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Effect of Predictive Model on ED Physician Assessments of Patient Disposition

NCT06434220 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 10

Last updated 2026-04-13

No results posted yet for this study

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.

Conditions

  • Patient Outcome Assessment

Interventions

DIAGNOSTIC_TEST

Baseline model

Model prediction of patient disposition including feature importance scores driving prediction.

DIAGNOSTIC_TEST

Fairness-aware model

Model prediction of patient disposition including feature importance scores driving prediction. This model has been tuned to minimize subgroup calibration errors.

Sponsors & Collaborators

Study Design

Allocation
RANDOMIZED
Purpose
OTHER
Masking
TRIPLE
Model
SEQUENTIAL

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2027-01-01
Primary Completion
2027-05-01
Completion
2027-09-01

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Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06434220 on ClinicalTrials.gov