This Algorithm Accidentally Predicted Which Hospital Patients Were Most Likely To Die

The algorithm didn’t work the way it was supposed to. But it ended up having a more powerful potential: telling doctors who their sickest patients were.

Sepsis is one of the biggest hospital hazards you’ve maybe never heard of. When the body overreacts to an infection, it can trigger widespread inflammation that can in turn cause tissue damage and organ failure. It causes one-third to one-half of all deaths in US hospitals. But because sepsis’s symptoms, like fever and difficulty breathing, sometimes look a lot like other illnesses, it can be hard to detect, especially in the early stages. So a team at Banner Health, a hospital system in Phoenix, Arizona, turned to computer science for a solution. Maybe they could develop an algorithm that constantly monitored electronic health records and warned hospital staff in real time when patients were at high risk for sepsis.

Dr. Hargobind KhuranaIt didn’t work. At least, not in the way Banner had hoped for. Five years after Banner put the alert in place, it turns out to not have done a very good job of diagnosing sepsis. But the team behind it, led by Dr. Hargobind Khurana, discovered it had an unexpected upside: It was good at identifying patients who were generally much sicker than average, even if they didn’t have sepsis. Although the alert mostly failed at its main goal, it ended up having a different, perhaps even more powerful potential: steering clinicians to their most vulnerable patients.

Algorithms have infiltrated almost every part of our lives, quietly yet deftly shaping both the mundane — calendar alerts, Facebook ads, Google predictions — and the vital. One of the most critical roles algorithms play is in electronic medical record software, which hospitals and doctor’s offices use to track and manage patients’ health and illnesses. Algorithm-based alerts are supposed to point out important information hidden in mountains of data — things like when someone’s medication needs to be refilled, or when a patient has an unusually high heart rate...