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Hospitals can use predictive AI to scan information in electronic health records (EHRs), flag patients whose data indicate elevated risk, and send those cases to a care team for review. The score is an early-warning signal—not a diagnosis or an automatic treatment decision. Whether it helps depends on the model, the patients and setting it was designed for, and what clinicians do when an alert arrives.
How an AI risk alert works in a hospital
Predictive AI is a broad label for statistical and machine-learning systems that classify patients or estimate the risk of a defined outcome. A model may analyze information already in the EHR, such as vital signs, laboratory results, clinical notes, and longitudinal health information. It then produces a score, risk category, or alert for a specified patient group and outcome. ONC identifies use cases such as early detection of inpatient disease, fall risk, and identifying outpatients who may be at risk of readmission.
- Data are collected: The model uses patient information available in the EHR or connected clinical systems. The input data and how often they refresh vary by system.
- The model estimates risk: It compares the patient’s information with patterns associated with a defined risk, such as deterioration. A threshold may determine when the system flags the case.
- The alert enters a workflow: A score may appear in an EHR or another clinical application and be routed to a designated clinician or team.
- A care team reviews and responds: Clinicians assess the patient and decide whether further evaluation, outreach, or escalation is appropriate.
The model supplies a signal; people and the surrounding clinical process determine what happens next. A score that is not seen, understood, or acted on promptly may not support earlier intervention.
What hospitals use predictive AI to flag
Hospital systems can be designed for different populations and outcomes. Examples include identifying inpatients at risk of clinical deterioration or falls, and flagging outpatients who may need follow-up because of readmission risk. These are distinct tasks: a model for one outcome should not be assumed to identify another reliably.
Sepsis screening illustrates why a risk score needs to sit within a broader clinical process. The CDC recommends that hospitals use a standardized screening process, but says the optimal approach is unclear and does not recommend one specific tool or method. Screening may be paper-based or EHR-based and repeated at set intervals or when clinical events occur; CDC also describes multidisciplinary evaluation as part of sepsis programs.
How widely hospitals report using predictive AI
In a September 2025 report, ONC Health IT Research & Analysis said 71% of U.S. non-federal acute care hospitals reported predictive AI integrated into their EHR in 2024, up from 66% in 2023. ONC’s definition covers statistical analysis and machine learning used to classify or produce risk scores. The figures indicate reported adoption across clinical and operational uses; they do not measure model accuracy, prove that hospitals use AI specifically for sepsis or early intervention, or show that patient outcomes improved. ONC’s report on hospital use, evaluation, and governance of predictive AI provides the survey context.
What studies say about outcomes
Clinical deterioration: a 19-hospital program
Escobar and colleagues studied an automated system intended to identify adults at risk for in-hospital clinical deterioration. Its staggered deployment took place at 19 hospitals from August 2016 through February 2019. Automated real-time scores flagged patients; nurses remotely reviewed records for those at high risk and communicated findings to rapid-response teams. For patients whose condition reached the alert threshold, the adjusted relative risk of death within 30 days after an alert was 0.84 (95% CI, 0.78–0.90; P<0.001) in intervention sites compared with comparison sites. This result belongs to that model and response program, not to predictive AI generally. The study was published in the New England Journal of Medicine in 2020.
Sepsis: findings from TREWS
A 2022 prospective, multisite study of the TREWS machine-learning early-warning system reported that patients whose alerts were confirmed by a provider 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 from that study and analysis; it does not establish that confirming an alert by itself caused the better outcomes. The study is indexed at PubMed.
A hospital pilot announcement
Cleveland Clinic announced in September 2025 that a pilot of Bayesian Health’s sepsis platform helped identify more cases, reduced false alerts, and alerted clinicians earlier. Those are findings reported by Cleveland Clinic about its pilot, not an independent comparative trial. Read Cleveland Clinic’s announcement.
Why a risk score is not a diagnosis
A risk score estimates or classifies risk; it does not independently establish that a patient has a condition or determine the right treatment. The FDA’s clinical decision-support guidance includes software that “Provides a risk probability or risk score for a specific disease or condition” among functions to consider in its policy framework. The function and intended use matter, and the presence of a score alone does not establish that a system is FDA-authorized or that every such system is regulated the same way. Do not infer regulatory status for a particular hospital deployment without checking that specific system and its intended use. See the FDA’s Step 6 policy navigator.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to evaluate before relying on a hospital AI alert
There is no basis here to rank vendors or claim that all models perform equally across hospitals or patient groups. A useful evaluation focuses on whether a specific system fits the intended clinical use and whether its performance and workflow are monitored in the setting where it operates.
- Purpose and patients: What outcome is the model intended to predict, and which patients was it designed to assess?
- Inputs and timing: Which records or measurements does it use, and how current are they when an alert is generated?
- Validation: In what population and clinical environment was it evaluated, and what performance evidence applies to the hospital considering it?
- Threshold and alert burden: What triggers an alert, and how often do alerts prove unhelpful or require review?
- Response workflow: Who receives the alert, how quickly should it be reviewed, and what clinical escalation or follow-up can it trigger?
- Ongoing oversight: How does the hospital monitor performance, workflow fit, and governance after deployment?
- Regulatory status: What is the software’s specific intended function, and what regulatory status applies to that use?
These questions matter because an alerting system is not just a prediction model: its inputs, threshold, delivery, clinical response, and ongoing oversight all shape how it is used.
Quick Recap
Sources and further reading
- 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 (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, announcement on expanded rollout of Bayesian Health’s sepsis platform (September 23, 2025)
- Prospective, multi-site study of patient outcomes after implementation of TREWS, indexed at PubMed (2022)
- Agency for Healthcare Research and Quality, PSNet, Clinical Decision Support Systems
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