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Objective Although patient generated health data (PGHD) has stimulated excitement about its potential to increase patient engagement and to offer clinicians new insights into patient health status, we know little about these efforts at scale and whether they align with patient preferences. This study sought to characterize provider-led PGHD approaches, assess whether they aligned with patient preferences, and identify challenges to scale and impact. Materials and Methods We interviewed leaders from a geographically diverse set of health systems (n = 6), leaders from large electronic health record vendors (n = 3), and leaders from vendors providing PGHD solutions to health systems (n = 3). Next, we interviewed patients with 1 or more chronic conditions (n = 10), half of whom had PGHD experience. We conducted content analysis to characterize health system PGHD approaches, assess alignment with patient preferences, and identify challenges. Results In this study, 3 primary approaches were identified, and each was designed to support collection of a different type of PGHD: 1) health history, 2) validated questionnaires and surveys, and 3) biometric and health activity. Whereas patient preferences aligned with health system approaches, patients raised concerns about data security and the value of reporting. Health systems cited challenges related to lack of reimbursement, data quality, and clinical usefulness of PGHD. Discussion Despite a federal policy focus on PGHD, it is not yet being pursued at scale. Whereas many barriers contribute to this narrow pursuit, uncertainty around the value of PGHD, from both patients and providers, is a primary inhibitor. Conclusion Our results reveal a fairly narrow set of approaches to PGHD currently pursued by health systems at scale.

BackgroundWith the emergence of new health information technologies, health information can be shared across networks, with or without patients’ awareness and/or their consent. It is often argued that there can be an ethical obligation to participate in biomedical research, motivated by altruism, particularly when risks are low. In this study, we explore whether altruism contributes to the belief that there is an ethical obligation to share information about one’s health as well as how other health care experiences, perceptions, and concerns might be related to belief in such an obligation.MethodsWe conducted an online survey using the National Opinion Research Center’s (NORC) probability-based, nationally representative sample of U.S. adults. Our final analytic sample included complete responses from 2069 participants. We used multivariable logistic regression to examine how altruism, together with other knowledge, attitudes, and experiences contribute to the belief in an ethical obligation to allow health information to be used for research.ResultsWe find in multivariable regression that general altruism is associated with a higher likelihood of belief in an ethical obligation to allow one’s health information to be used for research (OR = 1.22, SE = 0.14, p = 0.078). Trust in the health system and in care providers are both associated with a significantly higher likelihood of believing there is an ethical obligation to allow health information to be used (OR = 1.48, SE = 0.76, pConclusionsBelief that there is an ethical obligation to allow one’s health information to be used for research is shaped by altruism and by one’s experience with, and perceptions of, health care and by general concerns about the use of personal information. Altruism cannot be assumed and researchers must recognize the ways encounters with the health care system influence (un)willingness to share one’s health information.

Background Improving primary care for patients with chronic illness is critical to advancing healthcare quality and value. Yet, little is known about what strategies are successful in helping primary care practices deliver high-quality care for this population under value-based payment models. Methods Double-blind interviews in 14 primary care practices in the state of Michigan, stratified based on whether they did (n = 7) or did not (n = 7) demonstrate improvement in primary care outcomes for patients with at least one reported chronic disease between 2010 and 2013. All practices participate in a statewide pay-for-performance program run by a large commercial payer. Using an implementation science framework to identify leverage points for effecting organizational change, we sought to identify, describe and compare strategies among improving and non-improving practices across three domains: (1) organizational learning opportunities, (2) approaches to motivating staff, and (3) acquisition and use of resources. Results We identified 10 strategies; 6 were “differentiating” – that is, more prevalent among improving practices. These differentiating strategies included: (1) participation in learning collaboratives , (2) accessing payer tools to monitor quality performance, (3) framing pay-for-performance as a practice transformation opportunity, (4) reinvesting earned incentive money in equitable, practice-centric improvement, (5) employing a care manager, and (6) using available technical support from local hospitals and provider organizations to support performance improvement. Implementation of these strategies varied based on organizational context and relative strengths. Conclusions Practices that succeeded in improving care for chronic disease patients pursued a mix of strategies that helped meet immediate care delivery needs while also creating new adaptive structures and processes to better respond to changing pressures and demands. These findings help inform payers and primary care practices seeking evidence-based strategies to foster a stronger delivery system for patients with significant healthcare needs.

Introduction The growth of data science has led to an explosion in new knowledge alongside various approaches to representing and sharing biomedical knowledge in computable form. These changes have not been matched by an understanding of what healthcare delivery organizations need to do to adapt and continuously deploy computable knowledge. It is therefore important to begin to conceptualize such changes in order to facilitate routine and systematic application of knowledge that improves the health of individuals and populations. Methods An AHRQ‐funded conference convened a group of experts from a range of fields to analyze the current state of knowledge management in healthcare delivery organizations and describe how it needs to evolve to enable computable knowledge management. Presentations and discussions were recorded and analyzed by the author team to identify foundational concepts and new domains of healthcare delivery organization knowledge management capabilities. Results Three foundational concepts include 1) the current state of knowledge management in healthcare delivery organizations relies on an outdated biomedical library model, and only a small number of organizations have developed enterprise‐scale knowledge management approaches that “push” knowledge in computable form to frontline decisions, 2) the concept of Learning Health Systems creates an imperative for scalable computable knowledge management approaches, and 3) the ability to represent data science discoveries in computable form that is FAIR (findable, accessible, interoperable, reusable) is fundamental to spread knowledge at scale. For healthcare delivery organizations to engage with computable knowledge management at scale, they will need new organizational capabilities across three domains: policies and processes, technology, and people. Examples of specific capabilities were developed. Conclusions Healthcare delivery organizations need to substantially scale up and retool their knowledge management approaches in order to benefit from computable biomedical knowledge.