To govern AI feedback, corrections, and learning loops in ABA, capture each report with the exact input, output, source, model, user, affected record, and consequence. Separate user feedback from authoritative correction, repair every affected downstream state, and decide explicitly whether the case may support evaluation, prompt changes, fine-tuning, or vendor reporting. Protect sensitive examples, resist poisoning, revalidate changes, and close the reporter's loop.

Define Celia's AI feedback-to-correction and learning-disposition ledger

Celia separates a thumbs-down signal, free-text report, affected-person concern, source-record correction, reviewer adjudication, incident, test case, prompt change, retrieval change, fine-tuning example, vendor support ticket, and production release. These objects have different authority and privacy rules. The operating question is how to learn from failures without converting every report into truth, every correction into training permission, or every repaired output into repaired downstream work.

Record the decisions and evidence that release depends on

The AI feedback-to-correction and learning-disposition ledger records feedback ID and channel, reporter and role, affected person and record, use case, input and source version, output, model and configuration, observed problem, severity, immediate hold, evidence, authoritative reviewer, correction and rationale, affected downstream artifacts, privacy class, minimum-necessary analysis, vendor disclosure, evaluation eligibility, training or improvement permission, poisoning or abuse signal, change link, test, release, reporter response, owner, age, and closure. Structured fields support assignment, comparison, alerts, expiry, testing, and reconciliation. Narrative explains the real workflow, affected people, clinical and operational consequence, access needs, uncertainty, source limits, failed tests, and the accountable owner's disposition.

Run the implementation in a controlled sequence

Celia provides a low-friction accessible intake, preserves the exact failure, and triages safety, privacy, security, clinical, payer, employment, and financial consequences to the right owner. A qualified reviewer establishes the authoritative correction. The team repairs copied fields, messages, records, submissions, and decisions before considering product learning. Approved examples enter a controlled evaluation set; a separate change process governs prompts, retrieval, models, or fine-tuning and requires regression testing.

Keep the standard, platform, and decision boundaries visible

The NIST AI RMF and final Generative AI Profile support ongoing feedback, evaluation, monitoring, and change management as voluntary risk practices. The FTC AI privacy statement warns companies to honor privacy and confidentiality commitments when using customer data for new purposes. These sources do not make user feedback a clinical record, authorize vendor training, decide minimum necessity, or establish that a corrected example represents every future case.

Use five release gates

  • Every report binds to the exact use case, source, output, version, person, consequence, and reporter route.
  • User feedback, authoritative correction, incident classification, affected-record repair, and product change remain separate.
  • Evaluation, vendor disclosure, fine-tuning, and other improvement uses have explicit privacy and permission decisions.
  • Poisoned, abusive, conflicting, duplicated, and mass-submitted feedback is tested without silencing valid concerns.
  • Changes trace to locked cases, independent regression tests, approvals, monitoring, reporter response, and downstream closure.

Handle a realistic complication

A staff member may correct an AI-generated payer summary in the interface while the incorrect value has already populated a task and draft submission. Celia preserves the original, repairs both downstream artifacts, notifies the accountable owners, and prevents the edited display from masquerading as full closure.

Protect care, communication, records, and access

Celia traces effects from the AI feedback-to-correction and learning-disposition ledger to safety, clinical work, communication and AAC, privacy, records, authorizations, claims, payroll, payments, family contact, and accommodations. Urgent safety, incident, and reporting work proceeds through its own authority. A qualified clinician decides whether clinical services can proceed after a material technology failure; each other accountable owner decides within that role's scope.

Work through a fictional practice example

Celia locks 46 fictional feedback items. Thirty-four have exact evidence, authoritative disposition, downstream repair, privacy decision, learning status, owner, reporter response, and closure proof. Three lack source versions, two contain conflicting corrections, one appears poisoned, two were sent to a vendor without review, and four repaired the display but not copied fields. Seven repair; five remain open. This fictional scenario tests the control and denominator. It supports no conclusion about a real practice, person, product, legal duty, clinical outcome, payer decision, or security posture.

Measure the full locked cohort

Celia's initial readiness is 34 of 46, or 73.9%. The report retains all 46 feedback items due, including failed, unknown, skipped, expired, prohibited, and unresolved work. It states the lock date, review cutoff, reasons, owners, and age. Systems, people, records, events, attempts, findings, tests, and remediation actions keep separate denominators.

Test the failure modes that matter

Celia tests ordinary report, anonymous concern, accessibility barrier, duplicate report, conflicting corrections, malicious feedback, mass submission, wrong-person case, severe safety issue, vendor escalation, prohibited training use, repaired display with stale downstream field, regression failure, rollback, and reporter closure. Each case preserves the system and version, starting state, data, identity or process, expected result, observed result, raw evidence, defect, owner, retest, and disposition. A passed case applies only to the named configuration and conditions.

Avoid the failures that create false confidence

A feedback button can create false confidence when it collects sensitive content, treats popularity as truth, hides severe failures in aggregate ratings, or improves one route while leaving affected records and decisions wrong. Weak programs count ratings without cases, let any user overwrite authoritative facts, promise learning that never occurs, send PHI to vendors through support fields, retain examples forever, train on unadjudicated or poisoned data, ship a change without regression tests, and close the ticket before correcting downstream harm.

Require independent acceptance

Celia gives an independent reviewer the AI feedback-to-correction and learning-disposition ledger, locked scope, source map, configuration, raw evidence, failures, approvals, monitoring, remediation, and closure proof. The reviewer reproduces an ordinary path, a severe failure path, and the final denominator. A changed cohort, hidden manual repair, missing record, or undocumented dependency fails acceptance.

Place the implementation inside current healthcare duties

Celia uses the CASP public organizational overview only for high-level business, clinical-operations, and risk context. The HHS risk-analysis guidance requires a regulated entity's risk analysis to reach all ePHI it creates, receives, maintains, or transmits. Neither source validates this AI feedback-to-correction and learning-disposition ledger, a product, a clinical workflow, or a legal conclusion.

Keep current and proposed rules separate

Celia checks the current HHS Security Rule summary before release. As of August 24, 2026, that page still identifies the January 2025 cybersecurity update as proposed. The page therefore maps current duties and voluntary readiness sources separately and does not state proposed requirements as operative law.

Use each technical source within its stated scope

Celia's page-specific sources are National Institute of Standards and Technology, AI Risk Management Framework, National Institute of Standards and Technology, Generative AI Profile, National Institute of Standards and Technology, AI Test, Evaluation, Validation and Verification, U.S. Department of Health and Human Services, Minimum Necessary Requirement, U.S. Department of Health and Human Services, Business Associates, Federal Trade Commission, AI privacy and confidentiality commitments. Each retains its stated date, version, sector, status, and limits. The practice still verifies actual entity role, data, configuration, contract, accessibility, clinical authority, payer rules, state law, and deployed evidence.

Maintain the control after release

Celia assigns the AI feedback-to-correction and learning-disposition ledger a review cadence and event triggers for systems, data, identities, versions, configurations, vendors, workflows, incidents, contracts, law, and ownership. Material changes reopen affected gates and tests. This page remains draft until the named technology, privacy, security, clinical, accessibility, records, payer, and legal reviewers complete their work.

Related resources

Sources