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.

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.

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.