Fifty Years of Trust Research in Health Care: A Synthetic Review.

Policy Points First, policymakers can create conditions that will facilitate public trust in health care organizations by making creating and enforcing health policies that make exploitative behavior costly. Second, policymakers can bolster the trustworthiness of health care markets and organizations by using their regulatory authority to address and mitigate harm from conflicts-of-interest and regulatory capture. Third, policymakers and government agencies can further safeguard the public's trust by being transparent and effective about their role in the provision of health services to the public. CONTEXT: Trust plays a critical role in facilitating health care delivery and calls for rebuilding trust in health care are increasingly commonplace. This article serves as a primer on the trust literature for health policymakers, organizational leaders, clinicians, and researchers based on the long history of engagement with the topic among health policy and services researchers. METHODS: We conducted a synthetic review of the health services and health policy literatures on trust since 1970. We organize our findings by trustor-trustee dyads, highlighting areas of convergence, tensions and contradictions, and methodological considerations. We close by commenting on the challenges facing the study of trust in health care, the potential value in borrowing from other disciplines, and imperatives for the future. FINDINGS: We identified 725 articles for review. Most focused on patients' trust in clinicians (n = 499), but others explored clinicians' trust in patients (n = 11), clinicians' trust in clinicians (n = 69), and clinician/patient trust in organizations (n = 19) and systems (n = 127).  Across these five subliteratures, there was lack of consensus about definitions, dimensions, and key attributes of trust. Researchers leaned heavily on cross-sectional survey designs, with limited methodological attention to the relational or contextual realities of trust. Trust has most commonly been treated as an independent variable related to attitudinal and behavioral outcomes. We suggest two challenges have limited progress for the field: (1) conceptual murkiness in terms and theories, and (2) limited observability of the phenomena. Insights from philosophy, sociology, economics, and psychology offer insights for how to advance both the theoretical and empirical study of health-related trust. CONCLUSION: Conceptual clarity and methodological creativity are critical to advancing health-related trust research. Although rigorous research in this area is challenging, the essential role of trust in population health necessitates continued grappling with the topic.

US Residents' Preferences for Sharing of Electronic Health Record and Genetic Information: A Discrete Choice Experiment.

OBJECTIVES: The aim to this study was to assess preferences for sharing of electronic health record (EHR) and genetic information separately and to examine whether there are different preferences for sharing these 2 types of information. METHODS: Using a population-based, nationally representative survey of the United States, we conducted a discrete choice experiment in which half of the subjects (N = 790) responded to questions about sharing of genetic information and the other half (N = 751) to questions about sharing of EHR information. Conditional logistic regression models assessed relative preferences across attribute levels of where patients learn about health information sharing, whether shared data are deidentified, whether data are commercialized, how long biospecimens are kept, and what the purpose of sharing the information is. RESULTS: Individuals had strong preferences to share deidentified (vs identified) data (odds ratio [OR] 3.26, 95% confidence interval 2.68-3.96) and to be able to opt out of sharing information with commercial companies (OR 4.26, 95% confidence interval 3.42-5.30). There were no significant differences regarding how long biospecimens are kept or why the data are being shared. Individuals had a stronger preference for opting out of sharing genetic (OR 4.26) versus EHR information (OR 2.64) (P = .002). CONCLUSIONS: Hospital systems and regulatory bodies should consider patient preferences for sharing of personal medical records or genetic information. For both genetic and EHR information, patients strongly prefer their data to be deidentified and to have the choice to opt out of sharing information with commercial companies.

Learning about COVID-19: sources of information, public trust, and contact tracing during the pandemic.

OBJECTIVE: To assess the association between public attitudes, beliefs, and information seeking about the COVID-19 pandemic and willingness to participate in contact tracing in Michigan. METHODS: Using data from the quarterly Michigan State of the State survey conducted in May 2020 (n = 1000), we conducted multiple regression analyses to identify factors associated with willingness to participate in COVID-19 contact tracing efforts. RESULTS: Perceived threat of the pandemic to personal health (B = 0.59, p = <.00, Ref = No threat) and general trust in the health system (B = 0.17, p < 0.001), were the strongest positive predictors of willingness to participate in contact tracing. Concern about misinformation was also positively associated with willingness to participate in contact tracing (B = 0.30, p < 0.001; Ref = No concern). Trust in information from public health institutions was positively associated with willingness to participate in contact tracing, although these institutions were not necessarily the main sources of information about COVID-19. CONCLUSION: Policy makers can enhance willingness to participate in public health efforts such as contact tracing during infectious disease outbreaks by helping the public appreciate the seriousness of the public health threat and communicating trustworthy information through accessible channels.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines.

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.

Equity and AI governance at academic medical centers.

OBJECTIVES: To understand whether and how equity is considered in artificial intelligence/machine learning governance processes at academic medical centers. STUDY DESIGN: Qualitative analysis of interview data. METHODS: We created a database of academic medical centers from the full list of Association of American Medical Colleges hospital and health system members in 2022. Stratifying by census region and restricting to nonfederal and nonspecialty centers, we recruited chief medical informatics officers and similarly positioned individuals from academic medical centers across the country. We created and piloted a semistructured interview guide focused on (1) how academic medical centers govern artificial intelligence and prediction and (2) to what extent equity is considered in these processes. A total of 17 individuals representing 13 institutions across 4 census regions of the US were interviewed. RESULTS: A minority of participants reported considering inequity, racism, or bias in governance. Most participants conceptualized these issues as characteristics of a tool, using frameworks such as algorithmic bias or fairness. Fewer participants conceptualized equity beyond the technology itself and asked broader questions about its implications for patients. Disparities in health information technology resources across health systems were repeatedly identified as a threat to health equity. CONCLUSIONS: We found a lack of consistent equity consideration among academic medical centers as they develop their governance processes for predictive technologies despite considerable national attention to the ways these technologies can cause or reproduce inequities. Health systems and policy makers will need to specifically prioritize equity literacy among health system leadership, design oversight policies, and promote critical engagement with these tools and their implications to prevent the further entrenchment of inequities in digital health care.

How patients distinguish between clinical and administrative predictive models in health care.

OBJECTIVES: To understand patient perceptions of specific applications of predictive models in health care. STUDY DESIGN: Original, cross-sectional national survey. METHODS: We conducted a national online survey of US adults with the National Opinion Research Center from November to December 2021. Measures of internal consistency were used to identify how patients differentiate between clinical and administrative predictive models. Multivariable logistic regressions were used to identify relationships between comfort with various types of predictive models and patient demographics, perceptions of privacy protections, and experiences in the health care system. RESULTS: A total of 1541 respondents completed the survey. After excluding observations with missing data for the variables of interest, the final analytic sample was 1488. We found that patients differentiate between clinical and administrative predictive models. Comfort with prediction of bill payment and missed appointments was especially low (21.6% and 36.6%, respectively). Comfort was higher with clinical predictive models, such as predicting stroke in an emergency (55.8%). Experiences of discrimination were significant negative predictors of comfort with administrative predictive models. Health system transparency around privacy policies was a significant positive predictor of comfort with both clinical and administrative predictive models. CONCLUSIONS: Patients are more comfortable with clinical applications of predictive models than administrative ones. Privacy protections and transparency about how health care systems protect patient data may facilitate patient comfort with these technologies. However, larger inequities and negative experiences in health care remain important for how patients perceive administrative applications of prediction.

Public perspectives on the use of different data types for prediction in healthcare.

OBJECTIVE: Understand public comfort with the use of different data types for predictive models. MATERIALS AND METHODS: We analyzed data from a national survey of US adults (n = 1436) fielded from November to December 2021. For three categories of data (identified using factor analysis), we use descriptive statistics to capture comfort level. RESULTS: Public comfort with data use for prediction is low. For 13 of 15 data types, most respondents were uncomfortable with that data being used for prediction. In factor analysis, 15 types of data grouped into three categories based on public comfort: (1) personal characteristic data, (2) health-related data, and (3) sensitive data. Mean comfort was highest for health-related data (2.45, SD 0.84, range 1-4), followed by personal characteristic data (2.36, SD 0.94), and sensitive data (1.88, SD 0.77). Across these categories, we observe a statistically significant positive relationship between trust in health systems' use of patient information and comfort with data use for prediction. DISCUSSION: Although public trust is recognized as important for the sustainable expansion of predictive tools, current policy does not reflect public concerns. Low comfort with data use for prediction should be addressed in order to prevent potential negative impacts on trust in healthcare. CONCLUSION: Our results provide empirical evidence on public perspectives, which are important for shaping the use of predictive models. Findings demonstrate a need for realignment of policy around the sensitivity of non-clinical data categories.

Public comfort with the use of ChatGPT and expectations for healthcare.

OBJECTIVES: To examine whether comfort with the use of ChatGPT in society differs from comfort with other uses of AI in society and to identify whether this comfort and other patient characteristics such as trust, privacy concerns, respect, and tech-savviness are associated with expected benefit of the use of ChatGPT for improving health. MATERIALS AND METHODS: We analyzed an original survey of U.S. adults using the NORC AmeriSpeak Panel (n = 1787). We conducted paired t-tests to assess differences in comfort with AI applications. We conducted weighted univariable regression and 2 weighted logistic regression models to identify predictors of expected benefit with and without accounting for trust in the health system. RESULTS: Comfort with the use of ChatGPT in society is relatively low and different from other, common uses of AI. Comfort was highly associated with expecting benefit. Other statistically significant factors in multivariable analysis (not including system trust) included feeling respected and low privacy concerns. Females, younger adults, and those with higher levels of education were less likely to expect benefits in models with and without system trust, which was positively associated with expecting benefits (P = 1.6 × 10-11). Tech-savviness was not associated with the outcome. DISCUSSION: Understanding the impact of large language models (LLMs) from the patient perspective is critical to ensuring that expectations align with performance as a form of calibrated trust that acknowledges the dynamic nature of trust. CONCLUSION: Including measures of system trust in evaluating LLMs could capture a range of issues critical for ensuring patient acceptance of this technological innovation.

Demonstrating Trustworthiness to Patients in Data-Driven Health Care.

Patient data is used to drive an ecosystem of advanced digital tools in health care, like predictive models or artificial intelligence-based decision support. Patients themselves, however, receive little information about these technologies or how they affect their care. This raises important questions about patient trust and continued engagement in a health care system that extracts their data but does not treat them as key stakeholders. This essay explores these tensions and provides steps forward for health systems as they design advanced health information-technology (IT) policies and practices. It centers patients, their concerns, and the ways they perceive trustworthiness to reframe advanced health IT in service of patient interests.

A Critical Analysis of White Racial Framing and Comfort with Medical Research.

OBJECTIVE: Analyze racial differences in comfort with medical research using an alternative to the traditional approach that treats white people as a raceless norm. METHODS: Quantitative analysis of survey responses (n = 1,570) from Black and white residents of the US to identify relationships between perceptions of research as a right or a risk, and comfort participating in medical research. RESULTS: A lower proportion of white respondents reported that medical experimentation occurred without patient consent (p < 0.001) and a higher proportion of white respondents reported that it should be their right to participate in medical research (p = 0.02). Belief in one's right to participate was significantly predictive of comfort (b = 0.37, p < 0.001). Belief in experimentation without consent was significantly predictive of comfort for white respondents but not for Black respondents in multivariable analysis. CONCLUSIONS: A rights-based orientation and less concern about the risks of medical research among white respondents demonstrate comparative advantage. Efforts to diversify medical research may perpetuate structural racism if they do not (1) critically engage with whiteness and its role in comfort with participation, and (2) identify and respond specifically to the needs of Black patients.

Applying anti-racist approaches to informatics: a new lens on traditional frames.

Health organizations and systems rely on increasingly sophisticated informatics infrastructure. Without anti-racist expertise, the field risks reifying and entrenching racism in information systems. We consider ways the informatics field can recognize institutional, systemic, and structural racism and propose the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms. We enumerate guiding questions for stakeholders along with a PHCRP-Informatics framework. By focusing on (1) critical self-reflection, (2) following the expertise of well-established scholars of racism, (3) centering the voices of affected individuals and communities, and (4) critically evaluating practice resulting from informatics systems, stakeholders can work to minimize the impacts of racism. Informatics, informed and guided by this proposed framework, will help realize the vision of health systems that are more fair, just, and equitable.

Policy Preferences Regarding Health Data Sharing Among Patients With Cancer: Public Deliberations.

BACKGROUND: Precision health offers the promise of advancing clinical care in data-driven, evidence-based, and personalized ways. However, complex data sharing infrastructures, for-profit (commercial) and nonprofit partnerships, and systems for data governance have been created with little attention to the values, expectations, and preferences of patients about how they want to be engaged in the sharing and use of their health information. We solicited patient opinions about institutional policy options using public deliberation methods to address this gap. OBJECTIVE: We aimed to understand the policy preferences of current and former patients with cancer regarding the sharing of health information collected in the contexts of health information exchange and commercial partnerships and to identify the values invoked and perceived risks and benefits of health data sharing considered by the participants when formulating their policy preferences. METHODS: We conducted 2 public deliberations, including predeliberation and postdeliberation surveys, with patients who had a current or former cancer diagnosis (n=61). Following informational presentations, the participants engaged in facilitated small-group deliberations to discuss and rank policy preferences related to health information sharing, such as the use of a patient portal, email or SMS text messaging, signage in health care settings, opting out of commercial data sharing, payment, and preservation of the status quo. The participants ranked their policy preferences individually, as small groups by mutual agreement, and then again individually in the postdeliberation survey. RESULTS: After deliberation, the patient portal was ranked as the most preferred policy choice. The participants ranked no change in status quo as the least preferred policy option by a wide margin. Throughout the study, the participants expressed concerns about transparency and awareness, convenience, and accessibility of information about health data sharing. Concerns about the status quo centered around a lack of transparency, awareness, and control. Specifically, the patients were not aware of how, when, or why their data were being used and wanted more transparency in these regards as well as greater control and autonomy around the use of their health data. The deliberations suggested that patient portals would be a good place to provide additional information about data sharing practices but that over time, notifications should be tailored to patient preferences. CONCLUSIONS: Our study suggests the need for increased disclosure of health information sharing practices. Describing health data sharing practices through patient portals or other mechanisms personalized to patient preferences would minimize the concerns expressed by patients about the extent of data sharing that occurs without their knowledge. Future research and policies should identify ways to increase patient control over health data sharing without reducing the societal benefits of data sharing.

Integrating predictive models into care: facilitating informed decision-making and communicating equity issues.

OBJECTIVES: As predictive analytics are increasingly used and developed by health care systems, recognition of the threat posed by bias has grown along with concerns about how providers can make informed decisions related to predictive models. To facilitate informed decision-making around the use of these models and limit the reification of bias, this study aimed to (1) identify user requirements for informed decision-making and utilization of predictive models and (2) anticipate and reflect equity concerns in the information provided about models. STUDY DESIGN: Qualitative analysis of user-centered design (n = 46) and expert interviews (n = 10). METHODS: We conducted a user-centered design study at an academic medical center with clinicians and stakeholders to identify informational elements required for decision-making related to predictive models with a product information label prototype. We also conducted equity-focused interviews with experts to extend the user design study and anticipate the ways in which models could interact with or reflect structural inequity. RESULTS: Four key informational elements were reported as necessary for informed decision-making and confidence in the use of predictive models: information on (1) model developers and users, (2) methodology, (3) peer review and model updates, and (4) population validation. In subsequent expert interviews, equity-related concerns included the purpose or application of a model and its relationship to structural inequity. CONCLUSIONS: Health systems should provide key information about predictive models to clinicians and other users to facilitate informed decision-making about the use of these models. Implementation efforts should also expand to routinely incorporate equity considerations from inception through the model development process.

Discrimination, trust, and withholding information from providers: Implications for missing data and inequity.

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.

Public Deliberation Process on Patient Perspectives on Health Information Sharing: Evaluative Descriptive Study.

BACKGROUND: Precision oncology is one of the fastest-developing domains of personalized medicine and is one of many data-intensive fields. Policy for health information sharing that is informed by patient perspectives can help organizations align practice with patient preferences and expectations, but many patients are largely unaware of the complexities of how and why clinical health information is shared. OBJECTIVE: This paper evaluates the process of public deliberation as an approach to understanding the values and preferences of current and former patients with cancer regarding the use and sharing of health information collected in the context of precision oncology. METHODS: We conducted public deliberations with patients who had a current or former cancer diagnosis. A total of 61 participants attended 1 of 2 deliberative sessions (session 1, n=28; session 2, n=33). Study team experts led two educational plenary sessions, and trained study team members then facilitated discussions with small groups of participants. Participants completed pre- and postdeliberation surveys measuring knowledge, attitudes, and beliefs about precision oncology and data sharing. Following informational sessions, participants discussed, ranked, and deliberated two policy-related scenarios in small groups and in a plenary session. In the analysis, we evaluate our process of developing the deliberative sessions, the knowledge gained by participants during the process, and the extent to which participants reasoned with complex information to identify policy preferences. RESULTS: The deliberation process was rated highly by participants. Participants felt they were listened to by their group facilitator, that their opinions were respected by their group, and that the process that led to the group's decision was fair. Participants demonstrated improved knowledge of health data sharing policies between pre- and postdeliberation surveys, especially regarding the roles of physicians and health departments in health information sharing. Qualitative analysis of reasoning revealed that participants recognized complexity, made compromises, and engaged with trade-offs, considering both individual and societal perspectives related to health data sharing. CONCLUSIONS: The deliberative approach can be valuable for soliciting the input of informed patients on complex issues such as health information sharing policy. Participants in our two public deliberations demonstrated that giving patients information about a complex topic like health data sharing and the opportunity to reason with others and discuss the information can help garner important insights into policy preferences and concerns. Data on public preferences, along with the rationale for information sharing, can help inform policy-making processes. Increasing transparency and patient engagement is critical to ensuring that data-driven health care respects patient autonomy and honors patient values and expectations.

Socially situated risk: challenges and strategies for implementing algorithmic risk scoring for care management.

OBJECTIVE: To characterize challenges and strategies related to algorithmic risk scoring for care management eligibility determinations. MATERIALS AND METHODS: Interviews with 19 administrators from 13 physician organizations representing over 2200 physician offices and 8800 physicians in Michigan. Post-implementation interviews were coded using thematic analysis. RESULTS: Utility of algorithmic risk scores was limited due to outdated claims or incomplete information about patients' socially situated risks (eg, caregiver turnover, social isolation). Resulting challenges included lack of physician engagement and inefficient use of staff time reviewing eligibility determinations. To address these challenges, risk scores were supplemented with physician knowledge and clinical data. DISCUSSION AND CONCLUSION: Current approaches to risk scoring based on claims data for payer-led programs struggle to gain physician acceptance and support because of data limitations. To respond to these limitations, physician input regarding socially situated risk and utilization of more timely data may improve eligibility determinations.

Understanding racial differences in attitudes about public health efforts during COVID-19 using an explanatory mixed methods design.

Efforts to mitigate the spread of COVID-19 rely on trust in public health organizations and practices. These practices include contact tracing, which requires people to share personal information with public health organizations. The central role of trust in these practices has gained more attention during the pandemic, resurfacing endemic questions about public trust and potential racial trust disparities, especially as they relate to participation in public health efforts. Using an explanatory mixed methods design, we conducted quantitative analysis of state-level survey data in the United States from a representative sample of Michigan residents (n = 1000) in May 2020. We used unadjusted and adjusted linear regressions to examine differences in trust in public health information and willingness to participate in public health efforts by race. From July to September 2020, we conducted qualitative interviews (n = 26) to further explain quantitative results. Using unadjusted linear regression, we observed higher willingness to participate in COVID-19 public health efforts among Black survey respondents compared to White respondents. In adjusted analysis, that difference disappeared, yielding no statistically significant difference between Black and White respondents in either trust in public health information sources or willingness to participate. Qualitative interviews were conducted to explain these findings, considering their contrast with assumptions that Black people would exhibit lower trust in public health organizations during COVID-19. Altruism, risk acknowledgement, trust in public health organizations during COVID-19, and belief in efficacy of public health efforts contributed to willingness to participate in public health efforts among interviewees. Our findings underscore the contextual nature of trust, and the importance of this context when analyzing protective health behaviors among communities disproportionately affected by COVID-19. Assumptions about mistrust among Black individuals and communities may be inaccurate because they overlook the specific context of the public health crisis. These findings are important because they indicate that Black respondents are exhibiting strategic trust during COVID-19 despite systemic, contemporary, and historic barriers to trust. Conceptual specificity rather than blanket generalizations is warranted, especially given the harms of stereotyping and discrimination.

Patient-Reported Experiences of Discrimination in the US Health Care System.

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.