Digital Infrastructure for the Learning Health System The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary (2011) / Chapter Skim
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9 Growing the Digital Health Infrastructure
9 Growing the Digital Health Infrastructure
Pages 223-238
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From page 223... ...
They are included in Box 9-1 and described in more detail in the sections below. Participants called out a number of elements for consideration surrounding the strategic opportunities for technical progress.
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Finally, the need to drive continuous innovation of technical approaches through constant testing and refinement and the creation of a supporting multidisciplinary research ecosystem were suggested. Discussion of the strategic elements needing attention for the creation of a robust knowledge generation and use engine for the learning health
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Conveying the value proposition for stakeholder participation and creation of a shared learning culture among patients and the population were prominent themes. Participants discussed using the digital infrastructure to strengthen patient–clinician outcome partnerships through better patient portals and increased availability of lay-oriented, user-friendly clinical and nonmedical health data.
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Participants pointed to a focus on functionalities consistent with ULS systems, and their application to the digital health system, as a potential starting point in advancing the ULS approach. Definition of the ULS principles and characteristics that support learning system functionalities, including the feedback and feedforward nature of the learning engine, such as identification strategies, privacy controls, the availability of a complete longitudinal record at the point of care, inferential capacity, and researchreadiness, were highlighted as critical foundational steps in the development of this technical enterprise.
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, particular attention was paid to the use of metadata to facilitate interoperability and information exchange -- including to maintain data context and provenance, authentication, and privacy. This, in concert with a fast-prototyping component, can allow for incremental specification and system growth with the opportunity for functional enhancement, such as refinement of semantic interoperability, to meet specific requirements depending on use.
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Participants identified a number of needs to be addressed in order for the digital health infrastructure to reach its full potential as a source of real-time clinical research insights. For example, clinical research activities require enlisting clinician support and involvement in researchready clinical records on both quality and content dimensions for reuse in knowledge generation.
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To support this, and the continuing development and innovation around other analytical approaches, the importance of collaborative interdisciplinary networks of researchers was underscored. This was discussed not only for cooperative studies, but for cooperative engagement of issues such as strategies on consistent identifiers for patients, the use of modeling and simulation for knowledge generation, evaluation of approaches for the use of diverse data types and varying data quality, and development of methods for the use of information from mobile consumer devices and patient-generated data.
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Activities that foster patient involvement in and support of knowledge generation, including illustrating the importance of patient preference information to improving care, were discussed as potential approaches to this issue. Following the theme of "renorming" participation of patients and the population in health improvement, and building on the framework established by previous Institute of Medicine work in this area, discussions of the opportunity for strengthening patient–clinician outcome partnerships through the digital infrastructure were discussed.
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GOVERNANCE Discussions of governance strategies for the digital infrastructure for the learning health system focused on facilitating activities to advance some very basic components and principles of the ULS digital health information system. Participants often struggled with the question "what are we proposing to govern?
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As a means of establishing a reference point for progress, workshop participants articulated the need for work to establish a shared vision of the digital health utility for the learning health system. Prospective components noted for this vision include expectations, guiding principles, modus operandi, and an appreciation for the global perspective.
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In the spirit of a continuously improving learning health system, a process for continuous evaluation and improvement of the governance entity and approach was emphasized as important. Areas highlighted included establishing an approach to ongoing assessment of progress and problems, systematic assessment of value realization for recognition and promotion of successful practices, and the support of research on governance and orchestration of the ULS digital health utility in the United States and globally.
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This shift of scope brought into clearer focus several issues discussed, including the opportunity to use HIT and its associated information to build a concept of health that is about more than medical care and draws on seamless interface with information from nonmedical health-related sources to generate knowledge that allows for a more inclusive view of population health improvement. Leverage existing programs and policies.
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report and reiterated during the workshops, both the social and technical components of the digital health infrastructure require a framework that allows for tailoring to specific needs, local innovation, and evolvability. In this respect, the commonly repeated refrain was a call for the principle of parsi mony and minimizing centralization that might constitute a barrier to entry: specify only the minimal set of standards or requirements
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This approach is in line with strategies such as those suggested in the PCAST report for use of metadata for wrapping individual information packets to facilitate interoper ability and health information exchange, in which a primary focus would be on development of the metadata standards.
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A learning sys • tem is one that assesses its own performance against a set of goals and uses the results of that evaluation to change future behaviors. Workshop participants articulated the importance that all compo nents of a digital infrastructure must themselves function as learn ing systems.
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