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The field that has broadly been defined as Digital Health is concerned with the interconnected use of computational hardware and software in order to measure, understand, and in some cases manage or intervene in those phenomena that impact human health and wellness.  The field has seen an explosion of interest and activity given ever-increasing numbers of mobile, sensor, and telemedicine technologies that can support or enable the instrumentation of individual patients and broader populations.  Simultaneously, the field of Precision Medicine has seen substantial progress over the past decade, focusing on the establishment and linkage of data, information, and knowledge across scales from molecules to patients, such that health promotion and disease treatment strategies can be tailored to an individual’s unique phenotype and informed by the best possible scientific evidence.  There is a clear and compelling argument to be made that Digital Health approaches can serve to inform the compendium of data needed to pursue Precision Medicine paradigms. However, doing so requires that we establish and verify/validate rigorous methodologies for the development of these informatics tools and for the integration of such patient-generated data with other sources such as bio-molecular phenotypes, clinical features derived from a variety of health information technology (HIT) platforms, in particular with Electronic Health Records (EHRs) and environmental or exposome level factors.  A primary example of such efforts is the recent announcement of a partnership between Apple and over a dozen leading healthcare delivery organizations to create deep integration between their EHR platforms and the Apple Health app that is provided via iOS devices.  Such integration can and should yield opportunities for translational research in which multi-scale views of such digital health data, in conjunction with the aforementioned sources, yield evidence that enables the personalization of healthcare delivery.  In recognition of this opportunity, this session at PSB 2019 will focus on the body of research and development that seeks to develop and apply biomedical computing and informatics methods for the purposes of integrating Digital Health data “streams” with complementary and multi-scale biomedical data in support of translational research.  Further, via this session, we will explore the socio-technical issues that surround the effective development and use of methods that serve to integrate digital health data “streams” with more traditional biomedical data sources. In the context of this later area of emphasis, it is important to note that such methods and the broader setting in which they are to be employed differ greatly from traditional approaches to discovery science and healthcare practice, thus necessitating paradigm-shifting approaches to our collective thinking.

Objectives for this session include addressing the following major questions:

  • What are the methodological innovations needed to enable the systematic and rigorous integration, modelling, and analysis of multi-scale data sets involving Digital Health “streams”, bio-molecular phenotypes, clinical features that are measured via traditional clinical care processes and recorded in a variety HIT platforms, as well as complementary indicators of environmental or other exposome factors?

  • How do we involve stakeholders effectively and efficiently in the development of mobile health tools that generate patient-centered data, in order to facilitate informed and shared decision-making and improve clinical outcomes?

  • How do we overcome the socio-cultural and policy-based gaps or barriers that may be encountered when attempting to assemble and investigate such multi-scale data, incorporating both patient-generated data types as well as data collected via more traditional mechanisms such as laboratory-based research and/or clinical care delivery?

  • What are the decision and implementation science, workflow, and human-factors issues that must be addressed in order to return findings generated via the analysis of a combination of Digital Health and Precision Medicine derived data types, such that we can realize the vision of an Evidence Generating Medical System?

Session Organizers:
  • Philip Payne (Washington University in St. Louis)

  • Jessica Tenenbaum (Duke University)

  • Neil Sarkar (Brown University)

  • Subha Madhavan (Georgetown University)

  • John Wilbanks (Sage Bionetworks)