how automated healthcare fails, how you'd know, and what to do at each tier — every claim sourced, reviewed continuously
An independent validation of Epic's proprietary sepsis score found it far less accurate than the vendor reported: it missed 67% of sepsis cases while alerting on 18% of all hospitalizations.
Wong and colleagues applied the Epic Sepsis Model retrospectively to 38,455 hospitalizations of 27,697 patients at Michigan Medicine. The hospitalization-level AUC was 0.63, against the developer's reported 0.76 to 0.83. At the alert threshold of 6 the model had 33% sensitivity and 12% positive predictive value; it did not identify 1,709 of the 2,552 patients with sepsis, and it flagged only 183 septic patients (7%) whom clinicians had not already treated with timely antibiotics. Alerts fired on 6,971 hospitalizations. A 2026 multicenter validation of the redesigned version 2 found better discrimination (encounter-level AUROC 0.82 to 0.92) but still low PPV (0.13 to 0.26), high alert burden and wide variation between the four health systems.[1,2]
No individual patient harm documented in the study; the documented effect is missed detection and alert burden at scale.
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