AI Scribe Pilot Planning Guide

AI Scribe Pilot Planning Guide

An AI scribe pilot should be a versioned evidence protocol, not a timed trial, a vendor demonstration, or a promise that a tool will save time. Before any collection starts, the buyer should freeze the exact function, configured system, cohort, comparator, measures, case set, human authority, data boundary, stop conditions, and decision expiry. The final record should say what the evidence can support, what it cannot support, and where the result applies.

This article is for healthcare technology research and procurement planning. It is not medical, clinical, legal, billing, coding, reimbursement, compliance, statistical, or financial advice. Qualified clinical, patient-safety, privacy, security, HIM, EHR, analytics, legal, compliance, revenue-cycle, procurement, and statistical owners should decide which requirements apply and whether the local evidence is adequate.

Use this protocol alongside the AI for Clinical Documentation solution overview. The solution page describes the broader category; this page is limited to the evidence contract for a bounded local pilot.

Gate 1: freeze the decision, exact function, and cohort

A useful protocol begins with one decision and one reproducible boundary. “Evaluate an AI scribe” is not precise enough. Capture, transcription, speaker attribution, summarization, draft-note generation, patient instructions, coding suggestions, order suggestions, EHR write-back, and patient communication are different functions with different inputs, outputs, users, downstream actions, and risks.

The protocol should give each function its own row and identify which rows are in scope. Record the exact product, model, prompt, template, capture method, client or application, EHR interface, environment, and configuration version. Record prohibited actions and the human decision that remains outside the tool.

Define the cohort with the same precision. Name the site, specialty, language, encounter type, clinician or user group, participant rules, inclusion criteria, exclusion criteria, and enrollment evidence. A result from one configuration or cohort should not silently authorize another.

The NIST AI Risk Management Framework 1.0 supports documenting context of use, intended and unintended impacts, AI actors, human oversight, measurement limits, monitoring, and risk treatment. It does not prescribe a medical validation method or acceptance threshold. As of this review, AI RMF 1.0 is under revision, so the protocol should retain the exact source version and a recheck date.

The FDA Clinical Decision Support Software final guidance is January 2026 final guidance. The FDA policy for device software functions is September 2022 final guidance. Both require function-specific analysis of intended use, intended user, inputs, outputs, and decision role; neither supports a product-level conclusion from the marketing label “AI scribe.”

Evidence required:

• Protocol ID, version, status, created date, approved date, effective date, expiry date, and accountable owner.
• One decision question and the intended users and uses of the result.
• Function-by-function inventory of input, output, downstream action, prohibited action, and human authority.
• Exact product, model, prompt, template, capture method, client, EHR interface, environment, and configuration versions.
• Site, specialty, language, encounter type, users, participant rules, inclusion criteria, exclusion criteria, and enrollment source.
• Named owners for clinical safety, frontline workflow, HIM or CDI, privacy, security, EHR operations, analytics, compliance, procurement, and statistical review.

Stop rule:

• Stop if a marketing category substitutes for an exact function or intended use.
• Stop if the cohort cannot be reconstructed from the enrollment evidence.
• Stop if an exclusion changes without an amendment and analysis reset.
• Stop if a human authority or prohibited action is not named.
• Stop if historical outputs cannot be tied to their configuration version.

Gate 2: route readiness and conditional applicability

The protocol should route prerequisites to qualified owners before live use. It should not compress privacy, security, certified-health-IT, regulatory, payer, or authentication questions into one “compliant” checkbox. Each branch needs the facts that make it relevant, the source and version used, the owner’s scope-specific determination, open items, and a recheck trigger.

HHS Office for Civil Rights states in its cloud-computing guidance that business-associate obligations can apply when a cloud service provider creates, receives, maintains, or transmits ePHI on behalf of a covered entity or business associate, including when the provider lacks the decryption key. That is a conditional entity-and-data-flow question, not proof that every buyer, vendor, or data flow is covered.

The HHS OCR sample business-associate agreement provisions support asking about permitted uses, safeguards, incident reporting, subcontractors, access, return or destruction, and termination. A BAA is not a product certification and does not prove the configured workflow is secure, safe, or lawful.

Where the HIPAA Security Rule applies, the HHS OCR risk-analysis guidance supports inventorying ePHI, threats, vulnerabilities, existing safeguards, likelihood, impact, and ongoing review. It does not supply one universal control set or residual-risk threshold.

The ASTP/ONC Decision Support Interventions Certification Companion Guide is DSI CCG v1.10, last updated May 5, 2026. It supports a conditional inquiry into the exact certified module, criterion, function, supplier, output, and decision role. The category name does not make every AI scribe a Predictive DSI, and it does not establish that a third-party function is supplied by a certified developer.

The HTI-1 DSI fact sheet summarizes source-attribute and risk-management evidence for in-scope Predictive DSIs supplied as part of certified health IT. It is not a universal model-card requirement and is not proof that a function is valid for a local cohort.

The 2025 SAFER Organizational Responsibilities guide supports multidisciplinary responsibility, local inventory, training, monitoring, issue reporting, and explicit stop authority. SAFER is an organizational self-assessment and recommended-practices resource, not product validation or a legal conclusion.

Evidence required:

• Readiness owner matrix with decision authority, evidence owner, escalation owner, and stop authority.
• Training plan tied to the exact configured function and prohibited actions.
• Actual entity, purpose, recipient, data-flow, hosting, subprocessor, access, retention, deletion, backup, and recovery facts.
• Conditional HIPAA, BAA, risk-analysis, de-identification, DSI, FDA, CMS, recording, notice, consent, and other-law owner records.
• Version, status, publication date, access date, expiry, and recheck trigger for each time-sensitive source.
• Open questions recorded as open — owner/evidence required rather than favorable assumptions.

Stop rule:

• Stop if a conditional source is turned into a universal compliance, certification, device, clearance, approval, coverage, coding, or payment claim.
• Stop if a BAA, contract signature, questionnaire, or vendor assertion is treated as proof that controls operated.
• Stop if local recording, notice, consent, jurisdiction, entity-role, or payer facts lack an accountable owner.
• Stop if a material readiness item is unknown but marked complete.
• Stop if unresolved privacy, security, clinical-safety, data-integrity, accessibility, continuity, or legal evidence is treated as a weighted score.

Gate 3: reconstruct the baseline and comparator

The pilot needs a local current-state baseline and a declared comparator. Vendor estimates, customer stories, and generic benchmarks are context, not a buyer baseline. The comparator may be a current workflow, concurrent comparison, staged rollout, or another locally justified design, but its ability to support descriptive, associational, or impact conclusions should be stated before collection.

The AHRQ Workflow Assessment for Health IT Toolkit supports mapping workflow before selection, before implementation, and after use. Use separate current-state, proposed-state, and observed-state maps. Include tasks, owners, handoffs, waits, corrections, support work, and work displaced to another role.

The AHRQ Health IT Evaluation Toolkit supports defining goals, stakeholders, feasible measures, sources, collection methods, limitations, and analysis concerns before collection. It is a 2009 archived evaluation resource, not a current AI-scribe policy or benchmark.

The CDC Program Evaluation Framework, 2024 supports describing context and program logic, predeclaring intended users and uses, matching evaluation questions to design, documenting data quantity and quality, stating limitations, and acting on findings. It is nonprescriptive and does not make a simple before-and-after dashboard causal.

Evidence required:

• Current-state workflow with tasks, roles, handoffs, queues, waits, review, rework, support, and exception paths.
• Proposed-state workflow for the exact configured system.
• Comparator choice, rationale, observation windows, concurrent changes, and conclusion-strength label.
• Identical definitions for baseline and pilot measures unless a documented amendment explains the difference.
• Source extracts or queries that allow a reviewer to reproduce the cohort and missing cases.
• Record of selection, attrition, seasonality, learning effects, novelty, staffing, and other plausible confounders.

Stop rule:

• Stop if a vendor projection or industry value is used as the local baseline.
• Stop if baseline and pilot cohorts, tasks, units, or windows are not comparable.
• Stop if hidden review, correction, training, support, or rework is excluded from one side.
• Stop if a simple difference is described as caused by the product without a design that supports that claim.
• Stop if the comparator cannot be reconstructed by a reviewer who did not run the project.

Gate 4: freeze metric provenance and the analysis plan

Each metric needs a definition before anyone sees the result. A label such as “accuracy,” “time saved,” “adoption,” or “documentation quality” is not a metric until its claim boundary, numerator, denominator, unit, exclusions, source, extraction method, missingness, validation, reviewer, uncertainty, and action rule are declared.

Operational measures should remain separate from clinical, documentation-integrity, coding, payment, financial, and causal claims. A faster draft does not prove a complete note. A signature does not prove correctness. Use of a feature does not establish benefit. Absence of a reported incident does not prove that no incident occurred.

The NIST AI RMF Generative AI Profile, NIST AI 600-1, July 2024, supports context-matched pre-deployment testing, documentation of measurement limits, retention of test and version history, monitoring of errors and near misses, and avoiding extrapolation from narrow tests. It does not provide an AI-scribe accuracy or acceptability threshold.

Evidence required:

• Metric ID, decision question, result lane, definition, claim it can support, and claim it cannot support.
• Numerator, denominator, unit, direction, cohort, exclusions, window, and missingness categories.
• Source system, query ID, query version, timestamp, extract location, validation step, and owner.
• Sampling frame, sample-selection method, reviewer qualifications, review instructions, disagreement process, and uncertainty statement.
• Threshold owner, rationale, action tied to the threshold, and rules for incomplete evidence.
• Predeclared analysis plan and amendment history.

Stop rule:

• Stop if the numerator, denominator, query, cohort, or exclusion rule is undefined.
• Stop if a dashboard cannot be reproduced from its source system and query ID.
• Stop if unknown is converted to zero or a missing case is silently removed.
• Stop if a threshold is chosen after the result is known without an amendment and limitation.
• Stop if operational observations are converted into clinical, coding, payment, revenue, ROI, or causal conclusions without appropriate evidence.

Gate 5: test expected, exception, identity, write-back, and downtime cases

A pilot should test more than ordinary encounters. The case matrix should include an expected case, an excluded case, incomplete or ambiguous capture, unsupported detail, omission, disputed output, clinician correction or rejection, wrong patient or encounter, and duplicate, stale, late, partial, or failed write. It should also include AI unavailable, EHR unavailable, network loss, queued work, manual fallback, restart and downstream reconciliation.

For every case, record the expected behavior, observed behavior, protocol and system versions, reviewer, evidence link, detection method, correction, downstream effect, and result as pass, fail, conditional, or open. A failure should be detectable and reversible without an undocumented vendor action.

The NIST Cybersecurity Framework 2.0 supports governance, identification, protection, detection, response, recovery, supplier risk, current and target profiles, and restoration-integrity evidence. It defines outcomes, not a specific AI-scribe control set.

The 2025 SAFER Contingency Planning guide supports testing planned and unplanned unavailability, substitute workflow, communications, interface stop and restart, recovery, training, and reconciliation. It does not supply the buyer’s outage threshold or vendor guarantee.

The 2025 SAFER System Management guide supports configuration inventory, interface documentation, clinician-involved validation, maintenance, monitoring, and testing of change effects. An API claim does not prove configured write-back is complete, lossless, or safe.

The 2025 SAFER Patient Identification guide supports verifying that displayed and entered information is bound to the correct patient and testing duplicate, mix-up, and comingled-record risks. It does not prove a product’s identity, routing, or speaker-attribution accuracy.

Evidence required:

• Case ID, case family, scope version, configuration version, expected behavior, and evidence source.
• Expected, excluded, incomplete, ambiguous, unsupported-detail, omission, dispute, correction, identity, routing, write-back, and unavailable-system cases.
• Detection point, responsible reviewer, correction path, downstream impact, affected-record identification, and final state.
• Manual fallback, queue handling, stop authority, restart criteria, recovery proof, and reconciliation record.
• Coverage rationale showing why the case set is adequate for the declared decision and scope.

Stop rule:

• Stop if the case set consists only of a vendor demo or happy path.
• Stop if a failure cannot be detected, attributed to a version, corrected, or reconciled.
• Stop if the configured workflow can create a wrong-patient, wrong-encounter, duplicate, late, partial, or stale record without a controlled response.
• Stop if recovery depends on an undocumented vendor action.
• Stop if case coverage is asserted without a buyer-owned rationale and accountable reviewer.

Gate 6: preserve review, correction, authentication, and deviations

The record model should distinguish draft, final, amended, and entered-in-error states. Preserve the original output, evidence visible to the reviewer, edit or rejection, responsible reviewer, authentication event, amendment, and downstream correction. A generic “human in the loop” statement is insufficient unless the protocol shows where review occurs, what the reviewer can inspect, what authority the reviewer retains, and how disagreement is recorded.

The CMS MLN guide to Medicare signature requirements, July 2025, states in the cited Medicare context only that the provider using a scribe, including AI, signs the entry to authenticate the documentation and care provided or ordered. This is not a universal payer, service, order, jurisdiction, or organization rule, and signature does not prove accuracy, completeness, medical necessity, coding correctness, or payment.

Every protocol deviation should be visible. Record whether a user, encounter, version, case, data source, window, threshold, or procedure departed from the approved plan, why it happened, who approved the response, which outputs were affected, and whether the analysis or approval should reset.

Evidence required:

• State model for draft, final, amended, and entered-in-error records.
• Original output, reviewer-visible source evidence, edit or rejection, reviewer identity, authentication, timestamp, and correction propagation.
• Reviewer sampling log, reviewer disagreement, adjudication owner, adjudication reason, and final disposition.
• Protocol deviation log with affected case, configuration, metric, analysis impact, approver, and reset decision.
• Incident and near-miss record linked to affected outputs and downstream systems.

Stop rule:

• Stop if AI output becomes authoritative without a named responsible reviewer and review evidence.
• Stop if the organization treats authentication as proof of content quality.
• Stop if original output, edits, rejections, amendments, or downstream corrections cannot be reconstructed.
• Stop if reviewer disagreement is averaged away without adjudication.
• Stop if a material protocol deviation is hidden, retroactively normalized, or excluded without explanation.

Gate 7: analyze results, adjudicate disagreement, and state limits

Analyze only the cohort, configuration, window, and measures declared in the protocol or a traceable amendment. Preserve missingness and negative findings. Report process, implementation, outcome, impact, economic, and safety questions separately so one positive observation does not imply another.

Conclusion strength should match the design. Useful labels include descriptive only, observed association, impact estimate with stated assumptions, vendor assertion, buyer-modeled scenario, and open. Do not use “caused by,” “saved by,” “avoided by,” or equivalent language when the comparator, design, confounding controls, or analysis owner cannot support it.

Vendor evidence should retain product, model, prompt, population, language, specialty, setting, workflow, integration, comparator, missingness, and limitations. When those do not match the pilot, transferability remains open rather than becoming a favorable assumption.

Evidence required:

• Frozen cohort and data extract, protocol and configuration versions, analysis script or method, and analysis owner.
• Missing, excluded, unavailable, disputed, corrected, and protocol-deviation counts by declared denominator.
• Reviewer-agreement and adjudication record with limitations.
• Comparator findings, concurrent changes, selection effects, attrition, workflow fidelity, and uncertainty.
• Result labels that match the design and do not imply clinical, coding, payment, financial, or causal proof.
• Separate statements of what the result supports, what it does not support, and where transferability is open.

Stop rule:

• Stop if vendor results are relabeled as buyer-observed outcomes.
• Stop if a narrow specialty, language, capture method, workflow, or configuration is generalized without evidence.
• Stop if material missingness, attrition, disagreement, incident, or deviation is omitted.
• Stop if the analysis cannot be rerun from the frozen data, definitions, and method.
• Stop if a positive process metric is presented as clinical effectiveness, documentation integrity, payment, revenue, ROI, or enterprise readiness.

Gate 8: stop, roll back, reconcile, and expire the decision

The closing decision is not simply “pilot successful.” Use pass, fail, conditional approval, or open. Record the approved scope and configuration, unresolved evidence, required monitoring, stop conditions, rollback target, correction plan, expansion exclusions, expiry, and recheck trigger. A conditional approval should identify the condition, owner, evidence due date, and consequence if the condition is not met.

Hard stops remain independent of benefit metrics. Privacy, identity, record integrity, responsible authentication, clinical safety, auditability, recovery, accessibility, continuity, and legal failures cannot be offset by time, adoption, price, or projected savings. A pilot decision does not authorize a different site, specialty, language, encounter type, function, model, prompt, template, interface, data flow, or downstream action.

Evidence required:

• Hard-stop register with trigger, evidence, owner, authority, action, residual issue, and recheck date.
• Named pause and disable authority, fallback owner, rollback target, restart approval, and communication path.
• Affected-record inventory, correction status, downstream reconciliation, data return or deletion, and evidence preservation.
• Final decision label, approved scope, excluded scope, conditions, limitations, open evidence, monitoring, expiry, and recheck trigger.
• Separate expansion proposal with a new protocol or amendment when any boundary changes.

Stop rule:

• Stop if a material hard stop remains unresolved or lacks an accountable authority.
• Stop if new processing cannot be paused or the prior workflow cannot be restored.
• Stop if affected records cannot be enumerated, corrected, and reconciled.
• Stop if the final decision has no approved boundary, limitation, expiry, or recheck trigger.
• Stop if pilot completion is treated as enterprise or expansion approval.

Build seven buyer-owned pilot artifacts

These artifacts are blank evidence structures. They are not official forms, certifications, or universal validation templates. The buyer should adapt them to the declared decision and retain links to the underlying evidence.

A. Pilot charter and decision question

Record:

• Protocol ID, version, status, created date, approved date, effective date, and expiry.
• Decision question, intended users, intended use of findings, owner, approvers, and recheck trigger.
• Exact function, configuration, cohort, exclusions, prohibited actions, human authority, and stop authority.
• Evidence standard for pass, fail, conditional approval, and open.

B. Scope, cohort, exclusion, and frozen-configuration matrix

Record:

• Site, specialty, language, encounter type, user group, participant rules, inclusion, exclusion, and enrollment source.
• Product, model, prompt, template, capture method, client, EHR interface, environment, retention setting, and subprocessor versions.
• Function input, output, downstream action, authoritative system, human decision, and prohibited action.
• Conditional applicability owner records and approval-resetting fields.

C. Baseline, comparator, and metric-provenance dictionary

Record:

• Current, proposed, and observed workflow with task, owner, handoff, wait, correction, support, and exception.
• Comparator, rationale, baseline and pilot windows, concurrent changes, and conclusion-strength label.
• Metric ID, definition, numerator, denominator, unit, direction, cohort, exclusions, and missingness categories.
• source system, query ID, query version, extraction timestamp, QA step, sampling method, analysis owner, uncertainty, and action rule.

D. Expected, exception, identity, write-back, and downtime case matrix

Record:

• Case ID, family, protocol version, configuration version, expected behavior, observed behavior, and evidence link.
• Expected, excluded, incomplete, ambiguous, unsupported-detail, omission, disputed, correction, wrong-patient, wrong-encounter, duplicate, stale, late, partial, failed-write, and unavailable-system cases.
• Detection, reviewer, correction, affected record, downstream effect, fallback, restart, reconciliation, and result.
• Case-coverage rationale and accountable reviewer.

E. Review, correction, and adjudication record

Record:

• Draft, final, amended, and entered-in-error state transitions.
• Original output, evidence visible at review, edit or rejection, responsible reviewer, authentication, and correction propagation.
• Sampling frame, sample selection, reviewer disagreement, adjudication owner, reason, final disposition, and limitations.
• Incident, near miss, support issue, and affected-output links.

F. Run, deviation, incident, change, stop, and rollback log

Record:

• Run ID, protocol ID, system versions, start and end time, user, cohort, case set, and evidence location.
• Protocol deviation, incident, affected metric or output, owner, response, analysis effect, and reset decision.
• Change type, source, notice date, regression set, classification, approver, effective date, and historical output trace.
• Stop trigger, disable action, fallback, rollback target, affected-record inventory, restart approval, downstream reconciliation, and closure evidence.

G. Evidence-to-decision packet

Record:

• Frozen extracts, metric results, missingness, uncertainty, reviewer agreement, adjudication, deviations, incidents, and limitations.
• Decision label, rationale, evidence links, approved scope, excluded scope, conditions, and unresolved evidence.
• Monitoring owner, next review, expiry, recheck trigger, material-change reset, and expansion exclusions.
• Claims the decision can support and claims it cannot support.

Keep the protocol blank until local evidence exists

Every material input starts blank. The default state is open — owner/evidence required. A blank field is not a favorable assumption. The protocol should distinguish unknown, not collected, not observed, not applicable, and measured zero.

The rules are strict:

• unknown is not 0.
• not observed is not pass.
• Absence of a reported incident is not proof of zero incidents.
• Not applicable requires an owner, rationale, evidence, and reviewed scope.
• A vendor statement is not buyer-observed evidence.
• A signed note is not proof of content accuracy.
• A completed pilot is not approval for a different function, cohort, configuration, or site.

The duration, sample size, case coverage, and decision threshold should come from the declared question, cohort, expected and exception coverage, missingness, data quality, design, and accountable clinical, operational, analytics, and statistical owners. Do not start with a 30-, 60-, or 90-day default. Do not substitute a vendor or industry benchmark for a local method and rationale.

No official source cited here supplies a universal accuracy, hallucination, unsupported-detail, omission, speaker-attribution, edit-rate, adoption, time-saving, after-hours, burnout, documentation-quality, coding, denial, payment, revenue, ROI, duration, sample-size, or statistical-power benchmark.

Map data, record states, and conditional applicability

The data register should separate audio or local buffer, transcript, prompt and context, draft note, signed note, edit history, telemetry, support copy, and audit log. For each item, name the authoritative copy, system, recipient, entity role, purpose, location, subprocessor, access, retention and deletion, backup, recovery, and evidence owner.

Do not call data de-identified without a documented basis. The HHS OCR de-identification guidance describes the Privacy Rule’s Expert Determination and Safe Harbor methods. Removing names, redacting visible text, using a vendor label, or using synthetic-looking content does not by itself establish that audio, transcripts, drafts, or logs are de-identified. De-identification also does not resolve recording, notice, security, contract, data-quality, or other-law questions.

Keep these branches conditional:

• HIPAA and BAA: route from actual entities, purpose, and data flow. A BAA is not a product certification.
• Security Rule risk analysis: route only where applicable, retain the local scope and accountable owner, and recheck after changes or incidents.
• De-identification: record the method and evidence; do not infer status from appearance or product claims.
• ONC DSI: record the exact certified module, criterion, supplier, function, output, and decision role. The category does not make every AI scribe a Predictive DSI.
• FDA: route each software function by intended use, intended user, input, output, and downstream action. The guidance does not classify every AI scribe.
• CMS signature: retain the payer, service, document, and organization facts. The AI-scribe statement applies in the cited Medicare context only.
• Recording, notice, consent, state law, other jurisdictions, payer policy, coding, and payment: assign qualified local owners; the federal materials cited here do not provide one universal answer.

The record-state map should show when content is a draft, who can review or reject it, what evidence the reviewer sees, how it becomes final, how authentication is recorded, how amendments and entered-in-error states work, and how corrections propagate to every downstream copy.

Reset approval when the configured system changes

Track every change to model, prompt, template, language, specialty, capture method, client, EHR interface, workflow, intended use, intended user, output, downstream action, data field, retention setting, subprocessor, and hosting or recovery arrangement. Classify it as non-material, review-required, or approval-resetting before it takes effect.

The change register should include:

• Change ID, source, notice date, old version, new version, reason, affected functions, and affected cohorts.
• historical output trace showing which version produced each retained output.
• Risk and metric impact, regression corpus, expected results, reviewers, observed results, and unresolved findings.
• Approval class, approver, effective date, user communication, and monitoring change.
• Decision on whether baseline, comparator, sampling, analysis, conditions, or expiry must reset.

The NIST Generative AI Profile supports retaining test, override, error, near-miss, negative-impact, change, recovery, and decommissioning evidence. The SAFER System Management guide supports testing interface and configuration changes with clinicians and frequent users. Neither provides a default regression threshold.

Material hard stops include unresolved privacy, identity, record integrity, responsible authentication, clinical safety, audit, recovery, accessibility, continuity, or legal evidence. Those failures cannot be offset by productivity, adoption, price, or projected savings.

When a stop occurs, preserve the trigger, owner, authority, time, system state, affected-record inventory, correction status, communications, fallback, rollback target, restart approval, downstream reconciliation, data return or deletion, and retained evidence; pilot completion is not expansion approval, and an expired decision should return to open until re-reviewed.

Handoff to adjacent decision pages

This page owns the AI-scribe-specific evidence protocol. It receives inputs from adjacent pages and returns a bounded evidence packet without absorbing their decisions.

• The vendor evaluation guide owns vendor evidence and comparison. The pilot receives candidate claims and limitations; it does not rank vendors or approve procurement.
• The workflow implementation guide owns configured production workflow, operating roles, handoffs, monitoring, downtime, and long-term change control. The pilot freezes the workflow being tested; it does not replace the operating guide.
• The security review owns security control evidence, architecture, access, logging, supplier, incident, and recovery findings. The pilot records a dated dependency; it does not award a security badge.
• The compliance questions page owns qualified applicability decisions for entities, jurisdictions, notice, consent, contracts, documentation, and other obligations. The pilot keeps the owner record and open items; it does not give legal advice.
• The buyer checklist owns procurement completeness, evidence inventory, roles, and signoffs. The pilot returns test evidence; it does not duplicate the procurement checklist.
• The ROI model owns financial reconciliation, full cost, scenarios, and attribution boundaries. The pilot may provide local observations and limitations; it does not calculate ROI, payment, or revenue.
• The pricing questions page owns commercial terms, units, minimums, renewal, implementation, support, and exit terms. The pilot should not negotiate or compare price.
• The tool shortlist owns shortlist criteria. A pilot result does not automatically change the shortlist or endorse a product.
• The general healthcare AI pilot guide is the cross-category pilot entry point. This page remains the AI-scribe-specific evidence protocol for audio, transcript, draft, review, authentication, identity, write-back, and record correction.

Evidence boundary

This page synthesizes 19 official evidence entries from AHRQ, CDC, NIST, HHS OCR, ASTP/ONC, CMS, and FDA. The sources support evaluation structure, workflow observation, context and risk documentation, data and contract questions, conditional applicability routing, organizational responsibility, contingency testing, system management, patient identification, authentication context, and function-specific regulatory inquiry.

They do not establish product performance, local workflow fit, clinical effectiveness, documentation quality, legal applicability, compliance, coding correctness, payment, revenue, ROI, a universal pilot duration, a sample size, or an acceptance threshold. No product test, patient or clinician interview, local dataset, contract review, named reviewer, regulatory determination, or pilot result is claimed here.

Source versions matter. The AHRQ evaluation toolkit is archived. The CDC framework is the 2024 nonprescriptive framework. NIST AI RMF 1.0 is the current cited baseline but is under revision. The GenAI profile is NIST AI 600-1, July 2024. CSF 2.0 defines cybersecurity outcomes, not certification. DSI CCG v1.10 is conditional on exact certified-health-IT facts. The SAFER guides are self-assessment resources. CMS MLN905364 is a dated Medicare signature source. FDA routing remains function-specific.

A defensible closeout retains the source title, owner, version or status, publication or update date, accessed date, exact use, limitation, placement, and recheck trigger. If those fields or any material local input are missing, the status remains open — owner/evidence required.