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Document clinical AI use as a decision record, not as a model score. Identify the decision and intended population, record the patient data used with its source, timing and quality, show what is missing or uncertain, explain the output’s basis and validation, and record the clinician’s independent assessment. Put this information in the workflow where the decision is made, with safeguards for patient identity, monitoring and appropriate disclosure of AI involvement.
What a defensible patient-context record must accomplish
A useful note lets another clinician answer five questions without opening a separate analytics system:
- What decision was the AI intended to inform, and for which patient population?
- Which patient facts were supplied, where did they come from, and when were they measured or recorded?
- Which facts were missing, conflicting, stale or outside the tool’s intended use?
- How was the recommendation produced, and what evidence or validation supports it for this use?
- What did the responsible clinician decide after independently reviewing the patient and the output?
This is different from copying a probability, risk tier or generated paragraph into the medical record. Context must be specific enough to connect the result to the individual while preserving the clinician’s ability to decide that the information is incomplete or inapplicable.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchU.S. Food and Drug Administration (FDA) clinical-decision-support materials describe patient-specific information, relevant inputs, data-quality requirements and the ability to independently review the basis of an output. The FDA’s January 2026 final guidance is available at Clinical Decision Support Software Guidance. These are U.S. regulatory resources, not a universal note template for every country, specialty or organization.
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Start with the decision, intended use and population
Name the clinical decision
Write the decision in operational terms: for example, “support whether to obtain additional evaluation for suspected deterioration during this admission” or “estimate recurrence risk to support a shared discussion of follow-up options.” Avoid a vague statement such as “AI used for care.” A reviewer needs to know what action the output was supposed to inform and what it was not designed to decide.
Identify the user and point in care
Record who used the tool (such as a treating clinician, pharmacist or care coordinator), the care setting, and the time at which the output was viewed. The same model can have different implications in an emergency department, outpatient visit, inpatient handoff or remote-monitoring queue.
State the target population and exclusions
Document the population described by the tool’s instructions or validation, including relevant age range, disease status, care setting or other inclusion criteria. Note exclusions and populations in which performance has not been established. Do not describe a narrowly validated function as if it were a general-purpose model.
The FDA’s Step 6 policy navigator treats intended use and patient population as information relevant to independent review.
Record every decision-relevant input and its provenance
For each input that could change the recommendation, capture four elements: the value, its source, its date or time, and why it matters to this decision. A structured field is preferable when the value is numeric or categorical; a short narrative can explain clinical meaning or conflicts.
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| Input element | What to document | Example |
|---|---|---|
| Patient fact | Exact value or finding, including units or status | Oxygen saturation 91% on room air |
| Source | Origin of the data and person or system responsible for entry | Bedside pulse oximeter; nurse observation |
| Timestamp | When measured, collected or last updated | 2026-10-17 09:20 UTC |
| Relevance | Why the input is decision-relevant | Indicates current respiratory status for escalation assessment |
| Quality status | Reliability, plausibility, calibration or known limitations | Repeat measurement after poor waveform; value accepted |
| Conflicts | Different values or accounts that the clinician resolved or left unresolved | Home medication list differs from pharmacy fill history |
Include the required medical inputs identified in the tool’s documentation and explain how they should be obtained. The FDA guidance emphasizes the relevance of inputs and their data-quality requirements; a list of fields without provenance is not enough. Link the tool’s instructions or technical documentation in your local system when policy permits, but keep the patient-specific facts in the clinical record.
Make missing, uncertain and stale information visible
Separate known facts from information that is unknown, unavailable, contradictory or potentially outdated. Do not let a blank field look like a negative finding. Use explicit labels such as:
- Known: verified in the current encounter or a reliable recent source.
- Unknown: not assessed or not available.
- Unavailable: expected data could not be retrieved because of a system, access or timing problem.
- Conflicting: credible sources disagree and the discrepancy remains unresolved.
- Stale: present in the record but older than the time window appropriate for this decision.
- Not applicable: the item does not pertain to this patient or decision.
For each material gap, state its likely effect on interpretation and whether the clinician plans to obtain the information. For example: “Recent renal function unavailable; the risk estimate may be unreliable for medication selection. Repeat laboratory testing ordered.” If the tool produced an output despite missing data, record that fact and whether the output was used, qualified or disregarded.
The FDA policy navigator specifically instructs users to “Provide relevant patient-specific information and other knowns/unknowns for consideration.” See FDA Step 6.
Explain the recommendation’s basis so it can be reviewed
Describe method and data in plain language
State whether the output came from a rules-based calculator, statistical model, machine-learning model, generative system or another method, if that information is available and relevant. Identify the principal data sources or reference material used by the system. A clinician does not necessarily need source code, but does need enough information to understand what the output represents.
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Report the output with its units, time and scope
Record the result exactly as presented, including score or probability, category thresholds, confidence or interval information, and the time generated. Distinguish a prediction from a recommendation and a recommendation from an order. If the system provides several options, preserve the alternatives and the conditions attached to them.
Include validation information when applicable
Summarize the clinical population, setting, outcome and validation date if the vendor or organization provides them. Note whether validation matches this patient and use. Do not convert a validation result from one hospital, demographic group or workflow into a guarantee for another. If validation information is unavailable, say so rather than implying that the tool has been proven for the current case.
Record the clinician’s independent assessment
Document agreement, modification or rejection of the output and the clinical reasons. A concise entry might say: “The tool classified risk as high. I reviewed current vital signs, medication exposure and imaging; the patient is clinically stable and the classification appears driven by an outdated laboratory value. I will not use the score to trigger escalation.” This preserves human review instead of treating the AI result as an order.
Use a workflow-friendly note structure
Clinical decision support should be clear, well organized and fit the provider’s workflow. The Office of the National Coordinator for Health Information Technology (ONC) discusses these principles at Clinical Decision Support. A template can reduce omissions, but it cannot determine whether a value is clinically appropriate.
A practical documentation sequence
- Open the decision record at the point of use. Select the clinical decision, encounter and patient before launching or viewing the AI function.
- Confirm patient identity. Match at least the organization’s approved identifiers and verify that the displayed encounter and location are correct.
- State intended use and population. Enter the decision, user, setting, target population and relevant exclusions.
- Review inputs before accepting the output. Check value, source, timestamp, units and quality for every required or high-impact input.
- Mark gaps and conflicts. Use explicit unknown, unavailable, conflicting or stale statuses; add a plan for resolving material gaps.
- View the output with its basis. Capture method, reference data or logic, validation information and any warnings shown by the system.
- Record independent review. State what the clinician accepted, changed or rejected and why.
- Document the resulting action. Link the decision to an order, monitoring plan, referral, discussion or deliberate decision not to act.
- Finalize and preserve the audit trail. Record the version or release identifier when available, the generation time and the identity of the reviewer.
Keep the template short enough to complete during care. Put technical details in an accessible information panel or linked record rather than forcing clinicians to copy long model descriptions into every note.
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Copyable note template
AI-supported decision: [decision and intended action]
User/setting/time: [role], [care setting], [date and time]
Intended population and exclusions: [population; exclusions or out-of-scope factors]
Patient inputs:
- [fact] — source: [source]; measured/recorded: [time]; relevance: [why it matters]; quality: [status]
- [fact] — source: [source]; measured/recorded: [time]; relevance: [why it matters]; quality: [status]
Knowns/unknowns/conflicts: [list each material gap and its effect]
AI output: [exact result, units/category, generated time]
Basis and validation: [method, key data/reference, validation population/date, limitations]
Clinician review: [agree/modify/reject; independent reasoning]
Action and follow-up: [care action, monitoring, communication, unresolved data to obtain]
AI disclosure or policy field: [completed when required by organizational policy]Choose a documentation approach deliberately
No single schema is mandated for every specialty or jurisdiction. Compare approaches against the information a reviewer needs, not against how quickly a form can be completed.
| Approach | Strengths | Risks and controls |
|---|---|---|
| Free-text note | Captures nuanced reasoning and unusual context | Inconsistent terminology and omitted fields; use a short prompt or smart phrase with required headings |
| Structured fields | Consistent provenance, timestamps and missingness; supports audit and analytics | Can oversimplify clinical meaning; provide a narrative comment for conflicts and exceptions |
| Template embedded in the EHR workflow | Places context beside the decision and can require high-impact fields | Alert fatigue or click burden; test with users and make low-value fields optional |
| Integrated CDS display with linked explanation | Shows inputs, output and rationale at the point of care | Hidden data transformations or stale feeds; expose source, time, version and warnings |
Assess each design for completeness and provenance of inputs, visibility of uncertainty, clarity of method and validation, workflow fit, and patient-identification and monitoring safeguards.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect patient identity and the AI lifecycle
An accurate model applied to the wrong patient is a safety failure. Confirm identity before data retrieval and again before committing the output to the record. Investigate duplicate records, merged charts, wrong-encounter selections and copied-forward context. Record corrections visibly rather than silently overwriting a prior result.
ONC’s SAFER Guides address reliable patient identification and the design, implementation, use and monitoring of CDS and orders; the revised guide also addresses AI-enabled systems. Use the guides to assign ownership for interface testing, downtime procedures, alert review, change control and post-deployment monitoring.
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Version and change control
Capture the model or ruleset version, release date when supplied, input-mapping version and generation timestamp. If a tool changes, determine whether prior outputs remain interpretable and whether the organization must revalidate, notify users or update the template. A later reviewer should be able to distinguish a result generated under an earlier version from a current result.
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Monitoring after deployment
Monitor for missing-input rates, unexpected input distributions, identity mismatches, overrides, delayed alerts and outcomes relevant to the intended use. Review performance across the populations in which the tool is used, while recognizing that local monitoring does not establish universal effectiveness. Define an escalation path for suspected model error or unsafe behavior.
Document AI involvement according to risk and policy
Whether to disclose AI use in the medical record depends on the system’s impact, the decision, professional standards and applicable law or organizational policy. The American Medical Association’s Principles for Augmented Intelligence Development, Deployment, and Use recommend risk- and impact-based transparency and support documenting AI use when it directly affects patient care, access, medical decision-making or the record. Treat this as professional policy guidance; requirements vary by jurisdiction and organization.
When AI directly influences care, include a plain statement of its role, the clinician responsible for the decision and the independent review performed. Do not imply that disclosure transfers responsibility to the software or that a disclosure alone validates the recommendation. Follow local rules for patient communication, consent, privacy, retention and access.
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FDA classification depends on the particular software function and statutory criteria. Documentation of patient context alone does not make a system non-device software. The FDA’s Clinical Decision Support Software Guidance and Clinical Decision Support Software FAQ should be reviewed with regulatory and legal experts for the actual function.
Some patient-facing functions that organize and communicate health information may fit a distinct policy example, but that does not mean every patient-facing AI tool is unregulated. Describe what the software actually does, who uses it, what inputs and outputs it handles, and how the clinician can independently review it.
Common documentation failures and fixes
- Only the score is saved. Add intended use, inputs, timestamps, missingness, basis and clinician reasoning.
- “No data available” hides several conditions. Distinguish unknown, unavailable, conflicting and stale information.
- Copied-forward context is treated as current. Display collection time and require confirmation for high-impact inputs.
- Model population is omitted. Record inclusion criteria, exclusions and whether this patient is outside validated use.
- A recommendation is mistaken for an order. Label output type and document the clinician’s resulting action separately.
- Rationale is inaccessible. Provide a plain-language explanation and a route to method, data and validation details.
- Templates create alert fatigue. Require only decision-critical fields, test completion time and monitor override patterns.
- Identity checks occur only at login. Reconfirm patient and encounter at data retrieval and result filing.
- AI use is disclosed without accountability. Name the responsible clinician and record independent review and follow-up.
Pre-signature checklist
- Decision, intended action, user, setting and time are stated.
- Patient population, exclusions and out-of-scope factors are identified.
- Each material input has a value, source, timestamp, relevance and quality status.
- Unknown, unavailable, conflicting and stale information is labeled explicitly.
- Output, units or category, generation time and model or ruleset version are recorded when available.
- Method, key data or references and applicable validation limitations are understandable to the reviewer.
- Patient identity and encounter were verified.
- The clinician’s independent agreement, modification or rejection is documented.
- The resulting action, monitoring and unresolved information needs are clear.
- AI disclosure and retention requirements under local policy are satisfied.
Used consistently, this approach turns an opaque AI event into a reviewable clinical decision record: what was considered, what was not known, what the system contributed and why the clinician acted.
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