AI-Enabled IT and Supply Chain Resilience for Anticipating and Mitigating Critical Medical-Supply Disruptions in U.S. Healthcare Systems

Abstract

Critical medical supplies in the United States are disrupted by hurricanes that close a dominant plant, by quality shutdowns of sterile-injectable lines, by demand surges, by logistics delays and, increasingly, by cyber incidents that remove a health system's ordering and visibility systems. This article asks how much an AI-enabled early-warning and response layer, combined with information technology (IT) resilience, can reduce the harm of such disruptions for a regional health system, and at what cost. A transparent synthetic testbed of eight hospitals (2,150 beds), six critical items, a distributor with allocation rules, secondary sources and a spot market is subjected to seven disruption classes drawn from documented U.S. events. Five strategies are compared on identical episodes: independent reorder points (S0), system-wide visibility with rule-based alerts (S1), AI early warning with a gradient-boosted demand forecaster and supply-risk scorer calibrated to a false-alert budget (S2), S2 with max-min allocation, transshipment and tiered conservation (S3), and S3 with an IT resilience runbook (S4). Over 20 seeds, unmet demand for the affected items under a 90-day manufacturing outage fell from 23.7% (S0) to 9.9% (S2) and 0.5% (S3); under a four-fold demand surge from 38.3% to 26.3% and 6.8%; under a quality shutdown from 20.0% to 0.3% and 0.0%. Early warning preceded the onset in about a third of outage episodes and half of quality shutdowns; the gains came from acting on the first signals within the distributor's order cap, from pre-qualified secondary sources and from conservation. A 28-day cyber outage alone cost little, but the same outage during a supply disruption erased most of the AI layer's advantage (S2: 12.9%, S3: 2.8%) unless the runbook kept the system working (S4: 0.8%). One-factor sweeps and a 200-draw Monte Carlo analysis rank the levers: order caps, secondary-source lag, conservation effectiveness, outage length and manual throughput. A reference architecture, response workflow and phased roadmap mapped to U.S. policy and guidance are provided. All data are synthetic; the numbers describe the testbed, not any real health system.