Lydia T. Liu

Hospitals use readmission-risk predictions to decide who leaves first, but outcomes are recorded only for discharged patients, so retraining a predictor on that data can lock in censoring bias. Coupling a queueing-based Markov decision process with a periodically retrained risk predictor, the work finds that a discharge-up-to policy, which sets a target census and discharges by priority only when occupancy exceeds it, spreads observed lengths of stay through natural arrival variability. In synthetic experiments and calibrated simulations built on patient-level data from a partner hospital, the policy reduced learning error by 24%–25% relative to thresholding on risk and shrank baseline-adjusted readmission cost disparities by 38%–63%.

Hospitals use readmission-risk predictions to decide who leaves first, but outcomes are recorded only for discharged patients, so retraining a predictor on that data can lock in censoring bias. Coupling a queueing-based Markov decision process with a periodically retrained risk predictor, the work finds that a discharge-up-to policy, which sets a target census and discharges by priority only when occupancy exceeds it, spreads observed lengths of stay through natural arrival variability. In synthetic experiments and calibrated simulations built on patient-level data from a partner hospital, the policy reduced learning error by 24%–25% relative to thresholding on risk and shrank baseline-adjusted readmission cost disparities by 38%–63%.