Background: In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care. Objective: This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward. Methods: We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial Knowledge Gaps and Uncertainties proposed in 2007. Results: We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy. Conclusions: There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.

Some large population biobanks that house biospecimens and health information for research seek broad consent from participants, whereas others reconsent for specific new studies. Understanding research participants’ attitudes and preferences about broad and narrow consent may improve recruitment, retention, and public support. An online survey was conducted among a representative sample of 4,659 US adults to examine relationships between consent preferences and demographic factors, beliefs about privacy and the value of research, and the perceived trustworthiness of researchers. Participants preferred broad consent (52%) over study-by-study consent models (48%). Higher preferences for study-by-study consent observed among black non-Hispanic respondents and respondents with lower income and education were explained by differences in the prevalence of one or more beliefs about the study. Respondents with fears about research and those who would feel respected if asked for permission for each research use preferred study-by-study consent. Preference for broad consent was related to the desire not to be bothered with multiple requests and the belief that the study could lead to improved treatments, cures, and lives saved. These data suggest that support for broad consent is contingent on sufficient information about data use. Work with research participants and community leaders to understand, respond to, and influence opinions about a given, ongoing study may improve uptake of broad consent. Genet Med 16 1, 11–18.

Biobanks are made all the more valuable when the biological samples they hold can be linked to health information collected in research, electronic health records, or public health practice. Public trust in such systems that share health information for research and health care practice is understudied. Our research examines characteristics of the general public that predict trust in a health system that includes researchers, health care providers, insurance companies and public health departments. We created a 119-item survey of predictors and attributes of system trust and fielded it using Amazon’s MTurk system (n = 447). We found that seeing one’s primary care provider, having a favorable view of data sharing and believing that data sharing will improve the quality of health care, as well as psychosocial factors (altruism and generalized trust) were positively and significantly associated with system trust. As expected, privacy concern, but counterintuitively, knowledge about health information sharing were negatively associated with system trust. We conclude that, in order to assure the public’s trust, policy makers charged with setting best practices for governance of biobanks and access to electronic health records should leverage critical access points to engage a diverse public in joint decision making.

Quality care requires collaborative communication, information exchange, and decision-making between patients and providers. Complete and accurate data about patients and from patients are especially important as high volumes of data are used to build clinical decision support tools and inform precision medicine initiatives. However, systematically missing data can bias these tools and threaten their effectiveness. Data completeness relies in many ways on patients being comfortable disclosing information to their providers without prohibitive concerns about security or privacy. Patients are likely to withhold information in the context of low trust relationships with providers, but it is unknown how experiences of discrimination in the healthcare system also relate to non-disclosure. In this study, we assess the relationship between withholding information from providers, experiences of discrimination, and multiple types of patient trust. Using a nationally representative sample of US adults (n = 2,029), weighted logistic regression modeling indicated a statistically significant relationship between experiences of discrimination and withholding information from providers (OR 3.7; CI [2.6–5.2], p < .001). Low trust in provider disclosure of conflicts of interest and low trust in providers' responsible use of health information were also positively associated with non-disclosure. We further analyzed the relationship between non-disclosure and the five most common types of discrimination (e.g., discrimination based on race, education/income, weight, gender, and age). We observed that all five types were statistically significantly associated with non-disclosure (p < .05). These results suggest that experiences of discrimination and specific types of low trust have a meaningful association with a patient's willingness to share information with their provider, with important implications for the quality of data available for medical decision-making and care. Because incomplete information can contribute to lower quality care, especially in the context of data-driven decision-making, patients experiencing discrimination may be further disadvantaged and harmed by systematic data missingness in their records.