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Effect of Predictive Model on ED Physician Assessments of Patient Disposition
Sponsor: Boston Children's Hospital
Summary
The goal of this study is to measure the impact of fairness-aware algorithms on physician predictions of ED patient admission. Using an experimentally validated machine learning model tuned for equitable outcomes, the investigators quantify the impact of model recommendations on ED physician assessments of admission risk in a silent, prospective study. The investigators survey ED physicians who are not currently caring for patients using live site data. To quantify the impact of the model on ED physician assessments of admission risk, the investigators collect physician assessments before and after consulting the (original or updated) model prediction. The investigators measure ED physician adherence to model suggestions, along with the predictive accuracy and equity of downstream patient outcomes. The outcome of this study is an empirical measure of the extent to which fair ML models may influence admission decisions to mitigate health care disparities.
Key Details
Gender
All
Age Range
18 Years - 65 Years
Study Type
INTERVENTIONAL
Enrollment
10
Start Date
2026-05-01
Completion Date
2026-09-01
Last Updated
2024-08-09
Healthy Volunteers
Yes
Conditions
Interventions
Baseline model
Model prediction of patient disposition including feature importance scores driving prediction.
Fairness-aware model
Model prediction of patient disposition including feature importance scores driving prediction. This model has been tuned to minimize subgroup calibration errors.