be critical.

Life science CIOs and IT leaders: ■

Outline business process, compliance, regulatory and IT implications when including genomics medicine disciplines for decisions about research, therapies and business opportunities, while ensuring patient privacy.

Business Impact: The major value of genomics medicine so far has been in more �nely tuned

diagnosis and better-targeted cancer therapy and more careful prescribing for 120 or so medications. The use of genetics-directed chemotherapy is increasing. Prenatal care and genetic counseling are also active areas of value. Long-term, the business and population health impact of genomics medicine will be substantial. Researchers, life science companies, healthcare providers, and consumers variously will require genomics raw sequencing data, analysis and recommendations from sequencing data, results integration with EHR system and therapy selection support. Information exchange is needed among scientists, providers, patients, and families for collaboration and counseling. Increasingly, medication prescribing will be based on the presence or absence of enzymes suggested by genetic testing. Disease diagnosis and advising patients on managing health risks will rely more and more on genetic analysis. New genetic markers are constantly being discovered, requiring frequent reanalysis of patients' sequencing data. In turn, EHRs will need to be always connected to genetic analytical results so that diagnosis and treatment decision support are accurate and timely. For genomics medicine to be practiced on a wider scale, sweeping changes to platforms accessed by healthcare payers and providers will be necessary to expose more analytical tools, which can be leveraged to design better therapies. It will also impact products under development at

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pharmaceutical companies, adverse event reporting and safety systems, and product development platforms. Benefit Rating: Transformational

Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: 23andMe; ActX; Ancestry; Claritas Genomics; Euro�ns Genomics; Illumina;

NantHealth; Quest Diagnostics; Sarah Cannon/HCA (Genospace); Seven Bridges Recommended Reading: "Prepare Your Healthcare Delivery Organization for the First Wave of

Genomics" "Cool Vendors in Healthcare Providers, 2017" "Cool Vendors in Healthcare Providers, 2016"

PHI Consent Management Analysis By: Mike Jones Definition: Protected

health information (PHI) consent management is a system, process and set of policies for consumers and patients to determine what health information they permit their care providers to access or exchange. It enables them to af�rm participation in patient portals and health information exchanges (HIEs), and to establish privacy preferences (at a granular level) for access to their PHI regarding purpose and circumstances of use.

Position and Adoption Speed Justification: With the move to a more collaborative care

environment, it is more essential than ever to balance legitimate privacy concerns with the bene�ts of sharing PHI. In 2010, the U.S. Of�ce of the National Coordinator for Health Information Technology (ONC) met to address the concern that without a granular approach to consent, many individuals would completely opt out of sharing their PHI data and a great deal of potential value would be missed. Today, most consent management is still done on paper, although technology is less of an issue and a few mature consent management tools are available. In other regions of the globe, similar national debates have taken place. In 2017, the citizen-centric view of data control is now being realized in national and regional HIE and care record system architectures as the risks of not sharing data across agencies have become more prominent and balanced against the default position of not sharing without consent (that is, "opt in" is on the decline, versus "opt out by default"). Consent management projects will be driven by a strong collaboration between those concerned with policy and those concerned with the technological implications. The privacy needs of HIEs, accountable care and patient-centered healthcare movements will continue to drive industry interest in consent management going forward. The sheer complexity of providing granular consent capabilities to consumers and patients across independent electronic health record (EHR) systems,

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together with the lack of agreed-to industry consent standards, continues to slow the movement over the past year. During 2016, we have noticed that the issues of citizen and patient consent to share are at the heart of many national and regional integrated care record strategies. As such, PHI consent management will be a key architectural building block of these initiatives, and adoption will continue to increase in relevance. User Advice: CIOs, chief medical informatics of�cers (CMIOs) and those involved in privacy,

security and compliance within healthcare delivery organizations (HDOs) and HIEs should be thinking about what policies and technical controls are required to manage consent and limit the disclosure of PHI. They need to be asking what kind of consent management systems will be needed in the future to record and enforce the preferences of their consumers and patients. HDO CIOs will also need to make their legacy systems more privacy-aware. Any participation in an HIE should be based on a clear understanding of the policies for consent management, and whether those policies will be enforced centrally by the HIE, or whether the enforcement is a requirement of the end subscriber. Business Impact: Limited efforts have been made to date to prevent clinical system users from

accessing PHI that is beyond their required need to know. Role-based access controls typically permit users to access PHI available to their roles, even when such access is unnecessary or inappropriate. Application log management tools detect access after it has occurred. Patients and consumers concerned about the con�dentiality of their PHI are less likely to participate in an HIE. HDOs and HIEs should capture consumer preferences using consent management tools suitable for automation, and apply those preferences systemwide. Most HIEs have implemented general opt-in or opt-out models without highly granular controls. Benefit Rating: Moderate

Market Penetration: 1% to 5% of target audience Maturity: Adolescent Sample Vendors: Deloitte; Global Patient Identi�ers; HIPAAT; InterSystems; Jericho Systems;

McKesson; Optum; Private Access; ZeOmega (HealthUnity) Recommended Reading: "Business Drivers for Healthcare Provider Information Technology

Decisions, 2017" "Implementing and Managing EHRs, Telemedicine and Other Care Delivery IT Primer for 2017" "Leveraging IT for Effective Healthcare Consumer, Member and Patient Engagement Primer for 2017" "Modernizing Healthcare Administration Systems Primer for 2017"

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Sliding Into the Trough 3D Bioprinted Human Tissue Analysis By: Vi Shaffer Definition: 3D

bioprinted human tissue is the use of 3D printing technology to build functioning living tissue in vitro (e.g., skin) for human use. It involves representation of clinical imaging data as computer models and translation into programs for controlling the motion of 3D ce ll-dispensing nozzles and other 3DP technologies. This de�nition excludes bioprinted human tissue for life science R&D, 3D bioprinted human organs, implants or prosthetics.

Position and Adoption Speed Justification: There are so many practical uses of this engineering

feat that this market is maturing faster than the very challenging effort toward 3D printed human organs, which is why we these technologies are separate since 2016. Technologies at the Plateau of Productivity have mainstream medical use of the technology for at least three discrete human tissue use cases. This approach reveals whether various scienti�c and engineering challenges of production are conquered, such as printing human-scale tissues of the various personalized fabrications, and precise shapes and sizes required for the individual patient, plus nutrient diffusion for cell survival. Among the examples (some of which have already been achieved in clinical research trials on human patients) are multilayered skin, bone, vascular grafts, and heart and muscle tissue. Important progress has been made this past year in R&D, viable vendor formatio n and marketing. This causes us to move this ahead fairly aggressively to the midpoint between the peak and trough. For example: ■

■

In the U.S., the Wake Forest Institute for Regenerative Medicine , with funding from the U.S. Department of Defense, reported in May 2017 that it is "working to grow tissues and organs and develop healing cell therapies for more than 30 different areas of the body, from kidney and trachea to cartilage and lung." Chinese company Medprin Biotech, founded in 2008, says its �rst product, ReDura BiomimeticSynthetic-Absorbable Dural Substitute, is reaching broad adoption ("more than 30 countries on tens of thousands of cases") and "is considered as the dura mater (ridge) membrane (surrounding the brain and nerve cells) closest to a patient's autologous cells with the best repair effect." Medprin has set up branch of�ces in the U.S. and Germany, and references an array of human tissue repair products: "personalized skull and maxillofacial repair system, female pelvic diaphragm repair system, tensionless urethral sling, hernia repair piece, arti�cial skin, blood vessels and ligaments."

Barriers to progress include regulatory approvals following controlled clinical trials for many use cases, the set-up of capability centers through innovation labs and external supplier partnerships, and questions about cost and payment for these customized repairs. Another challenge is how to maintain sterility and prevent bad cells (e.g., cancer and contamination) from being 3D printed. User Advice:

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Medical leaders in life science and health insurance industries will want to keep a close eye on all advancements in 3D bioprinting for appropriate involvement and decisions. Healthcare delivery organizations' clinical service line and physician leaders should prepare to position their institutions for adding relevant advance s from this emerging �eld, and working relevant regulatory and payment issues. CIOs, CMIOs and VPs of supply chain in developed countries should begin working with their physicians, 3DP tissue suppliers, and vendors of EHRs and ERP/supply chain systems within three to �ve years to manage the broader use provision of 3D bioprinted tissues. They should also prepare to work with the associated supply chain implications, IT order entry, item master/ charge master, tissue tracking, documentation and decision support capabilities. This may include creation of unique identi�ers, compliance with as-yet-unde�ned track-and-trace and chain of custody requirements for implanted tissues. EHR system order entry, clinical decision support and documentation needs will also need to be determined.

Business Impact: The ability to build new and precisely personalized human tissues greatly

expands the array of therapeutic options for a wide range of patient injuries and diseases. 3D bioprinted tissues using the patient's own cells are hoped to have the bene�t of avoiding the rejection of implanted/transplanted tissue, and the costs of a lifetime of anti-rejection drugs. Many of the emerging use cases are for limited or rare scenarios. However, in aggregate over time, the total impact on medicine is high. There is a current and currently bigger market for production of 3D printed tissues for life science research and drug testing. This business is providing some of the needed early revenue for startups, targeting human tissue and organ 3D bioprinting. Benefit Rating: High

Market Penetration: Less than 1% of target audience Maturity: Emerging Sample Vendors: Cyfuse Biomedical K.K.; Medprin Biotech; Nanoscribe; Organovo; University of

Zurich; Wake Forest Institute for Regenerative Medicine; Wyss Institute Recommended Reading: "3D Printing Opportunities and Uses Primer for 2017"

Real-Time Physician Documentation Improvement Analysis By: Barb Mann; Vi Shaffer Definition: Real-time

physician documentation improvement (RTPDI) solutions deliver specialized real-time decision support to optimize clinical documentation at the point of care in order to increase accuracy, comprehensiveness and ef�ciency by leveraging arti�cial intelligence (AI), natural-language processing (NLP), rules and algorithms. RTPDI engages clinicians in real time at the point of care, as opposed to CAC and CACDI solutions, which are leveraged postdischarge by coders/specialists.

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Position and Adoption Speed Justification: RTPDI innovation is accelerating, with growing

pressures on HDOs to reduce medical costs, improve margins, and capture the data necessary to take on increased risk in value-based care models. This drives HDO demand for comprehensive and compliant clinical documentation. Real-time data capture supports optimized scoring and coding at the point of care, driving downstream ef�ciencies and revenue. Vendors have made progress in solution development and in marketing to early adopters this past year. We place RTPDI signi�cantly behind computer-assisted coding (CAC), which gained traction during the ICD-10 conversion mandate. The RTPDI vendor positions are shaking out differently than the CAC market, largely because of Nuance and M*Modal's ability to leverage prior physician documentation capabilities and bring to market uniquely branded solutions. Vendor innovations are projected to quickly evolve as demand for real-time, intuitive support increases to address the complexity in clinician documentation, while reducing impact to ensure adoption and results. We will see increasing market penetration in 2017 and beyond as EHR vendor activity continues to partner to embed RTPDI functionality with multiple vendors. Competition will increase to differentiate RTPDI to support greater real-time clinical data collection, trigger real-time alerts and downstream system functionality. This technology pro�le is based on the initial RTPDI market in the U.S., and, because this capability is dependent on NLP, it may not be available in many countries for a long time. User Advice: EHR voice recognition and documentation templates, CAC, and previous CDI

programs have all paved the road for RTPDI. However, nonintrusive, fully adopted RTPDI cannot be driven by the CFO and revenue cycle/healthcare information management (HIM) department leaders alone. Medical leadership must champion the NLP/AI-aided approach to revenue and quality measurement. CIOs, CFOs and chief medical of�cers should ensure that: ■

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CMIOs and HIM leaders share responsibility for CDI and RTPDI strategy, planning, evaluation, implementation and bene�ts, along with other documentation support tools. The current physician climate and level of burnout are assessed and addressed in the timing and the introduction of new tools, care models, and data capture expectations. CMIOs accept ownership of "coding for money," and �nd the balance between protecting physician productivity and advancing the total health outcomes offered by RTPDI — clinical, �nancial and operational. Information, data governance and data stewardship are a priority at all levels of the enterprise. Clinical data drives �nancial, operational, and clinical outcomes with monitoring, decision support and analytical capabilities. Executives must drive a core competence in data mastery to fully drive results. Longer-term strategy aligns to a consolidated approach across RTPDI and CAC vendors to optimize functionality and performance. They assess RTPDI vendor tool interoperability and usability within the EHR work�ow, and engage clinicians in requirements to optimize ef�ciencies, productivity and adoption.

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Business Impact: The �rst focus and value of RTPDI is on revenue. Its use can strengthen the case

mix index (CMI), a revenue contributor factored into payment models. RTPDI supports the capture of all rendered services, clinical complexity and severity, and critical patient data and con�icts. The solution prepopulates templates; saves time; avoids clinician disruption with queries; collects quality and core measures; and integrates with data registry, clinical trials candidate identi�cation and clinical indicators. Real-time improved documentation increases quality and adds speci�city with medical evidence that improves patient safety alert accuracy, reporting for value-based care models and revenue cycle performance. RTPDI also identi�es the Joint Commission's dangerous abbreviations list, and conducts ongoing surveillance for alerts. As value-based care continuum models expand, RTPDI will evolve across care coordination and population health with increased data accuracy and triggers. In turn, leaders will gain greater visibility into true total operational and medical costs with granularity to support improved oversight of performance in risk-sharing care and payment models. While these solutions are very important to the overall management of clinical and revenue cycle operations, we rate their relative value as "moderate," simply recognizing that RTPDI is an adjunct to transformational and high-value systems, like the patient �nancial system (PFS), EHR and CDI programs, as well as quality improvement and population health platforms. Bene�ts are theoretically highest for HDOs that have not embedded a CDI program into their operations and use RTPDI as a trigger to �x that, but only if the organization also boosts the quality of its leadership and physician relationship management. Benefit Rating: Moderate

Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: 3M Health Information Systems; Arti�cial Medical Intelligence (AMI); M*Modal;

Nuance Recommended Reading: "Market Guide for Healthcare Computer-Assisted Coding, Computer-

Assisted Clinical Documentation Improvement and Real-Time Physician Documentation Improvement"

OpenNotes Analysis By: Thomas J. Handler, M.D.; Mike Jones Definition: OpenNotes

is a healthcare delivery organization (HDO) initiative to give patients convenient access to their clinical records stored within the electronic health records (EHR). This is most often accomplished through a portal tethered to the EHR.

Position and Adoption Speed Justification: With U.S. EHR adoption exceeding 96% and

increasing in other countries, the deployment of more functional patient portals, and government initiatives granting individuals' access to their records; the enabling technologies and the legal incentives are coalescing to empower broad patient access to most of their medical records. However, patient access to clinical notes (the portions of the medical record in which physicians Page 62 of 88



document — often in free text — the patient's report of their symptoms, physical exam observations, the physician's assessment of likely diagnoses and the proposed treatment) has been the subject of skepticism and controversy. Chief concerns were disruption to the provider's work�ow, alarm to patients and lack of evidence of value to patients. The OpenNotes movement was formed in 2010 to advocate for the value to patients and to quell the concerns of providers. In December 2015, four foundations dedicated $10 million to expanding full visit note access to 50 million patients in the U.S. At present, more than 20 million U.S. patients have access to the clinical notes from their physician visits — up from 3 million in 2014. Leading EHR vendors have made implementation of OpenNotes a matter of con�guration rather than coding. As adoption among leading institutions progresses (for example, the University of Texas MD Anderson Cancer Center, Beth Israel Deaconess Medical Center, Geisinger Health System, Kaiser Permanente, U.S. Department of Veterans Affairs and Carolinas Healthcare System), evide nce of its bene�ts grows (including improving patient engagement by allowing better recall of care plans, creating a clearer understanding of their conditions, and giving patients the feeling of being more in control of their healthcare). OpenNotes has attracted a lot of interest in Europe, Australia and New Zealand, but capabilities are limited by the EHRs in those countries. Estonia's nationwide EHR displays visit notes. Notes in Sweden are viewable on a countywide portal. Hospitals and practices in the U.K. are beginning to provide access to visit notes. It is increasingly likely that OpenNotes will be rapidly accepted and reach mainstream adoption in less than �ve years. User Advice: CMIOs, CIOs, chief medical of�cers, chief patient experience of�cers and chief population health of�cers should endorse and advocate within their organizations for the philosophy

of patient-provider collaboration and data sharing that is behind OpenNotes. Public health agencies and ministries of health should endorse OpenNotes because it educates and engages patients in their care, resulting in favorable clinical and �nancial outcomes. Look to the OpenNotes website for assistance with policy, adoption and change management issues. CMIOs who are contemplating a move toward implementing OpenNotes should immediately engage the technical teams supporting the electronic health record (EHR) and/or patient portal in order to determine which OpenNotes policies and reporting requirements can be accommodated, and with what level of effort. Access to medical records could be detrimental for certain patients, and it is prudent to establish policies that allow physicians to exempt those patients from online access to their record or parts of their record. Additionally, policies may be needed to allow patients to opt out of, or establish their preferences for, receiving noti�cations that notes are available for viewing. HIPAA requires that a mechanism be established for reviewing and posting amendments submitted by patients. As the patient portal garners more use, consider establishing a web service desk to help patients navigate the technology. Payer CIOs and medical management leaders should monitor the adoption of OpenNotes, and consider adding content to care management programs that assists members and their caregivers in accessing and interpreting their clinical notes. Business Impact: OpenNotes has shown early indications of signi�cant value to patients, as

published in prominent industry journals (see a list of more than 25 journal articles at the OpenNotes website). This value includes the following:

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Validating patients' right to conveniently and securely access their medical records Enhancing the quality of the medical record by allowing patients to discover and correct errors Improving outcomes by better communicating physicians' observations and instructions to patients and their caregivers

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Assisting patients in feeling better-prepared for their visits

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Activating patients to take greater ownership of their health and treatment

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Motivating patients to be more consistent in taking their medications

Although surveys of the OpenNotes participants have shown convincing patient and provider satisfaction, the impacts on clinical, utilization and �nancial outcomes are still being assessed. Some HDOs have reported improvements in medication adherence. Some implementations of OpenNotes have demonstrated that the availability of clinical notes has driven signi�cant increases in patient portal adoption, thereby indicating potential secondary value in meeting meaningful use requirements and administrative ef�ciencies. Those HDOs that adopt an OpenNotes policy will be able to use it in messaging to underscore their commitment to enabling transparency through technology, providing patient access to records and fostering patient engagement with their caregivers. Benefit Rating: Moderate

Market Penetration: 5% to 20% of target audience Maturity: Adolescent Sample Vendors: Allscripts; Cerner; Epic Recommended Reading: "Healthcare Provider CIOs Must Begin the Pivot to Putting the Patient at

the Center of IT Strategy" "Toolkit: Framing the Conversation Around Consumer Engagement in Healthcare"

Patient Throughput and Capacity Management Analysis By: Vi Shaffer Definition: Patient

throughput and capacity management (PTCM) systems offer a sophisticated, less-siloed approach to patient �ow, including bed management, case management, transport and staff assignment. PTCM systems integrate and analyze data, providing predictive models, real-time operations dashboards, contextualized alerts and decision support. They anticipate demand and alert to bottlenecks, monitor conformance with pathways, sensor-track resources and patients, and balance resources and demand in real time as circumstances change.

Position and Adoption Speed Justification: Patient �ow and optimizing utilization of facilities, staff

and materials are persistent hospital priorities. Thus, many healthcare delivery organizations (HDOs) have invested in earlier-generation bed management and patient �ow solutions. PTCM is a next-

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generation evolution of these prior solutions. PTCM R&D investments are advancing, and we have moved this category ahead for 2017 based on important advances such as TeleTracking Technologies' introduction of predictive models. This kind of situationally aware and predictively informed decision making will become the new operational management standard. The theoretical value of optimizing patient throughput is well-understood by most healthcare organizations, but the magnitude of any individual hospital's variance from best practice or costsavings opportunity may not be acknowledged. There are numerous barriers to PTCM adoption, but the main two barriers are completion of current R&D plans (including more-predictive algorithms and better dashboard-displayed integration, synthesis and contextualization of data for real-time decision making) and more proof of value (POV), which should arrive over the next two years. Signi�cant investments continue from several vendors, with nurse and staff scheduling, patient classi�cation and acuity, and staf�ng predictive models added through acquisitions or partnerships. Other adoption barriers beyond R&D include the large number of other priorities, HDO executives' willingness to take the early risk, chief nursing of�cers' (CNOs') willingness to lead, and stronger collaboration between nursing and physician leaders of emergency departments and ICUs. Finally, integrated command centers require reskilling, reassignment and centralization of responsibilities like bed assignment. Thus, we continue to predict that it will reach the Plateau of Productivity on the far end of �ve to 10 years. More-precise location sensor technologies for important data on patient and care team location, time and patterns are a valuable enhancement, but we have not made it a requirement for PTCM. User Advice: CNOs, chief medical of�cers (CMOs), CIOs and COOs should consider a fairly early

(stepwise) investment in PTCM. Note that full PTCM capability will not be available in all countries. Top actions for CIOs: ■

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Be sure executives are informed and engaged about a patient throughput roadmap, especially the CNO, who will likely be the chief sponsor. However, consider medical and nursing leaders in the emergency department, ICU and surgery as important collaborators. Build a strong enterprise patient throughput steering committee, and pick a vendor with the architecture and strategic intent to ful�ll the broader patient throughput vision with bold intent, early successes, an approach that looks repeatable and a business that looks viable. Look for the chief nursing informatics of�cer (CNIO) to take on more than electronic health record (EHR) support and to be a key leader in the application of patient throughput technologies. Emphasize change management. The biggest risk will likely be weak leadership and inadequate appreciation of change management, which will result in failing to derive bene�ts, alienating clinical staff and eroding the patient experience.

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Business Impact: PTCM systems aim to deliver higher patient throughput and, therefore, higher

utilization and revenue at lower resource use (cost), by eliminating waste and delay. PTCM is a central part of evolving toward the real-time healthcare system. In the evolving value-based payment and accountable care environment, many leaders have become focused on chronic disease management strategies and avoiding hospital admissions and readmissions. However, there is still critical work to do in making hospitals more ef�cient and maximizing asset and human resource use by optimizing patient throughput and quality. Bundled payment models, such as those from the Centers for Medicare and Medicaid Services in the U.S., make this clear. While vendors do not yet offer the full capabilities covered here, a 2015 Rand study of HealthFirst improvement initiative at Health First (J. Blanchard and R. Rudin's "Improving Hospital Ef�ciency Through Data-Driven Management: A Case Study of Health First, Florida." ) concludes that a currently available system and patient �ow initiative — in conjunction with strong executive involvement and smart change management — "substantially streamlined hospital operations and produced enormous gains in ef�ciency." Note that, while this pro�le focuses on hospital patient throughput, vendors are beginning to expand the use of these solutions and management concepts across the continuum of care, which is added value for revenue, resource utilization, patient satisfaction and value-based care contract accountabilities. Benefit Rating: High

Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: Allscripts; Aptean (Medworxx); BedWatch; Care Logistics; Cerner; Epic; GE

Healthcare; McKesson; TeleTracking Technologies Recommended Reading: "Healthcare Provider CIOs Must Boost CNO Collaboration to Materialize

the Real-Time Health System" "Industry Vision: The Real-Time Health System" "Developing the IT Strategy for the Real-Time Health System Primer for 2017" "The Stages of Maturity in the Journey to the Real-Time Health System"

3DP Presurgery Anatomical Models Analysis By: Vi Shaffer Definition: 3D

printed presurgery anatomical models combines an individual patient's CT scans and/or MRIs with software and 3D printers to produce highly accurate three-dimensional replicas of

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a patient's surgical site. The model can include dimensional details beyond those discernible by surgeons just from the images. Models can be applied to a wide variety of surgical situations. Position and Adoption Speed Justification: Various means of creating presurgery models have

been around for a couple of decades, but 3DP dramatically increases their utility. In the last few years, these models have reached more mainstream awareness and there is growing selective adoption. However, we measure its position against the degree to which use is common within a hospital across multiple surgical specialties and procedures, not just a unique circumstance or two, such as modeling a conjoined twin situation before surgical separation. This category is the earliest, "easiest" and most common use case for medical 3DP hospitals/ ambulatory surgery centers and their physicians. It doesn't have the business, regulatory or adoption complexities of the other medical use cases we're tracking, such as 3DP human tissue, surgical plans/implants or the ultimate aspirations for 3D printed organs for human transplant. The many potential bene�ts listed below make a good case for focusing the 3DP innovation lab concept on this as a starting point, and we project a rapid increase over the next few years. Most of the barriers to model-only uses are around the practical translation of the technology to regular use. Barriers include awareness, joint planning and prioritization procurement and execution for multiple uses, and users across multiple physical hospital and physician locations. Vendor white papers that explain the best use cases are helping with this. How ever, the traction for models is also being fueled by the growing evidence of the value of surgical guides and custom-printed implants. These often include presurgery models as part of the total solution, such as Materialise's U.S. FDA clearance for 3D printed patient-speci�c radius and ulnar osteotomy guides for children. However, as always, HDO executives need to take medical liability into consideration. For photos and video of these models, see for example: ■

SIMPeds 3D Print

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Materialise's 3D anatomical models

■

Stratasys' 3D printed surgical procedure models

■

Formlabs' blog on 3D printed medical models

User Advice: CIOs and CMIOs should play an orchestrating role in de�ning their health systems'

strategies and roadmaps across physician users, use cases, technologies and vendors for medical 3D printing capabilities. The setup of an innovation lab and the introduction of 3DP presurgery anatomical models for multiple categories of surgeons are solid places to start. While large hospitals and integrated delivery systems can create their own capability, smaller hospitals cannot really sustain this yet and will need a service bureau or partnership. Do consult the legal department during planning and procurements to address risks and liabilities. Business Impact: There is growing anecdotal evidence about the many bene�ts of using 3DP in

medicine and the surgery environment:

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Improved diagnostic ability and accuracy

■

Improved surgery planning and identi�cation of obstacles and unique challenges

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Education and training of new physicians, especially around complex/unusual pathological conditions

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New techniques enabled with experimentation

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Improved communication among the multidisciplinary surgical team

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Improved ability to practice presurgery, potentially reducing time in surgery/operating room utilization, and minimizing risk of complications and infections Enhanced the patient experience through better discussion and communication about situations, options and planned procedures

Benefit Rating: Moderate

Market Penetration: 1% to 5% of target audience Maturity: Emerging Sample Vendors: axial3D; Embody Orthopaedic; Formlabs; Materialise; Stratasys Recommended Reading: "Healthcare Provider CIOs Should Quickly Advance to a Bimodal IT

Model" "3D Printing Opportunities and Uses Primer for 2017" "Predicts 2017: 3D Printing Accelerates" "How 3D Printing Will Revolutionize the Wearables Industry" "Hype Cycle for 3D Printing, 2016"

Generation 3 Enterprise EHR Systems (Non-U.S.) Analysis By: Thomas J. Handler, M.D. Definition: Generation

3 enterprise EHRs are sophisticated clinical systems for acute care (medical or surgical wards, emergency, intensive care unit [ICU], surgical theaters, and labor and delivery suites) as well as ambulatory or outpatient clinics. They contain patient-centric, electronically maintained information about an individual's health status and care; focus on tasks and events directly related to patient care; facilitate and enable clinical pathways; enable the practice of evidence-based medicine; and are optimized for use by all clinicians.

Position and Adoption Speed Justification: Generation 3 enterprise electronic health record (EHR)

capabilities, such as clinical decision support, computerized physician order entry and clinical work�ow, are essential components of 21st-century medical practice. These systems have been available for a decade, and adoption in the U.S. has moved beyond the plateau. The positioning of Page 68 of 88



this technology pro�le re�ects the overall non-U.S. market. In many countries, the adoption of EHRs continues to lag behind the U.S. Notable exceptions include Australia, Canada, parts of the Middle East and the Netherlands. There are leading hospitals in several countries that have implemented a Generation 3 EHR, but these are often isolated occurrences. EHR adoption has continued to grow, albeit very slowly, even though governments are encouraging their use and more hospital leadership is recognizing the value of EHRs. In the U.K., a number of trusts have implemented, or are in the process of implementing, Generation 3 EHRs, and similar investigation and procurements are occurring across EMEA. Although there is ample proof that these systems provide value (hence, the maturity rating), some notable implementation challenges have caused a fair amount of skepticism, which is slowing adoption. Barriers include cost, dif�culty in demonstrating an ROI, lack of strong clinical leadership support and the dif�cult task of convincing clinicians that adoption of technology can be a positive change. Implementing an EHR system can take two years or more, and the follow-up activities to optimize the performance and clinical use of the system require many additional years. EHR vendors can be divided into those that have clients in multiple countries across more than one continent, and those that are more geographically limited. User Advice: Generation 3 enterprise EHR systems are no longer "nice to have." As the exponential

growth of medical knowledge has gone beyond the capabilities of human cognition, they have become essential. Healthcare delivery organization (HDO) CIOs must work with senior clinical leaders to help promote and implement these systems and to ensure that their organizations have the appropriate technical infrastructure and policies to support them. To take full advantage of the EHR system, HDO CIOs should approach these systems as part of an ongoing clinical transformation program effort (requiring process re-engineering, ongoing clinical decision support and work�ow evaluations, as well as clinical content life cycle management). If this is to succeed, there must be a strong clinical IT governance structure in place that includes a formal clinical decision support committee. Successful organizations have established clinical informatics roles, including a chief medical informatics of�cer (CMIO), whose function is to ensure that deployment, adoption and content life cycle management work are accomplished. It should be anticipated that the entire effort might double the HDO's IT budget as a percentage of operating expenses because of access, security, infrastructure, data center availability and business continuity investments related to the EHR system. HDOs with a Generation 3 system already in place should focus on clinical optimization activities such as creating order sets, de�ning clinical work�ows, improving clinical decision support and creating an effective knowledge management mechanism to track advances in evidence-based medicine. Those without Generation 3 systems need to begin the process of obtaining them. Business Impact: A Generation 3 EHR system can provide support for a wide variety of clinical

activities that affect all caregivers and patients. It can reduce the rate of medical errors, eliminate unwarranted practice variations, improve operational ef�ciency and compensate for the shortage of skilled healthcare workers by streamlining previously manually intensive work�ows. Although the envisioned bene�ts are many, it takes substantial planning, time, money and effort to obtain the full value of an EHR system.

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Benefit Rating: Transformational

Market Penetration: 5% to 20% of target audience Maturity: Early mainstream Sample Vendors: Allscripts; Cerner; Epic; InterSystems; Meditech Recommended Reading: "Without Adequate Organizational Readiness, Electronic Health Records

Program Success Is Unlikely" "Toolkit: Clinical Readiness Assessment" "The Bene�ts and Realities of the Healthcare Provider CIO's Quest for EHR Value Delivery" "Toolkit: Best-Practice Checklist for EHR Life Cycle Stage 1, Strategize and Plan" "Toolkit: Best-Practice Checklist for EHR Life Cycle Stage 3, Select"

Track-and-Trace and Serialization Analysis By: Andrew Stevens Definition: Track-and-trace

and serialization, currently deployed mainly in life sciences companies, is an evolving architecture of integrated and point solutions that enhance product quality, compliance and safety predominantly from primary manufacturing to the �nal use of the product with the patient/consumer. At their core solutions focus on data capture, the generation of serial numbers (bar codes) and the exchange of key datasets across interoperable networks of supply chain stakeholders.

Position and Adoption Speed Justification: Manufacturers, supply chain partners and healthcare

providers must develop more collaborative, holistic and data-driven competencies as costs and risks grow due to adherence to global serialization compliance mandates. Broader stakeholder interconnectivity requires new information standa rds and greater levels of collaboration across supply chain partner networks and customers. Track-and-trace requires serial number generation, data governance and data capture capabilities to be integrated and communicating at an enterprise, data management and physical transactional level. It is anticipated mature solutions will be at least �ve years out, and mainstream adoption much longer. Manufacturers, supply chain trading partners, healthcare providers and electronically connected patients will each play a role in maintaining a products chain of custody. Solution providers are actively working to identify frameworks to develop the �nal models for fully integrated, end-to-end electronic tracking across all impacted supply chain stakeholders. As a result, emerging track-andtrace solutions will need to deploy integrated planning platforms that can leverage core standards and modes of operation while executing targeted, differentiated solutions focused on speci�c compliance criteria. This means it will take longer to reach the plateau.

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Solutions are predominantly specialized to country legislation presently, with many providers beginning to consolidate expertise through partnering to broaden the business scope. Standards organizations are assisting to lay core foundational building blocks for serial numbers and electronic data exchange models. Hurdles of nonaligned legislative mandates still pose challenges such as interoperability, data governance, aggregation, data ownership and repository management. User Advice: Requirements for track-and-trace and serialization solutions to work across multiple

enterprises will require companies to revisit their end-to-end supply chain IT system architecture and interconnectivity across trading partners, healthcare networks, patients and consumers. Supplier networks may also be in scope. Companies should revisit their end-to-end IT system a rchitecture and interconnectivity across trading partner networks in the following areas: ■

■

■

■

■

■

■

■

Enterprise level for global integration of common datasets, emerging standards and links to central repositories Across interoperable communication networks, including end-consumer connectivity Data management solutions for governance, aggregation, randomization, data storage and encryption Operational-line level for material �ow, automation, scanning, routing and data capture through vision systems and camera technology Final mile logistics and customer ful�lment aligned to speci�c regulatory or trade management mandates and speci�c logistics or network communications infrastructures Potential integration with systems and solutions such as warehouse management systems (WMSs), quality management systems (QMSs), laboratory information ma nagement systems (LIMSs), cold chain, manufacturing execution systems (MESs), ERP automation and documentation systems The data con�guration, security and capacity capabilities of the bar codes such as 2D data matrix and RFID, and the value realization opportunities for that data Strategic collaborative initiatives and deployments focused on risk mitigation especially for counterfeit or falsi�ed products

Business Impact: Solidifying protocols and systems for track-and-trace and serialization will aid in

decreasing overall enterprise and business risks,. Other areas of impact include: ■

Risk: High — Failure to take action can result in detrimental brand performance.

■

Strategic policy change: Medium — It is variable, based on localized compliance and

regulatory considerations, and other externalities. ■

Organization change: Medium — A focus on end-to-end capabilities will change collaborative

models across an increasing number of stakeholders.

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Culture change: High — New thinking and approaches are additive to existing customer-

centricity focuses. Change management for technical and regulatory serialization compliance across partners will become critical. Business leaders need to scope serialization potential beyond compliance based activity. ■

Process change: High — It requires rede�ning business process and information �ows inside

the organization, and with trading partners, healthcare providers and customers. ■

Competitive value: Medium — Compliance readiness, where applicable will take priority. Competitive value will be realized through increased operational and transactional ef�ciencies

as well as interoperable communication of existing and new types of data across stakeholders. Benefit Rating: High

Market Penetration: 5% to 20% of target audience Maturity: Emerging Sample Vendors: Adents; Antares Vision; Axway; Frequentz; Kezzler; Optel; rfXcel; Systech;

TraceLink

Climbing the Slope Accountable Care Organization Analysis By: Laura Craft; Vi Shaffer Definition: Accountable

care organization (ACO) is an umbrella term for tracking those healthcare delivery organizations (HDOs) making a major switch in how they contract with public or private payers in a shift to a value-based model. In this model, providers assume some portion of the risk for cost and quality for a panel of bene�ciaries through a variety of value-based payment models over a speci�ed contract period. ACOs include primary care services.

Position and Adoption Speed Justification: While a wide array of ACO-like models are appearing

in the U.S. and other countries, the position of the ACO model on this Hype Cycle is calculated using the percent of U.S. Medicare-insured individuals covered under one of several formal ACO models managed by the federal government's Centers for Medicare & Medicaid Services (CMS). CMS has been aggressively increasing adoption of value-based models. In January 2017, CMS announced that there are over 12.3 million Medicare and Medicaid bene�ciaries (up from 8.9 million in 2016) served in 562 ACOs across the Shared Savings Program, the Next Generation Accountable Care Organization (ACO) Model and the Comprehensive End-Stage Renal Disease Care Model (CEC) across 50 states, the District of Columbia, and Puerto Rico. In 2016, CMS introduced the "Next Generation ACO Model" for experienced ACOs. It assumes higher levels of �nancial risk in return for higher �nancial reward. The goal of this model is to test the impact on health outcomes and the cost of strong �nancial incentives combined with tools for better patient engagement and care management. In 2016, 21 ACOs participated; in 2017, 121 ACOs are in a risk-bearing track. This signals continued strong growth and adoption of ACO payme nt models. CMS goals are to have Page 72 of 88



50% of Medicare payments fall under one of the models by 2018, and private insurers are following suit. For example, Aetna has targeted 75% of their spend to be in a value-based contract by 2020. The 2016 U.S. election did bring a political shift to the White House and has left the future of healthcare reform in an uncertain place. There will continue to be periods of stalled progress, adjustment and realignment as this unfolds; however, as noted last year, we believe this train has left the station and project that the ACO model will enter the Plateau of Productivity at 30% adoption in less than �ve years. We have nudged its positioning from 30% post-trough to 40%, and modi�ed the maturity from adolescent to early mainstream as we see more operational maturity and the market for ACO-enabling tools stabilizing. User Advice: Gartner has been reporting for several years that the shift to value-based care is the

biggest shift in hospital operations in the past 60 years; a shift that cannot be achieved without deliberate recon�guration of existing organizing principles, partnerships and contract negotiations strategies, and an investment in new capabilities. Unfortunately, the vendor landscape for emerging solutions has been a confusing mix of product offerings from vendors jockeying for position, rede�ning their solution scopes, upgrading their platforms and often merging or getting bought. This has resulted in cautious procurement. However, CIOs who have not already invested in a population health management solution or put it on your roadmap for 2017 are placing your ACO at risk of not having the right tools to be successful, and should consider: ■

The market for population health solutions has matured: A refresh of the vendor space is

critical because in 2016 we saw a notable improvement in the leading megasuite vendors' population health capabilities (Epic, Cerner) as well as credible progress with leading standalone solutions. Buyers now have a richer and more mature market from which to select. Early adopters now provide a rich base of references, success stories and lessons learned. ■

Finance needs new tools to manage the contracts: Managing upside and downside risk and

having prospective insight into contract compliance are entirely different than managing fee-forservice arrangements. Value-based performance management tools are available on the market that have speci�c capabilities to help, from contract modeling and forecasting, to contract and network performance management. Sample vendors CIOs should bring to the attention of the CFO include Cedar Gate Technologies and WhiteCloud. Business Impact: Revenue from accountable care models is expected to make up more than 25% of enterprise revenue within �ve years. This shift is transformational and requires substantial IT

support. Execution will be the key to success. Delivering care under these new paradigms requires fundamental changes to processes that have been entrenched in organizations' operations and cultures for years. The types of questions being asked are new, the types of decisions that need to be made are different, and the way technology is being used and interacted with is changing. It's a disruptive change. It will take substantial resources, leadership and commitment. Benefit Rating: Transformational

Market Penetration: 5% to 20% of target audience Maturity: Early mainstream

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Sample Vendors: athenahealth; Cerner; Change Healthcare; Epic; Evolent Health; Health Catalyst;

IBM Watson Health; Optum; Philips (Wellcentive); xG Health Solutions Recommended Reading: "Healthcare Provider CIOs Need to Stay on Course and Procure a

Population Health Solution" "Market Guide for Healthcare Provider Population Health Management Platforms" "Market Guide for Healthcare Provider Value-Based Performance Management Analytics"

Computer-Assisted Coding (Hospital) Analysis By: Barb Mann; Vi Shaffer Definition: Computer-assisted

coding (CAC) is an application combining natural-language processing with machine learning to provide or suggest medical codes based on reading and interpreting clinical documentation for coders to augment and validate, or, potentially over time, do so without coder review, in some cases.

Position and Adoption Speed Justification: CAC is tracked here based on U.S. hospital enterprise

adoption. It experienced a surge in buying between 2013 and 2015 spurred by the mandated conversion to ICD-10, followed by a slowdown. Many HDOs are replacing legacy patient �nancial system. CAC will likely come with or after that PFS project. There is a steady �ow of new adopters and we project this will become a standard component of the coding process. Lack of vendor standard methodologies and best practices caused numerous struggles with early implementations of this application, but that is stabilizing. There is still debate about the bene�ts and longer-than-promised payback periods to achieve ROI, yet more early adopters have quanti�ed decent ROI in terms of coder productivity and increased revenue. An optimization process is required to achieve these quanti�ed results. The role and value of CAC and computer-assisted clinical documentation improvement (CACDI) are better understood (CACDI is covered separately in this Hype Cycle). We expect new site implementations by multiple vendors to move ahead on a more traditional adoption pace. This will move CAC toward strong growth and mainstream adoption. We continue to project that the Plateau of Productivity — the point at which CAC becomes a standard part of coding practice for hospitals — will be achieved in two to �ve years. User Advice: CAC is one of the inevitable bene�ts of combining advances in clinical data

digitization, natural-language processing, data analytics and machine learning, because coding is a persistent, costly need and challenge. CIOs, CMIOs and HIM leaders should plan for its eventual use in those countries where it will be relevant and available. They should collaborate to formulate a comprehensive, sustained data/documentation/coding quality management program leveraging CAC and CACDI: ■

Evaluate CAC application accuracy and �t with your established work�ows. Accuracy rates impact productivity, and are measured by comparing codes assigned by the application to those �nalized and coded by a competent coder.

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Con�rm candidate vendors have demonstrated a strong relationship with your core EHR and

RCM vendor(s) to avoid unanticipated problems during system changes. ■

■

■

■

■

Ensure performance accountability by con�rming with the RCM leader baseline payment and cost-to-collect metrics and CAC expectations. Use CAC to improve the use of coding resources, to increase productivity, and to reduce outsourcing expenses where possible. As CAC enables coders to evolve as analysts and apply critical thinking skills, HIM will need to continue to assess role, skills and development requirements. Combine CAC with related CDI programs and tools, such as CACDI and RTPDI to improve clinical documentation at the clinician source. Assess CAC for outpatient use as similar types of bene�ts are demonstrated. Outside the U.S., recognize that creation of accurate CAC requires assembly of a large localized database of records, claims and payments to achieve accurate output. CIOs and CFOs should assess emerging capabilities.

Business Impact: CAC is primarily used by coders, retrospective to the patient stay, to allow for quick scanning of volumes of documentation, highlighting or linking documentation to speci�c

medical codes for easy validation. CAC applications are often paired with detailed code assignment reporting and coder production in hospital inpa tient and outpatient settings. Bene�ts include: ■

■

■

■

■

Improved evidence-based coding supports increased, and appropriate capture of comorbidities, complications and severity. Productivity improvements based on vendor case studies are variable; inpatient coding shows increases of 10% to 20%. Coders review increased documentation and clinical query response times prolong the coding cycle. Improved productivity and improved A/R cycle time as AI and NLP evolve coding automation of targeted, less-complex medical records. Increased revenue and revenue integrity and decreased external auditor reliance costs from clinical documentation data completeness, machine-learned inferences, and traceability. Improved data quality advances enterprise and quality analytics for value-based care business models' monitoring.


Market Penetration: 20% to 50% of target audience Maturity: Early mainstream Sample Vendors: 3M (Health Information Systems); Arti�cial Medical Intelligence; Dolbey Systems;

ezDI; nThrive (Precyse); Nuance; Optum; Platocode

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Recommended Reading: "Market Guide for Healthcare Provider Computer-Assisted Coding,

Computer-Assisted Clinical Documentation Improvement, and Real-Time Physician Documentation Improvement" "Modernizing Healthcare Administration Systems Primer for 2017"

Entering the Plateau Advanced Clinical Research Information Systems Analysis By: Vi Shaffer; Laura Craft Definition: An

advanced clinical research information system (ACRIS) is a complex constellation of capabilities that can rapidly assemble data assets for clinical research questions. It also provides data mining and research process support to meet the needs of clinical and translational research, and related biostatistics and biocomputation. It includes electronic health record (EHR) access, open-source components and an approach for big data needs.

Position and Adoption Speed Justification: ACRIS is relevant for those HDOs where medical R&D

and clinical research are businesses. We measure progress against a much smaller denominator of institutions, such as the 60 U.S. National Institutes of Health's Clinical and Translational Science Award (CTSA) grant sites. Other countries have similar small numbers of research institutions. Breakthrough information and tools are in rapid demand for sophisticated research users. CIOs and chief medical informatics of�cers (CMIOs) are becoming more engaged with and savvier about these requirements. Gartner continues to hear from those institutions around the world that are making the case. CIOs are also collaborating with research leaders to make the additional investments for this "next generation" approach to their clinical research businesses. ACRIS includes tools that enable: ■

Clinical trial data capture and analysis

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Access to and mining of EHRs (including unstructured data via NLP) and other sources

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Automatic correlation of data with medical knowledge

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Access to external data and open-source tools translating between ACRIS data models and vocabularies, tools like SAS and R for statistical computing and graphics, and use of i2b2 and tranSMART (Note that, in May 2017, the two foundations completed a merger into the i2b2 tranSMART Foundation to manage an open-source/open-data community around the i2b2, tranSMART and OpenBEL translational research platforms.)

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Patient data deidenti�cation

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Facilitation of researcher work�ows (scienti�c method, grant prep/management)

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User Advice: An ACRIS is not a single packaged solution an enterprise can just go buy. However,

select vendors are actively positioning their portfolio to be central or value-added for an enterprise's research ecosystem. Researchers trying to eliminate disease and change the world will always impatiently reinvent their demands for data and tools. The critical need for CIOs and CMIOs is to drive a closer relationship with research leaders. In turn, clinical research leaders need to learn and comply with changing regulations and breach reporting, become part of IT governance, and participate in the IT and information prioritization process. Speci�cally, CIOs and CMIOs of research-oriented healthcare delivery organizations (HDOs)/

academic medical centers (AMCs) must: ■

■

■

■

■

■

Collaborate with research leaders on requirements. Increase select IT staff knowledge of the language, tools (including open source), business and processes (including institutional review board [IRB] review, clinical trial management and grant accounting). Consider having a medical informatics specialist in clinical research. Be prepared to help researchers advance their competitiveness by using EHR data ("real-world evidence" to them) — including both structured and unstructured data — while coaching on necessary security and privacy compliance. Recognize that the "hot" �elds of clinical research advance regularly, driving new needs. For example, some leading genomics researchers are transferring their focus to the microbiome, according to Cleveland Clinic's report on the Top 10 Medical Innovations for 2017. Help link enterprise ACRIS decisions and investments to community marketing. Also, ask how those platforms can enhance collaboration between and among institutions and life science partners (integrated research networks). Leverage IT's relationships with major IT vendors and knowledge of advancing technologies to advise clinical researchers on partnerships and procurements. Remember that the research community embraces open-source approaches.

Pharmaceutical CIOs looking for partnering and sponsoring activities should address ACRIS capabilities with partners as part of their evaluations of potential partnerships, especially for drug development, personalized medicine and biomarker research programs. Business Impact: Clinical research is a big business and an important part of the brand for many

prestigious health systems. There is also substantial competitive pressure. Information and technology breakthroughs are conspiring to create a revolution in c linical research that will ultimately advance personalized medicine as the next medical model. For example, Mayo Clinic in 2016 received a $142 million NIH award to build a national biobank under President Obama's Precision Medicine Initiative. AMCs around the world that do not invest in an ACRIS will have increased dif�culty competing for research contracts and grants, and will face diminished stature and global funding within �ve years. In the U.S., the CTSA program and the National Cancer Institute (NCI) encourage more collaborative

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studies and knowledge sharing among funded research sites. This is a fundamental, challenging, and incredibly exciting change in the expectations set for both clinical research and IT leadership. Benefit Rating: Transformational

Market Penetration: 20% to 50% of target audience Maturity: Adolescent Sample Vendors: Apache Software Foundation; Deloitte (ConvergeHEALTH); i2b2 tranSMART

Foundation; IBM Watson Health; IDBS; Linguamatics; Microsoft (Revolution Analytics); Oracle; SAS; Teradata Recommended Reading: "Magnify the Value of Your Healthcare Delivery Organization's EDW by

Including These Top 10 Data Sources" "Arti�cial Intelligence Primer for 2017"

"Magic Quadrant for Data Science Platforms"

Healthcare Real-Time Location System Analysis By: Gregg Pessin Definition: Healthcare

real-time location systems (hRTLSs) are used to track the location of identi�ed assets or people within the con�nes of the healthcare provider facility. Location information is sent in real time from wireless tags that are either worn by people inside facility or attached to an asset. The wireless communication is typically over radio frequency (RF), but can also be accomplished with infrared or ultrasound.

Position and Adoption Speed Justification: hRTLS is used to track mobile medical assets such as

intravenous infusion pumps, wheelchairs, pulse oximeters, specialized surgery tables and equipment, and computers on wheels. It is also used to track nurses, patients and ancillary staff. hRTLS has increasingly become a routine component of everyday cost, patient care �ow and quality initiatives. Healthcare providers globally are adopting the technology with most developed markets having at least one vendor to consider. Overall, vendors continue to vie for market share and leadership and push for widespread adoption, while expanding their footprints of applications and supported sensor technologies. This technology pro�le is the next version of the Wireless Healthcare Asset Management pro�le and replaces it on this Hype Cycle. With the inclusion of people tracking, newer sensing technologies such as Bluetooth and industry acceptance of RTLS as the term describing these solutions, this new pro�le is entering the Hype Cycle as preplateau. As this technology approaches mainstream adoption, it will most likely be absorbed into the Internet of Health Things (IoHT) set of standard capabilities. hRTLS is well ahead of IoHT on the Hype Cycle and, when combined, will move IoHT forward on the cycle.

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User Advice: hRTLS is foundational to the real-time health system operational paradigm: ■

hRTLS is foundational to the real-time health system operational paradigm:

■

Larger hospitals and multihospital systems should be leveraging this technology now.

■

■

Select from vendors that have extended their value from location detection to equipment inventory optimization, maintenance management and regulatory compliance. Total cost of ownership (TCO) evaluation for hRTLS should include the appropriate allocation of shared and incremental (if needed) infrastructure (such as Wi-Fi, LAN and so on) that is needed to ensure the granularity of the location to meet the desired business outcome. The TCO also includes tags, batteries, sensors, receivers, software/support, staff training and support, and managerial oversight to ensure the full potential impact of hRTLS is realized.

Business Impact: hRTLS improves timely clinician access to mobile equipment, improving its

utilization, maintenance and management. It can also be used to aid in the management of IT assets and other �xed assets. It has signi�cant bene�ts of increasing productivity and reducing costs for nursing staff and clinical engineering departments with the ability to identify the location in real time of a nurse or piece of equipment. Properly implemented hRTLS can reduce the organization's total cost (including new purchase and rental costs) for equipment such as infusion pumps and wheelchairs (two of the most commonly tracked assets), and other biomedical and IT equipment. Benefit Rating: Moderate

Market Penetration: 20% to 50% of target audience Maturity: Early mainstream Sample Vendors: AiRISTA Flow; Halma (CenTrak); Intelligent InSites; Sonitor Technologies; Stanley

Healthcare (AeroScout); TeleTracking Technologies; Versus Technology; Vizbee Recommended Reading: "Developing the IT Strategy for the Real-Time Health System Primer for

2017" "Industry Vision: How Technology and Service Providers Can Enable the Digital Hospital" "Essential Best Practices for Tracking Critical Assets Using RFID"

Generation 3 Enterprise Patient Financial Systems (U.S.) Analysis By: Vi Shaffer; Barb Mann Definition: Generation

3 (Gen 3) enterprise patient �nancial systems (PFSs) provide core revenue cycle work�ow functionality for billing, claims processing, collections and reconciliation. These systems are categorized as Gen 3 because most are tied to a vendor's electronic health record (EHR) Gen 3 architecture, and are an integrated part of a megasuite. Patient access (including

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scheduling and registration), HIM, charge capture/charge data master and contracts modules are often deployed in conjunction with the PFS. Position and Adoption Speed Justification: These systems displace both, earlier patient

accounting systems and physician practice management systems for those enterprises wishing to have a consolidated approach to revenue and business of�ce oversight. They are now wellpenetrated in the U.S. — especially noting the large number of live Epic sites and Cerner's more recent but substantial growth. These two vendors' combined market penetration represents a dramatic change in market share leadership from just a decade ago. We anticipate moving this category off the Hype Cycle for the U.S. in 2018. We leave it on this year to remind executives that while the market is well-penetrated, there are still vendors with lessmature offerings and recent examples of poor implementations — even using experienced vendors. These have put both cash �ow and responsible executives' jobs at risk. Many medium/large hospitals and integrated health systems delayed replacement to Gen 3 until after the ICD-10 conversion. Or perhaps their preferred megasuite vendor's PFS offering was immature and legacy systems were still meeting their needs. Most of these are now moving ahead. Purchasing of PFS has for some time been often bundled into comprehensive megasuite contracts and it's becoming more common to see a "big bang" — combined implementation of EHR, PFS and patient scheduling (on separate timelines for hospitals and practices). Worries about eroding support or ultimate sunsetting of legacy products, supporting scalable business of�ce consolidation for economies of scale, and delivering a more modern, patient-friendly and integrated �nancial experience are motivating factors for transition. Factors slowing adoption include high risk of a poor transition, uncertainty about enterprise EHR direction, legacy-vendor loyalty or a decision-making "limbo" caused by possible sale, merger or acquisition activity. In some cases, an acquired entity may have a better PFS or megasuite solution than the acquirer. There are a limited number of vendors offering scalable PFSs, reduced by Cerner's 2015 acquisition of Siemens' health solution products (though Cerne r still supports both Invision and Soarian Financials). Note: Position is tracked for the U.S. Many other countries have similar needs; we do not include non-U.S. vendors here; availability/maturity is variable pe r country. User Advice: Replacing or deploying a patient �nancial system is one of the largest, most complex

and highest-risk projects that health systems can undertake, right up there with the EHR, but even more so because it is usually a replacement; everything has to work day one. Legacy systems have lasted so long that many CFOs, CIOs and revenue cycle VPs have never managed a new system to go-live. CIOs need to be aware that implementations targeting operating model change will trigger all sorts of culture, control and legacy-vendor loyalty obstacles. CIOs: ■

Make sure if you are bundling PFS into a megasuite deal that all aspects of the revenue cycle are given proper planning and project attention. EHR and clinician interests can dominate these efforts to the detriment of RCM.

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■

■

■

■

■

Be sure a strong HIM director is in place and engaged from the outset as a critical contributor to success of both PFSs. Compare current revenue cycle management applications/bolt-ons: Which in-place would be still required, which need to be replaced, and which can be jettisoned with the new system. Include encoders and computer-assisted coding in that assessment. Invest the resources to fully document third-party system integration points a s there may be hundreds of distinct integration needs that may not be known, tested and work after the go-live date. Establish proper program governance, accountabilities and resource planning across clinical and revenue management teams. Change control and data governance may have been administered separately for clinical systems and PFS or for different facilities in the past. An integrated system requires enterprise standards and uni�ed oversight. Identify enterprise revenue cycle metrics, measure baseline, set expectations and measure after go-live. Write metrics into the vendor contract and performance evaluation. If RCM metrics are poor, consider getting some improvement with the help of an expert consultant before taking on system conversion. Don't forget to harvest later waves of value after go-live by leveraging analytics and vendor enhancements.

Business Impact: Investing in a Generation 3 PFS along with revenue cycle best practices is essential to the �nancial health of the healthcare delivery organization. The system helps realize economies of scale by enabling consolidating business of�ce operations. A PFS should allow for

maximizing revenue-capturing opportunities with minimum revenue leakage, reduced cost to collect, reduced cycle time from scheduling to actual payment, and related reporting. The system is essential, but the metrics gains from a Gen 3 versus Gen 2 system are fairly modest for a well-managed HDO, and highly dependent on good end-to-end revenue megaprocess management across tools, tasks and departments. Enterprise business of�ce and other operations' consolidation, application of computer-assisted coding and other innovations will yield higher bene�t, but add complexity to the PFS IT plan. Benefit Rating: Moderate

Market Penetration: 20% to 50% of target audience Maturity: Mature mainstream Sample Vendors: athenahealth; Allscripts; Cerner; CPSI (Healthland); Epic; GE Healthcare; Harris

Healthcare (QuadraMed); McKesson; Meditech Recommended Reading: "Healthcare Provider CIOs Should Avoid the Four Ma jor Risks of

Megasuite Vendor 'Lazy Loyalty'"

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"Guidelines to Manage High-Risk Healthcare Enterprise-Level Revenue Cycle Management System Implementations" "No Change, No Gain — The Role of the Program Change Manager in Directive Programs"

Patient Portals Analysis By: Thomas J. Handler, M.D. Definition: Patient

portals enable a secure digital patient-provider relationship and access to clinical, �nancial and administrative functionality, educational information, and personal health maintenance tools. Patient portals are provided by a healthcare organization — which could be a primary physician — and can be stand-alone or tethered to (integrated with) electronic health record (EHR) and healthcare megasuite systems.

Position and Adoption Speed Justification: There are two forms of patient portals — tethered to

an EHR/megasuite (ambulatory as well as combined ambulatory and acute care) and stand-alone. EHR-tethered portals typically display the patient's encounter summary, problem list, medication list, allergies, recent vital signs and lab results, and they allow patients to schedule or request appointments and prescription re�lls, conduct an e-visit, and ask questions of providers or medical assistants. Some organizations leverage the portal to enable collection of patient-generated information. Tethering to an EHR/megasuite facilitates convenient work�ow for the provider. Some providers have further enabled their EHR-tethered patient portals with access to full provider visit notes. Although functionally rich, tethered portals have a disadvantage: Patients who see multiple physicians will have multiple portals if all clinicians aren't using the same EHR. Stand-alone portals in the U.S. are typically used with health information exchange (HIE) organizations as a front end, for both patients and clinicians to see data collected from multiple healthcare system and physician of�ce EHRs. Access to multiple EHRs is an advantage for the patients, but they tend to have less interactive functionality with providers, such as for appointment requests, medication re�lls, and messaging with providers and medical assistants. The absence of tethering to any single EHR leads to less convenient work�ow for providers, and semantic interoperability remains a barrier for the stand-alone solutions. Stand-alone portals outside the U.S. are often regional or national systems that provide patients with access to a summary of their medical data and recent interactions, and enable patients to renew prescriptions, book appointments and have e-visits. Examples of countries and regions that have pioneered patient portals include Denmark, Estonia, Sweden, Andalusia (Spain), Lombardy (Italy), the U.K., New Zealand and Australia. Patient portals are very close to reaching the Plateau of Productivity, based on adoption by more than 50% of U.S. healthcare delivery organizations (HDOs) and growth in the global presence of portals. Over time, we expect the functionality provided in patient portals will be delivered �rst through consumer health portals, again tied to a speci�c organization, and ultimately through consumer health hubs that may exist across organizational boundaries.

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User Advice: CIOs of HDOs with multiple EHRs or whose EHRs lack adequate portal functionality

should consider using portal platforms to construct web-based composite applications and then linking them to clinical applications (using service-oriented architecture [SOA] techniques — APIs and web services — to reuse application and system logic and data). Patients increasingly expect access to their electronic health records. Ideally, patient portals are connected with other patient-facing resources on the HDO website, such as provider referral assistance and health education materials. HDO CIOs should work with clinical leaders to develop longer-term plans to extend interactive capabilities, including patient-provider communication, e -visits and video visits. However, expectations need to be correctly set. Stand-alone portals tend to have less clinical information and functionality than tethered portals do. Furthermore, even appropriately interfaced portals require clinicians to step outside their regular work�ows and use a "different" system, and clinical data may not be available for automated clinical decision support or care management functionality. The patient portal strategy should also be aligned with self-service kiosk and mobile application strategies. HDO CIOs should track the rise of consumer health portals and the functionality they can deliver, as these may ultimately replace the functionality of patient portals. Business Impact: Patient portals provide patients access to test results and can increase patient satisfaction and improve healthcare system and physician of�ce brand loyalty. As more robust

interactive functionality is built that enables collection of patient-generated information, automation of requests and facilitation of minor illness management, HDOs can expect improvements in clinician productivity. In addition, organizations can improve the quality of care delivered by using the patient portal to improve communication between patients and providers. Benefit Rating: Moderate

Market Penetration: More than 50% of target audience Maturity: Mature mainstream Sample Vendors: Allscripts; Cerner; Epic; InterSystems; Medicity; Meditech; Orion Health Recommended Reading: "Leading HDOs Focus on 12 Categories of Patient Engagement

Technology" "Business Drivers of Technology Decisions for Healthcare Providers, 2017"

Appendixes

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Figure 3. Hype Cycle for Healthcare Providers, 2016 3D Bioprinted Human Tissue Eldercare-Assistive Robots Healthcare CRM/Contact Center Systems Population Health Management Platforms

expectations Real-Time Hospital Operations Dashboards Postmodern Healthcare ERP Logical Data Warehouse Genomics Medicine 3DP-Aided Hip/Knee Implants Smart Machines — Healthcare Sages Unique Device Identification (UDI) Healthcare Consumer Persuasion Analytics Real-Time Healthcare Costing Critical Condition Surveillance Systems 3D Bioprinted Organ Transplants

Natural-Language Processing (Healthcare Delivery Organization) PHI Consent Management

Nanomedicine

Generation 3 Enterprise Patient Financial Systems (U.S.)

OpenNotes Provider Population Health Analytics Care Coordination and Management Applications Patient Throughput and Capacity Management

Customer Engagement Hub Value-Based Performance Management Analytics Patient Decision Aids

Patient Portals Advanced Clinical Research Information Systems Computer-Assisted Coding (Hospital) Integrated Clinical/Business Enterprise Data Warehouse

Healthcare Algorithmic Marketplace Generation 3 EHR Systems (Non-U.S.)

Accountable Care Organization LCST App Platforms Computer-Assisted Clinical Documentation Improvement (Hospital)

Precision Medicine Healthcare Distributed Ledger and Blockchain

Track-and-Trace and Serialization As of July 2016

Innovation Trigger

Peak of Inflated Expectations

Trough of Disillusionment

Slope of Enlightenment

Plateau of Productivity

time Years to mainstream adoption:

less than 2 years

2 to 5 years

5 to 10 years

more than 10 years

obsolete before plateau


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Gartner, Inc. | G00314257



Hype Cycle Phases, Bene�t Ratings and Maturity Levels Table 1. Hype Cycle Phases Phase

Definition

A breakthrough, public demonstration, product launch or other event generates signi�cant press and industry interest.

Innovation Trigger

Peak of Inflated Expectations

During this phase of overenthusiasm and unrealistic projections, a �urry of well-publicized activity by technology leaders results in some successes, but more failures, as the technology is pushed to its limits. The only enterprises making money are conference organizers and magazine publishers.

Trough of Disillusionment

Because the technology does not live up to its overin�ated expectations, it rapidly becomes unfashionable. Media interest wanes, except for a few cautionary tales.

Slope of Enlightenment

Focused experimentation and solid hard work by an increasingly diverse range of organizations lead to a true understanding of the technology's applicability, risks and bene�ts. Commercial off-the-shelf methodologies and tools ease the development process.

Plateau of Productivity

The real-world bene�ts of the technology are demonstrated and accepted. Tools and methodologies are increasingly stable as they enter their second and third generations. Growing numbers of organizations feel comfortable with the reduced level of risk; the rapid growth phase of adoption begins. Approximately 20% of the technology's target audience has adopted or is adopting the technology as it enters this phase.

Years to Mainstream Adoption

The time required for the technology to reach the Plateau of Productivity.


Table 2. Bene�t Ratings Benefit Rating Definition

Transformational

Enables new ways of doing business across industries that will result in major shifts in industry dynamics

High

Enables new ways of performing horizontal or vertical processes that will result in signi�cantly increased revenue or cost savings for an enterprise

Moderate

Provides incremental improvements to established processes that will result in increased revenue or cost savings for an enterprise

Low

Slightly improves processes (for example, improved user experience) that will be dif�cult to translate into increased revenue or cost savings


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Table 3. Maturity Levels Maturity Level

Status

Embryonic

■

In labs

■

None

Emerging

■

Commercialization by vendors

■

First generation

■

Pilots and deployments by industry leaders

■

High price

■

Much customization

■

Second generation

■

Less customization

■

Third generation

■

More out of box

■

Methodologies

■

Several dominant vendors

■

Maintenance revenue focus

■

Used/resale market only

Adolescent

■

Early mainstream

■

Uptake beyond early adopters

■

Proven technology

■

Mature mainstream

Legacy

Obsolete

Maturing technology capabilities and process understanding

Vendors, technology and adoption rapidly evolving

■

Robust technology

■

Not much evolution in vendors or technology

■

Not appropriate for new developments

■

Cost of migration constrains replacement

■

Rarely used

Products/Vendors


Gartner Recommended Reading Some documents may not be available as part of your current Gartner subscription.

"Understanding Gartner's Hype Cycles" "Four Healthcare Hype Cycle Megatrends Reveal Pivotal IT Role in a Seismic Industry Transformation" "Use These Guidelines to Align the CMIO's Role With Health System Imperatives" "Business Drivers of Technology Decisions for Healthcare Providers, 2017" "2016 Top Actions for Healthcare Provider CIOs: Streamline the IT Portfolio and Get Focused on the Patient and Outcomes" "Predictive Algorithms That Healthcare Delivery Organizations Are Using to Improve Outcomes"

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"Healthcare Provider CIOs Must Boost CNO Collaboration to Materialize the Real-Time Health System" "Healthcare Provider CIOs Should Avoid the Four Major R isks of Megasuite Vendor 'Lazy Loyalty'" "Healthcare Provider CIOs Must Drive Value-Based Performance Mana gement Into the HDO Analytics Portfolio" "Predicts 2017: 3D Printing Accelerates" More on This Topic This is part of an in-depth collection of research. See the collection: ■

2017 Hype Cycles Highlight Enterprise and Ecosystem Digital Disruptions: A Gartner Trend Insight Report

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Hype Cycle for Healthcare Pr 314257

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