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Key Points Question What are the national prevalence, frequency, and main types of discrimination that adult patients report experiencing in the US health care system? Findings In this nationally representative cross-sectional survey study, 21% of 2137 US adult survey respondents indicated that they had experienced discrimination in the health care system, and 72% of those who had experienced discrimination reported experiencing it more than once. Racial/ethnic discrimination was the most frequently reported type of discrimination respondents experienced. Meaning Experiences of discrimination in the health care system appear to be more common than previously recognized and deserve considerable attention., Importance Although considerable evidence exists on the association between negative health outcomes and daily experiences of discrimination, less is known about such experiences in the health care system at the national level. It is critically necessary to measure and address discrimination in the health care system to mitigate harm to patients and as part of the larger ongoing project of responding to health inequities. Objectives To (1) identify the national prevalence of patient-reported experiences of discrimination in the health care system, the frequency with which they occur, and the main types of discrimination experienced and (2) examine differences in the prevalence of discrimination across demographic groups. Design, Setting, and Participants This cross-sectional national survey fielded online in May 2019 used a general population sample from the National Opinion Research Center’s AmeriSpeak Panel. Surveys were sent to 3253 US adults aged 21 years or older, including oversamples of African American respondents, Hispanic respondents, and respondents with annual household incomes below 200% of the federal poverty level. Main Outcomes and Measures Analyses drew on 3 survey items measuring patient-reported experiences of discrimination, the primary types of discrimination experienced, the frequency with which they occurred, and the demographic and health-related characteristics of the respondents. Weighted bivariable and multivariable logistic regressions were conducted to assess associations between experiences of discrimination and several demographic and health-related characteristics. Results Of 2137 US adult respondents who completed the survey (66.3% response rate; unweighted 51.0% female; mean [SD] age, 49.6 [16.3] years), 458 (21.4%) reported that they had experienced discrimination in the health care system. After applying weights to generate population-level estimates, most of those who had experienced discrimination (330 [72.0%]) reported experiencing it more than once. Of 458 reporting experiences of discrimination, racial/ethnic discrimination was the most common type (79 [17.3%]), followed by discrimination based on educational or income level (59 [12.9%]), weight (53 [11.6%]), sex (52 [11.4%]), and age (44 [9.6%]). In multivariable analysis, the odds of experiencing discrimination were higher for respondents who identified as female (odds ratio [OR], 1.88; 95% CI, 1.50-2.36) and lower for older respondents (OR, 0.98; 95% CI, 0.98-0.99), respondents earning at least $50 000 in annual household income (OR, 0.76; 95% CI, 0.60-0.95), and those reporting good (OR, 0.59; 95% CI, 0.46-0.75) or excellent (OR, 0.41; 95% CI, 0.31-0.56) health compared with poor or fair health. Conclusions and Relevance The results of this study suggest that experiences of discrimination in the health care system appear more common than previously recognized and deserve considerable attention. These findings contribute to understanding of the scale at which interpersonal discrimination occurs in the US health care system and provide crucial evidence for next steps in assessing the risks and consequences of such discrimination. The findings also point to a need for further analysis of how interpersonal discrimination interacts with structural inequities and social determinants of health to build effective responses., This cross-sectional study examines the responses to a recent National Opinion Research Center survey to assess the prevalence, frequency, and main types of discrimination experienced by adult patients in the US health care system.

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.

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students’ visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students’ successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules.

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.

Improving primary care for high-needs patients is critical to advancing healthcare quality and value. Yet, little is known about what strategies have been successful in helping practices make the n...