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There is no universal data checklist for hospital AI risk models. The right inputs depend on what the model predicts, when and where it is used, who acts on its output, and which patients it serves. Hospitals should select and validate data for that specific context, then protect it through purpose-limited access, appropriate privacy and security controls, and oversight throughout the model’s lifecycle.
What data might a hospital AI risk model use?
A risk model uses information to estimate a defined outcome—for example, inpatient deterioration, readmission, early disease detection, or a missed appointment. Those tasks do not call for the same inputs. A variable’s presence in an electronic health record (EHR) does not, by itself, show that the variable is accurate, complete, representative of the patients being served, or appropriate for a particular use.
Depending on the outcome and workflow, candidate information may include:
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- Measurements: laboratory results and vital signs, with attention to when and how they were recorded.
- Care and medication history: medications, procedures, and other treatment-related information.
- Utilization and timing: encounter history, care transitions, and the timing or frequency of events relevant to the prediction.
- Demographic or social-context information: features that may be relevant to the defined task and population, subject to careful assessment of their purpose and effects.
These are possible categories, not a recommended universal feature set. The model’s target, prediction horizon, intended user, point in the care process, and patient population should guide selection. ONC’s Decision Support Interventions resource describes predictive interventions and examples including ASCVD, eGFR, APACHE IV, and LACE+. A model name or published example does not establish that its development data represent a hospital’s local patients; model developers may not have access to the training data needed to describe that representativeness.
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Define the model’s purpose before choosing inputs
Start with a documented context of use. Be specific about the outcome, the time horizon, when the prediction is generated, who receives it, what action the recipient may take, and the population in which the model will be evaluated and used. A risk score is not meaningful in isolation: its usefulness and potential harms depend on how it enters a clinical or operational workflow.
For models covered by applicable certified health IT transparency requirements, ONC’s HTI-1 Final Rule overview and Decision Support Interventions resource describe information intended to help clinical users assess predictive interventions, including their fairness, appropriateness, validity, effectiveness, and safety. Applicability depends on the product and regulatory context; these resources are not a universal feature checklist for every hospital model.
Documentation should make it possible for clinical and technical reviewers to understand:
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- the intended outcome, user, workflow, and patient population;
- where inputs came from and how their quality, completeness, and timing were assessed;
- which patients were represented in development and in local evaluation, and what is unknown about that representation;
- how missing or changing data are handled, and what limitations are known; and
- who owns the model and how updates, changes, and concerns are reviewed.
How should hospitals protect patient privacy?
The privacy approach should match the project’s purpose, the data pathway, who will have access, and the applicable legal and contractual obligations. The guidance below concerns U.S. federal HIPAA rules; state law, institutional policies, contracts, and other requirements may also apply. HIPAA does not apply identically to every organization or holder of health data.
Set purpose and access boundaries
Identify why patient information is needed and which people or systems need it. 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 rule has exceptions, and its application depends on the circumstances. HHS explains the principle and its qualifications in its Minimum Necessary Requirement guidance and Summary of the HIPAA Privacy Rule.
Covered entities also need privacy procedures, staff training, assigned responsibility, and protections against access by people who do not need the records. Make access decisions explicit rather than treating broad access to an EHR or analytic environment as necessary simply because the data are technically available.
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Choose a data pathway deliberately
De-identification is one way to disclose information under HIPAA, not a requirement for every model project or a substitute for considering all applicable privacy obligations. HHS recognizes two HIPAA de-identification methods:
| Method | What it requires |
|---|---|
| Expert Determination | A person with appropriate knowledge and experience applies accepted statistical and scientific principles, determines that the risk of identification is very small in the anticipated recipient context, and documents the method and result. |
| Safe Harbor | Remove the specified identifiers and have no actual knowledge that the remaining information could identify an individual. |
HHS cautions that “Both methods, even when properly applied, yield de-identified data that retains some risk of identification.” Assess uniqueness, likely linkage sources, the recipient, release conditions, and whether repeated releases or changing outside information could affect risk. See HHS OCR’s guidance on HIPAA de-identification for the methods and their limits.
Protect systems, people, and data flows
A security risk analysis should cover the systems, people, data flows, and safeguards involved in developing and operating a model. HHS OCR’s Guidance on Risk Analysis points regulated entities to the Security Rule risk-analysis requirement and the ONC/OCR Security Risk Assessment Tool, while cautioning that its guidance is not a one-size-fits-all blueprint.
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Encryption can help protect electronic PHI (ePHI), but key protection matters too. HHS explains that ePHI encrypted using an accepted process can be considered unusable to unauthorized people when the confidential decryption process or key has not been breached. Its guidance on rendering unsecured PHI unusable, unreadable, or indecipherable explains the relevant conditions. Encryption alone does not replace a broader assessment of access, systems, and risks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate accuracy, fairness, and safety
Evaluation should address whether the model is fit for its intended use—not just whether it produces a promising headline accuracy figure. ONC’s predictive decision support risk-management provisions identify validity, reliability, robustness, fairness, intelligibility, safety, security, and privacy as dimensions to analyze and mitigate. They also address governance of data acquisition, management, and use. The ONC Decision Support Interventions resource describes this framework.
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For a hospital’s decision, useful questions include:
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- Validity and reliability: Does the model predict the defined outcome in the local setting, and are results dependable under the conditions of use?
- Data fit: Are the inputs sufficiently accurate, complete, timely, and appropriate for the patients and workflow being assessed?
- Fairness: Does performance differ across relevant patient groups, and could those differences affect care or operations?
- Robustness and safety: How does the model behave when data patterns or operating conditions change, and what could happen if its output is wrong?
- Intelligibility and workflow: Can intended users understand the output well enough to use it appropriately in the actual process?
- Privacy and security: Are the information flows and safeguards proportionate to the purpose and risks?
NIST’s voluntary AI Risk Management Framework FAQs encourage consideration of trustworthiness across design, development, deployment, use, and testing or evaluation. This lifecycle perspective complements ONC’s risk dimensions; neither should be mistaken for proof that a particular model is effective or appropriate in a particular hospital.
Monitor models after deployment
Evaluation does not end when a model is put into use. Hospitals can assign an owner and define a process for reviewing performance shifts, changes to inputs or workflows, subgroup results, and potential harms. A practical governance arrangement can bring together clinical, privacy, security, data, and operational perspectives, with local validation and a change-management process. This is a practical way to address the risk dimensions and lifecycle concerns above, not a single committee structure mandated for every hospital.
ASTP/ONC’s September 2025 Data Brief 80 reports survey findings from non-federal acute care hospitals, based on the 2023 and 2024 AHA Information Technology supplements:
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|---|---|
| Predictive AI integrated with the EHR | 71% in 2024, compared with 66% in 2023 |
| Evaluated predictive AI for accuracy | 82% in 2024 |
| Evaluated predictive AI for bias | 74% in 2024 |
| Conducted post-implementation evaluation or monitoring | 79% in 2024 |
| Reported multiple entities accountable for predictive AI evaluation | 74% in 2024 |
The brief defines predictive AI as statistical analysis and machine learning used to classify or produce an individual risk score. These are hospital-reported survey findings, not evidence that every model was assessed or that evaluation and monitoring were effective. The brief also reports that fewer hospitals evaluated all or most of their models and includes “don’t know” responses. ONC’s 2025 SAFER Guides add AI-enabled systems to organizational-responsibility guidance for patient-care administration, diagnosis, treatment, and management.
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