and the need to further develop and pilot the policies, analytic methods, and technologies associated with their use.

Engaging bias. Several challenges and barriers to learning from the digital health data utility were cited, including uncertainty about the completeness and reliability of many data sources, as well as the presence of multiple forms of bias. The need for detailed expert assessments of the implications of bias on analyses, as well as in the new context of the very large datasets now emerging, was suggested by several workshop participants.

Core elements. The identification and application of a set of minimum data elements to provide information on cost, quality, health status, and health trends was suggested, by several discussants, as a critical component to accelerating progress on learning from the health data utility. Reform of regulatory frameworks to encourage structured collection, assessment, and use of routinely collected data, in order to facilitate and support this learning, was highlighted by some participants as an important first step.


Greater clarity on governance, both in terms of what it would look like and the issues for engagement, specifically in terms of access and sustainability, was a theme echoed in many workshop discussions.

Domains. Some participants pointed to a need to identify key domains for which governance structures are necessary to accelerate the evolution of the digital data utility, and begin to catalyze their engagement.

Access and ownership. Suggested approaches to ensuring participation included enabling broader access to data sources and ensuring that the flow of information is multidirectional. This democratization of roles could facilitate the engagement of the issue of data ownership, broaden sources of input, exhibit the potential of information use to meet stakeholder needs, and demonstrate the value of the collection and use of the data.

Business model. There is a need for a better understanding of both the costs and benefits associated with the uses of digital health data for learning and continuous improvement. Quantitative and qualitative approaches to insights on how information might be leveraged to increase health benefits and minimize associated costs from the perspectives of the many diverse stakeholders were highlighted by some participants as an important first step on this count. Additionally, the application of analytics for patient panel management and to support pay for performance payment initiatives such as ACOs were cited by individual participants as examples of areas of promise for establishing sustainable efforts.

The National Academies of Sciences, Engineering, and Medicine
500 Fifth St. N.W. | Washington, D.C. 20001

Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement