This workshop brought together leading researchers and policymakers to generate a white paper on the successes of Smart and Pervasive Health research activities, the evolution of relevant computing capabilities (sensing, advanced analytics, networks, data infrastructure, advanced imaging, cyber-physical systems in health, privacy and security of health data and systems), the application of these technical innovations across a range of wellness and healthcare needs (aging, disabilities, chronic disease management and prevention, prosthetics and rehabilitation) and emerging paradigms (e.g., precision medicine and personalized treatment).

The workshop was held in Washington, DC on Monday and Tuesday, December 5th and December 6th. Participants represented academia, industry, and government. Over 1.5 days, participants answered key questions such as what are the indicators and examples of success in Smart and Pervasive Health? What are the key scientific challenges? How can these challenges be addressed in a 3, 5, and 10 year time frame? The results will inform both policymakers and the research community on opportunities for research and development in Smart and Pervasive Health going forward.

Workshop discussions explored the following themes:

Supporting Healthy Communities through Physiological, Behavioral, and Environmental Sensing

Always available remote physiologic and behavioral monitoring brings the transformational possibility of health care that is bound by neither space nor time. It enables observations of dynamic changes in each individual’s health state as well as key physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk, anytime and anywhere that have not been possible in the past. The goal of this panel is to examine where we are as a field with physiological and behavior sensing capabilities, to identify opportunities for platforms that efficiently fuse multi-modal sensing, and how those advances impact the future of personalized and context-adaptive diagnosis, interventions, and treatment, and population health.

Closing the Loop in Real-Time Smart Health Systems  

Advances in sensing and data capture allows a coupling between sensing, actuation and control to effectively close the loop in real-time and invoke the need for various safety critical CPS technologies. In addition, these closed- or human-in-the-loop solutions will require real-time modeling to realize individualized and personalized interaction and interventions. These feedback loops go well beyond classic supervisory control and can build upon past work that provided feedback to people, devices and systems. This new generation of data and sensing in closed loop systems has the potential for a revolution in smart health.

Contextually-Aware Analysis, Decision-Support, and Informed Response

Rapidly expanding data acquisition by mobile phones, wearables, intelligent vehicles, smart homes and offices is leading to increasing availability of data and information in various forms, including text, images, audio, video, motion, gesture, physiology, and geoexposure, among several others. Diversity, density, and depth of these data necessitate new algorithms and techniques for data analytics and visualization, computational intelligence, machine perception, and human-centric computing that can provide personalized diagnosis and treatment plans, offer better contextual information to healthcare providers and physicians to make more informed decision and more rapid response, and enable the discovery of new knowledge about health at system and community scale. In addition to supporting individualized healthcare and (tele-)medicine, novel methods are also needed to analyze, visualize, and present interpreted data back to patients, so to better engage them in managing their own health and wellness.

Connected Context-Aware Distributed Care: Advancing Effectiveness For All Stakeholders

Health is moving outside the traditional “box” of the clinic, hospital, or rehab facility while at the same time emerging mobile and networking capabilities have the potential to change the paradigm of health. New sensing  and  real-time access to high bandwidth data across a distributed care network can enable new models for monitoring, discovery, clinical decision support and population health. Health systems and communities will have new opportunities and challenges to manage and monitor health and healthcare services and to better manage the health of the population under their care (with potential revenue implications). Likewise people will have new ways to capture data on wellness and disease management with a constant coach or companion to support health. For all stakeholders, these systems will rely on critical context-aware capabilities to manage timely information delivery and combat information overload. This panel will talk about the challenges to develop high bandwidth, highly connected and distributed capabilities to revolutionize health.

With these four themes as a foundation, the workshop participants also discussed cross-cutting challenges in security, privacy, deployment and sustainability issues, and multidisciplinary collaboration needs and barriers.

All participants were asked to create a one pager that addresses the following three items:

• A computing grand challenge in smart health.
• Open research areas in your field of study.
• Questions for the broader community.

This workshop is part of a series of workshops to inform computing research investments and priorities for Health IT, Smart and Pervasive Health and related health and healthcare priorities.  CCC workshops in 2009 and 2012 resulted in two white papers that informed programs such as the joint NSF and NIH Smart and Connected Health research program. Since then additional workshops have focused on the Brain research initiative (2014) and articulated a research agenda for Aging in Place (2014) and Inclusive Access to Rich Online Content and Services (2015). Healthcare was also called out as a critical research area in PCAST’s 2015 review of NITRD.