Incident 123: Epic Systems’s Sepsis Prediction Algorithms Revealed to Have High Error Rates on Seriously Ill Patients

Description: Epic System's sepsis prediction algorithms was shown by investigators at the University of Michigan Hospital to have high rates of false positives and false negatives, allegedly delivering inaccurate and irrelevant information on patients, contrasting sharply with their published claims.


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Alleged: Epic Systems developed an AI system deployed by University of Michigan Hospital, which harmed sepsis patients.

Incident Stats

Incident ID
Report Count
Incident Date
Sean McGregor, Khoa Lam
Epic's widely used sepsis prediction model falls short among Michigan Medicine patients · 2021

Among roughly 38,500 hospitalizations, researchers said a proprietary sepsis prediction algorithm developed by Epic missed two-thirds of sepsis patients and generated numerous false alerts. While the EHR vendor attributed the weak performan…

An Epic Failure: Overstated AI Claims in Medicine · 2021

Epic Systems, America’s largest electronic health records company, maintains medical information for 180 million U.S. patients (56% of the population). Using the slogan, “with the patient at the heart,” it has a portfolio of 20 proprietary …

Artificial Intelligence Can Improve Health Care—but Not Without Human Oversight · 2021

Every year 1.7 million adults in the United States develop sepsis, a severe immune response to infection that kills about 270,000 people. Detecting the disease early can mean the difference between life and death.

One of the largest U.S. de…

Algorithms Are Making Decisions About Health Care, Which May Only Worsen Medical Racism · 2022

Artificial intelligence (AI) and algorithmic decision-making systems — algorithms that analyze massive amounts of data and make predictions about the future — are increasingly affecting Americans’ daily lives. People are compelled to includ…

Epic overhauls sepsis algorithm · 2022
Naomi Diaz post-incident response

Epic has made changes to its sepsis prediction model in a bid to improve its accuracy and make its alerts more meaningful to clinicians.

An Epic spokesperson told Becker's in an emailed statement that it began the development of its new sep…


A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.