Case 3 · Predicting sepsis in hospitalized patients
The goal. Alert clinicians early when a hospitalized patient may be developing sepsis.
The system. A proprietary model built into a widely used electronic health record and deployed at hundreds of US hospitals. It rescored patients every 15 minutes and fired an interruptive alert above a vendor-recommended threshold. One of its inputs was whether a clinician had ordered antibiotics.
Vendor-reported performance. AUC 0.76–0.83.
Independent validation. 38,455 hospitalizations at one academic medical center; 7% developed sepsis.
| Metric | Result |
|---|---|
| AUC | 0.63 |
| Sensitivity | 33% |
| PPV | 12% |
- Generated alerts on 18% of all hospitalized patients
- Missed 67% of patients who developed sepsis
- Of 2,552 septic patients, identified 183 (7%) whose sepsis was not already being treated in time
Your task
- Vendor 0.76–0.83, independent 0.63. How does that gap happen?
- What does an alert on 1 in 5 patients do to the people receiving alerts?
- What is the effect of using “clinician ordered antibiotics” as an input?
- It added value for 7% of septic patients beyond usual care. What does that say about the right baseline?
Sources are on the main case studies page — read them after the session.