AI incident ·
Purportedly AI-Generated Sepsis Alert Reportedly Prompted Potentially Inappropriate IV Fluid Administration for a Dialysis Patient, Averted by Clinician Intervention
In brief
An AI system built by Unknown sepsis alert model developer and Unknown healthcare technology and deployed by St. Rose Dominican Hospital (Henderson, Nevada) and 2 others allegedly harmed St. Rose Dominican Hospital (Henderson, Nevada) and 5 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 1 report
What happened
A nurse at St. Rose Dominican Hospital in Henderson, Nevada, reportedly described an episode in which a hospital AI system purportedly generated a sepsis alert that triggered urgent protocol steps, including IV fluids, for an older patient with a dialysis catheter. Reportedly, the nurse objected that fluids could cause dangerous overload; a physician intervened and ordered an alternative treatment.
Editor's notes
Timeline note: The reported near-harm episode occurred "a few years" before publication; since no specific incident date is provided, the incident date is set to 02/17/2026 (Scientific American report publication date) as a fallback. The incident ID was created 02/21/2026.
Laws that address this harm
Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (1)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- St. Rose Dominican Hospital (Henderson, Nevada) Nurses Medical personnel
- Alleged developer
- Unknown sepsis alert model developer Unknown healthcare technology
- Alleged harmed party
- St. Rose Dominican Hospital (Henderson, Nevada) Patients Nurses Medical personnel Epistemic integrity Doctors
AI systems implicated
Unknown sepsis alert technologyUnknown healthcare technologyAI-enabled decision support systems
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.3.
- Reliability issues
"Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...
- Type 2: Bigger than expected
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
- Type 3: Worse than expected
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
- Ethics and Morality Issues
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
- Safe learning
"AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."
- Malign belief distributions
"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...
- Meta-cognition
"Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...
- Technical and operational risks
"To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...
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Source record: incident #1374 on the AI Incident Database · all 1 report