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

Responded
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
123
Report Count
5
Incident Date
2021-08-01
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

123

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

Epic's widely used sepsis prediction model falls short among Michigan Medicine patients+1
An Epic Failure: Overstated AI Claims in Medicine
Artificial Intelligence Can Improve Health Care—but Not Without Human Oversight+1
Algorithms Are Making Decisions About Health Care, Which May Only Worsen Medical Racism
Epic's widely used sepsis prediction model falls short among Michigan Medicine patients

Epic's widely used sepsis prediction model falls short among Michigan Medicine patients

fiercehealthcare.com

An Epic Failure: Overstated AI Claims in Medicine

An Epic Failure: Overstated AI Claims in Medicine

mindmatters.ai

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

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

pewtrusts.org

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

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

aclu.org

Epic overhauls sepsis algorithm

Epic overhauls sepsis algorithm

beckershospitalreview.com

Epic's widely used sepsis prediction model falls short among Michigan Medicine patients
fiercehealthcare.com · 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
mindmatters.ai · 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
pewtrusts.org · 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
aclu.org · 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
beckershospitalreview.com · 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…

Variants

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.

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