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

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Summary

Health systems need to provide clear information on predictive models to clinicians for informed decision-making. Addressing bias and equity concerns throughout model development is crucial for responsible use.

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Area of Science:

  • Health Informatics
  • Clinical Decision Support
  • AI in Healthcare

Background:

  • Predictive analytics are increasingly integrated into healthcare systems.
  • Concerns about bias in predictive models and informed provider decision-making are growing.
  • Ensuring equitable use of healthcare AI requires careful consideration of model information.

Purpose of the Study:

  • Identify user requirements for informed decision-making regarding predictive models.
  • Anticipate and address equity concerns in the information provided about predictive models.
  • Develop a framework for transparent and equitable use of healthcare predictive analytics.

Main Methods:

  • Qualitative user-centered design study with clinicians and stakeholders (n=46).
  • Equity-focused interviews with experts (n=10).
  • Analysis of informational elements for decision-making and equity considerations.

Main Results:

  • Four key informational elements identified: model developers/users, methodology, peer review/updates, and population validation.
  • Equity concerns highlighted the model's purpose, application, and relationship to structural inequity.
  • A prototype product information label was developed.

Conclusions:

  • Healthcare systems must provide essential information on predictive models to users.
  • Implementation should integrate equity considerations from the outset of model development.
  • Promoting transparency and equity is vital for the responsible adoption of predictive analytics in healthcare.