Hospitals can connect predictive models to electronic health records (EHRs) to flag patients whose recorded information suggests rising risk. The model produces a score, category, or alert; a clinician or care team reviews that signal and decides whether to assess, contact, or escalate care. AI can help bring attention to risk sooner, but it does not diagnose a patient or decide treatment on its own.
How an AI early-warning workflow works
- Collect patient information. A model may analyze information recorded in the EHR, such as vital signs, laboratory results, clinical notes, and longitudinal health information.
- Estimate a defined risk. Statistical or machine-learning software classifies a patient or calculates a risk score for an outcome the hospital is monitoring. The output might concern inpatient deterioration, early disease detection, falls, or the risk that an outpatient will need follow-up after discharge.
- Display a score or alert. If a result reaches the system’s configured threshold, it can appear in a clinical application or EHR-integrated workflow. The threshold determines which cases are brought forward for attention; it is not itself a diagnosis.
- Route the case for human review. The alert may prompt a nurse, physician, or other care-team member to review the record, assess the patient, contact another team, or take another action under the hospital’s process.
The surrounding workflow is as important as the score. For example, a deterioration program studied by Escobar and colleagues used automated real-time scores, remote nurse review of flagged records, and communication with rapid-response teams. A signal is useful only if the right people can assess it and respond in time.
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Where hospitals use predictive AI
Predictive AI is not one single technology or task. In ONC’s survey, the term covers statistical analysis and machine learning used to classify information or produce risk scores. Reported hospital use includes inpatient applications such as early disease detection and falls, as well as outpatient follow-up, including readmission risk. A national adoption figure therefore should not be read as a measure of use for any one condition.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteFor sepsis, screening is one possible part of a hospital’s response. The Centers for Disease Control and Prevention (CDC) recommends a standardized screening process, but says the optimal approach remains unclear and does not recommend a specific tool or method. Screening may be paper-based or EHR-based and can take place at set intervals or in response to clinical events, alongside multidisciplinary evaluation.
How common is AI in hospital EHRs?
ONC Health IT Research & Analysis reported that 71% of U.S. non-federal acute care hospitals had predictive AI integrated into their EHR in 2024, up from 66% in 2023. These are reported adoption figures, not evidence that a particular model is accurate or that adoption has improved patient outcomes. They also cover clinical and operational applications, not only systems intended to identify patients for earlier intervention.
What outcome studies show—and what they do not
In-hospital deterioration
Escobar and colleagues evaluated a deterioration-identification and response program deployed in a staggered fashion at 19 hospitals from August 2016 through February 2019. Among patients who reached the alert threshold, the adjusted relative risk of death within 30 days after an alert was 0.84 for the intervention cohort compared with the comparison cohort (95% confidence interval, 0.78–0.90; P<0.001). This result concerns that model, its alert threshold, and the care-team response in those hospitals; it is not a general estimate of the effect of hospital AI.
Sepsis early warning
A prospective multi-site study of the TREWS machine-learning early-warning system reported that patients whose alerts providers confirmed within three hours had lower adjusted in-hospital mortality, organ failure, and length of stay than patients whose alerts were not confirmed within that window. The finding is an association reported by that study and analysis; it does not show that confirming an alert alone caused the better outcomes.
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A hospital implementation announcement
Cleveland Clinic reported in September 2025 that its pilot of Bayesian Health’s sepsis platform helped identify more cases, reduced false alerts, and alerted clinicians earlier. Those are findings reported by the health system about its pilot, not an independent comparative trial or proof that other hospitals would see the same results.
Why a risk score is not a diagnosis
A score indicates that recorded information matches a pattern associated with a defined risk. It does not establish that a patient has a disease, explain why the risk is elevated, or prescribe the correct response. Clinicians need to interpret it alongside the patient’s condition and other clinical information. The care pathway—including alert routing, review, and follow-up—helps determine whether a model’s signal leads to useful action.
The U.S. Food and Drug Administration (FDA) lists “Provides a risk probability or risk score for a specific disease or condition” among software functions to consider in its clinical decision-support policy navigator. FDA guidance considers a software function’s purpose and intended use; that statement does not mean every risk score is regulated as a medical device. The regulatory status of a particular hospital deployment cannot be inferred from the fact that it uses AI. Avoid describing a system as “FDA-approved” unless the authorization of that specific device has been verified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What hospitals should assess before relying on a model
A model’s usefulness depends on its intended purpose, the patients and setting for which it was developed or evaluated, and how a hospital acts on its alerts. Hospitals assessing a system should ask:
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- What risk is it meant to identify? Clarify the outcome, patient group, and care setting rather than treating “early warning” as a single use case.
- What information does it use, and how often does it update? Determine which EHR inputs feed the model and whether the timing suits the clinical decision.
- Where has it been validated? Examine whether its evaluation population and care environment fit the hospital’s own patients and workflows. Performance in one setting does not establish performance in another.
- What threshold triggers an alert? Understand how the threshold affects which patients are flagged and the volume of alerts clinicians must review. The available sources do not establish that all systems perform equally or compare their performance across demographic groups.
- Who receives the alert, and what happens next? Identify the person or team responsible for review, the response pathway, and how urgent cases are escalated.
- How will the system be monitored and governed after deployment? Review its performance and fit in the local environment over time, and establish responsibility for oversight.
- What is its regulatory status for this intended use? Check the specific software function and intended use rather than assuming all clinical risk scores share the same status.
These questions matter because an alert can be missed, arrive too late, or add to a workload without changing care. Evaluation should address both the model’s performance and whether its workflow supports timely, appropriate clinical review.
Quick Recap
Sources
- ONC Health IT Research & Analysis, Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023-2024 (September 2025).
- Escobar et al., Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration, New England Journal of Medicine (November 11, 2020).
- U.S. Food and Drug Administration, Step 6: Is the Software Function Intended to Provide Clinical Decision Support?
- Centers for Disease Control and Prevention, Hospital Sepsis Program Core Elements.
- Cleveland Clinic, Cleveland Clinic Announces the Expanded Rollout of Bayesian Health’s AI Platform for Sepsis Detection (September 23, 2025).
- Nature Medicine study indexed at PubMed, Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis (2022).
- Agency for Healthcare Research and Quality, PSNet, Clinical Decision Support Systems.
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