Human-in-the-loop artificial intelligence for drug-shortage recurrence-risk ranking and synthetic inventory-policy simulation using U.S. Food and Drug Administration Data

Abstract

Background and motivation: Drug shortages disrupt patient care and hospital operations, while public records lack inventory and procurement data. Problem statement: This study separately evaluates whether U.S. Food and Drug Administration (FDA) shortage histories can rank 90-day recurrence risk and how a status-derived risk proxy changes inventory-policy trade-offs in a synthetic simulator; it does not imply autonomous procurement. Methodology: The analysis used 1688 FDA records. Snapshot classification served only as a diagnostic. The main empirical task used a 90-day recurrence panel with historical-only features, temporal splits, leakage controls, group holdouts, and machine-learning models. Aggregate counts were compared with naive, rolling-mean, exponential-smoothing, and autoregressive integrated moving average (ARIMA) baselines. Separately, tabular Q-learning was evaluated in a disclosed synthetic inventory environment against economic order quantity (EOQ), min-max, safety-stock, threshold, and human-rule policies using stress tests, sensitivity analysis, effect sizes, and governance proxies. Findings: With 15 positive test windows, gradient boosting achieved receiver-operating-characteristic area under the curve (ROC-AUC) 0.831, precision-recall area under the curve (PR-AUC) 0.0465, and recall 0.667; logistic regression captured 40% of positives in the highest-risk 10% of observations. In simulation, Q-learning yielded a stockout rate of 0.160 versus 0.163 for min-max, with higher cost and more emergency orders. The threshold policy produced the lowest stockout rate (0.048) and greatest intervention burden. Implications: Public FDA histories are more defensible for rare-event risk ranking than high-precision prediction. Policy and governance results remain simulation-derived until validated with hospital data and qualified users.