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.