There is no universal data checklist for hospital AI risk models. The right inputs depend on what the model predicts, when and where it runs, who acts on its output, and which patients it is intended to serve. Hospitals should document the data used, limit access to what the project needs, assess privacy and security risks, and evaluate the model in its local clinical or operational setting—not assume that data in an electronic health record are automatically suitable or safe.
What data do hospital AI risk models need?
Start with the model’s context of use, not with a list of fields an electronic health record happens to contain. Define the outcome, the time horizon, the point in the workflow when the model runs, the person or system expected to act, and the patient population for which it is intended. A model for inpatient deterioration, readmission, early disease detection, missed appointments, or treatment recommendations may need different information because each addresses a different decision.
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ONC defines a Predictive Decision Support Intervention as technology that uses models derived from training data to produce an output such as a prediction, classification, recommendation, evaluation, or analysis. Its examples include ASCVD, eGFR, APACHE IV, and LACE+. The examples are not a universal prescription for hospital inputs, and the availability of a model’s original training data may be limited. A model’s name or presence in a clinical system does not by itself establish that its training population represents the hospital’s patients. ONC’s Decision Support Interventions resource
Potential input categories—not a required checklist
Depending on the outcome and workflow, a model might use information from one or more of these broad categories:
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- Clinical history: diagnoses, prior conditions, and relevant patient history.
- Measurements: laboratory results and vital signs, including their timing and availability.
- Care history: medication, procedure, and health-care utilization information.
- Timing and workflow: when information was recorded, how recently a patient used services, and other context relevant to the prediction point.
- Demographic or social context: features that may be relevant to the intended outcome, provided their use is justified and assessed for fairness and appropriateness.
These categories are possibilities, not a recommendation to include every field. A feature can be present in the record yet be incomplete, outdated, inconsistently recorded, or an unsuitable proxy for the outcome. The hospital should be able to explain why each input is used and whether it would actually be available at the time the model makes its prediction.
What reviewers should be able to learn about the data
Model documentation should make it possible for clinical and technical reviewers to understand the intended use, where the inputs came from, their quality and completeness, which population was represented in development and local evaluation, how missing values and updates are handled, and what limitations are known. ONC’s transparency requirements for predictive interventions in certified health IT are intended to give clinical users information to assess factors including fairness, appropriateness, validity, effectiveness, and safety. ONC’s HTI-1 Final Rule overview
How should hospitals protect patient privacy?
Privacy safeguards begin with a defined purpose and carefully bounded access. The details depend on the project and the applicable legal, contractual, and institutional requirements. The guidance below describes U.S. federal HIPAA sources; state law and other rules may impose additional obligations, and HIPAA does not apply identically to every holder of health information.
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Set the purpose and access boundaries
Identify why the data are needed, which people or systems need access, and what information is necessary for the work. Under HIPAA, covered entities generally must take reasonable steps to limit uses, disclosures, and requests for protected health information (PHI) to the minimum necessary for the intended purpose. The Privacy Rule has exceptions, so its application depends on the circumstances; the principle should not be reduced to a blanket rule that every data use must be minimized in the same way. HHS OCR’s Minimum Necessary Requirement guidance
HIPAA’s Privacy Rule also requires covered entities to maintain privacy procedures, train staff, assign responsibility, and safeguard records against access by people who do not need them. HHS OCR’s Summary of the HIPAA Privacy Rule
Choose a data pathway deliberately
For data disclosed as de-identified under HIPAA, HHS recognizes two methods:
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- Expert Determination: A qualified person with appropriate knowledge and experience applies accepted statistical and scientific principles, determines that the chance of identification is very small in the anticipated recipient context, and documents the method and result.
- Safe Harbor: The specified identifiers are removed, and the holder has no actual knowledge that the remaining information could identify an individual.
HHS cautions that even properly de-identified data retain some risk of identification. Risk assessment should consider uniqueness, likely linkage sources, who will receive the data, release conditions, and whether repeated disclosures or new outside data could change the risk. De-identification is one possible pathway, not a requirement for every model project and not, on its own, a resolution of every privacy obligation. HHS OCR’s HIPAA de-identification guidance
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A security risk analysis should cover the systems, people, data flows, and safeguards involved in building and operating the model. HHS OCR points regulated entities to the Security Rule risk-analysis requirement and the ONC/OCR Security Risk Assessment Tool, while cautioning that guidance is not a one-size-fits-all blueprint. HHS OCR’s Guidance on Risk Analysis
Encryption can help protect electronic PHI (ePHI), but key management matters too. HHS explains that ePHI encrypted using an accepted process can be treated as unusable to unauthorized people when the confidential decryption process or key has not been breached. Protect the keys and decryption process as part of the control, rather than treating encryption alone as the whole security plan. HHS OCR’s guidance on rendering unsecured PHI unusable, unreadable, or indecipherable
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How can a hospital tell whether a model is suitable and fair?
A single headline accuracy score cannot establish that a risk model is fit for a hospital’s intended use. The hospital should evaluate the model against the outcome it is meant to predict, the local population and workflow, and the consequences of acting—or not acting—on its output. Relevant questions include:
- Validity and reliability: Does the model measure or predict the intended outcome, and are results dependable in the setting where it will be used?
- Robustness: How does it behave when inputs are incomplete, data patterns shift, or systems and workflows change?
- Fairness: Does performance differ across patient groups relevant to the hospital’s population, and could the model’s inputs or errors contribute to inequitable decisions?
- Intelligibility and workflow fit: Can intended users understand the output well enough to use it appropriately, and is it clear what action the output should inform?
- Safety, security, and privacy: What harms could follow from an incorrect or misused output, and are the information and systems protected?
These dimensions align with ONC’s predictive decision-support risk-management provisions. NIST’s AI Risk Management Framework offers a voluntary lifecycle framework for considering trustworthiness during design, development, deployment, use, and testing or evaluation. NIST’s AI Risk Management Framework FAQs
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What do hospital surveys show about predictive AI oversight?
ASTP/ONC’s September 2025 Data Brief 80 reports hospital survey findings for 2024. In this report, predictive AI refers to statistical analysis and machine learning used to classify or produce an individual risk score. The integration estimate is for non-federal acute care hospitals; the other figures below are also hospital-reported survey results, not proof that every model was assessed or that the assessment was effective. ASTP/ONC, Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023–2024
| Reported measure | Survey finding |
|---|---|
| Predictive AI integrated with the EHR among non-federal acute care hospitals | 71% in 2024, compared with 66% in 2023 |
| Hospitals reporting evaluation for accuracy | 82% in 2024 |
| Hospitals reporting evaluation for bias | 74% in 2024 |
| Hospitals reporting post-implementation evaluation or monitoring | 79% in 2024 |
| Hospitals reporting multiple entities accountable for predictive AI evaluation | 74% in 2024 |
The brief also found that fewer hospitals evaluated all or most of their predictive AI models, and survey respondents included “don’t know” answers. The percentages therefore describe reported hospital practices, not a guarantee of coverage for a particular tool or of effective oversight.
How should oversight continue after deployment?
Deployment does not end the evaluation. ONC’s 2025 SAFER Guides add AI-enabled systems to organizational-responsibility guidance covering patient-care administration, diagnosis, treatment, and management. ONC’s SAFER Guides
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A hospital can translate the official risk dimensions into a lifecycle process with clear ownership and scheduled review. In practice, that can mean involving clinical, privacy, security, data, and operational expertise; validating locally before use; monitoring performance and relevant subgroup outcomes; documenting changes; and revisiting the assessment when the model, data, patient population, or workflow changes. NIST describes lifecycle risk management as voluntary, while ONC’s predictive intervention provisions set out risk-management expectations for their applicable scope.
For a particular model, compare options using the same questions: what outcome and context each serves; where its inputs come from and how complete and representative they are; what local validity and accuracy evidence exists; how it performs across relevant patient groups; whether its output fits the workflow; what privacy and security exposure it creates; and what monitoring and governance will continue after launch. Without model-specific evidence on those points, there is no sound basis for ranking named models.
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