Draft for Public Comment · v1.0

The Ascerta Provenance Standard (APS)

A Proposed Standard for Recording AI Decisions in Prior Authorization and Other Utilization Management

Health plans increasingly use artificial intelligence to make or shape coverage decisions: whether a requested service is approved, denied, reduced, or delayed. A growing body of law now governs how these decisions are made, requires that they remain under accountable clinician oversight, and requires that they be auditable after the fact. What the law does not specify is how a plan is to prove any of it.

This profile defines what must be recorded, and kept tamper-evident, for every coverage decision an AI system makes or shapes in a role certified to the Standard, so that each decision can be independently reconstructed, inspected, and challenged. It states why the record is necessary, what must be captured, and the healthcare-specific requirements the general standard leaves to a domain: human accountability, reviewer competence, and the routing of every non-approval. It assumes no particular pipeline: it applies identically where a model issues the determination, where models extract and summarize while deterministic code decides, and to architectures neither of those describes. Section 4 states the requirement in terms that hold for any of them. It is published as a draft for public comment, and we invite health plans, vendors, regulators, clinicians, and researchers to review it, apply it, and help refine it.

The keywords MUST, MUST NOT, SHOULD, SHOULD NOT, and MAY carry their conventional RFC 2119 meanings.

Regulatory CrosswalkRead the Research
1

Why the record is necessary

An AI coverage decision cannot be trusted unless it can be reconstructed and examined after the fact. Four properties of these systems make that impossible unless the right record is captured at the moment the decision is made. Each is the reason for a specific capture requirement later in this document.

1.1 The model cannot be read after the fact

A modern AI system is a large neural network. Unlike ordinary software, it is not written as explicit rules a person can inspect; it is trained, and its behavior emerges from billions of numerical parameters adjusted during that training. Those who build and run these systems can see what a model outputs but cannot, in general, explain why it produced one output rather than another.

This is not a fringe claim; it is acknowledged by the frontier developers themselves. Anthropic describes its own models as a black box: something goes in and a response comes out, and it is not clear why the model gave that particular response instead of another. The research effort to change this, mechanistic interpretability, is real, but does not yet let anyone reconstruct why a model reached a particular decision by examining the model itself.

The decision cannot be recovered from the model. It can only be recovered from a record of what went into it.

1.2 The model's stated reasons cannot be trusted

It is tempting to think this is solved by having the model explain itself. It is not. Peer-reviewed research has established that a language model's stated reasoning is frequently unfaithful: the explanation it writes does not reflect the factor that actually drove its answer. Turpin et al. (2023) showed that a model can be influenced by something — the order of options, a bias in the prompt — and then produce a fluent, plausible rationale that never mentions that influence. The rationale is written to look reasonable, not to report the true cause.

For coverage the consequence is direct: a denial can cite the correct clinical criteria and read as sound while the real driver, a cost signal or a utilization target, goes unstated. The record therefore cannot rest on the model's explanation. It must preserve the inputs and conditions that allow the decision to be checked independently.

1.3 The same case can produce a different decision

An AI decision is a function not only of the clinical facts but of the exact conditions under which it was produced: what the model was told and how it was worded, which model and version answered, the sampling settings, the tools it could call, the documents and policy in its context, and any memory it retrieved. Changing a single one of these — a word of framing, a figure in the context, an item of retrieved memory — can flip a decision that should not have changed. A decision can only be shown to have been made properly if all of these conditions were recorded.

1.4 The record is the only thing that makes a decision challengeable

An affected member, a reviewer, or a regulator can only contest a decision they can see into, and adding an AI system does not suspend that right. Litigation over automated post-acute coverage tools has turned on precisely this failure: denials that patients could neither understand nor contest because the basis was never disclosed or reconstructable, with a federal court observing that affected patients had no way to challenge the process that produced them.

A conformant provenance record is what makes a coverage decision challengeable.

2

Decisions in scope

Normative

When a deployment is certified under this Profile, a conformant provenance record MUST be produced, and held tamper-evident, for every decision an AI system makes or shapes of the following kinds:

  • Prior-authorization approvals, denials, and partial approvals.
  • Reductions, step-downs, and level-of-care or duration decisions.
  • Concurrent and continued-stay reviews.
  • Step-therapy and site-of-care determinations.
  • Any medical-necessity determination, and any confidence, priority, or scoring signal that shapes one.

No decision in scope may be exempted after the fact. A determination an AI system made or shaped does not leave scope by being labeled advisory, minor, preliminary, or subordinate to a later human sign-off. Where an AI system's output shaped what a human was asked to decide, the record MUST be produced.

A decision that pends, holds, or defers is in scope and MUST be recorded as such. Any outcome that is not a full approval — a denial, a partial approval, a reduction, a pend, or a routing of the case onward — is treated as a non-approval and carries the additional requirements of Sections 5 and 6.

Approvals are in scope
The majority of determinations in a typical deployment are approvals issued without human involvement, and they are the least examined. A standard that covered only adverse outcomes would leave the largest automated population unrecorded and would make the approval/denial boundary, the thing most worth watching, invisible.
3

What must be captured

Normative

The principle is simple: the record must account for everything that could have influenced the decision, so that no unrecorded influence is possible. A system that decides each request on its own, with no memory or outside context, has a short and easily met list. A system that pulls in memory or additional context takes on more to capture, because that content shaped the decision and is exactly where a hidden or injected instruction would enter unseen.

Content is bound by digest. Standing material — policy text, deployment configuration — is stored once and referenced by a hash that pins the exact version in force; only what is specific to the decision is recorded per decision.

3.1 The evidence manifest

A determination is made against a set of documents, and that set changes over time. Records are faxed in late. Notes are amended, addended, or signed after the fact — all of which are lawful and routine. Without a sealed record of the set, there is no way to distinguish a document the reviewer saw from one that arrived afterward.

For every decision in scope, the record MUST contain a manifest of the evidence, with one entry per document:

  • A stable identifier for the document, and its source.
  • A digest of its exact content at the time of the decision.
  • The time it was received.
  • Whether it was presented to the model.
  • Whether it was presented to the human reviewer, where one was involved.

The manifest MUST itself be digested, and that digest MUST be committed to the ledger with the determination. The two presentation flags are not bookkeeping. A document that was on file but never sent to the model, and a document nobody ever had, are different failures with different remedies, and no other part of the record distinguishes them.

What the manifest settles
A document added later is absent from the manifest, and is therefore provably not among the facts considered. A document amended later no longer matches its digest, and is therefore provably a different version than the one relied on. A document lost later is still proven to have existed and been considered. None of these require the plan to have stored the document itself; sealing the set is what answers the question, and storing the content is a separate requirement addressed in Section 9.

3.2 The input surface

The record MUST contain everything the system was given, as it was given:

  • The request: the service, item, or drug sought, with units and duration; the member; and the date and time of the decision.
  • The exact prompt and instructions, as worded, including all system-level instructions.
  • The full text of the coverage rule or policy applied, pinned by hash to the exact version in force. A version identifier alone is insufficient: policies are revised, and an appeal turns on what the criterion said that day.
  • The tools or retrieval available, and the results actually returned by any that were used, captured as retrieved rather than merely noted.
  • Any memory or additional context present in the system's window at decision time.

Where a deployment uses no memory or outside context, the record MUST state that the decision was made on the request alone; that attestation satisfies this part. Anything able to influence a decision that is not captured is a conformance gap. This is the central obligation of the Profile.

3.3 The execution configuration

The settings that govern how the system produced its output MUST be recorded and bound to the decision:

  • Model identity and version, including any provider-side version or system fingerprint returned with the response.
  • Temperature, top-p, and any other sampling parameters.
  • The sampling seed, where the provider accepts one.
  • Token limits and stop conditions in force.
  • Any confidence or routing threshold applied to this decision.

These MAY be held as a single versioned configuration referenced by each decision rather than copied into each record, provided that every change is written as a new dated entry in the tamper-evident ledger, and that each decision unambiguously references the version in force when it was made. This is how a change to a routing threshold — the lever that decides how many cases a human sees — is caught: not by logging the number on every decision, but by making every change to it a permanent, timestamped, attributable event.

Why the sampling settings are required
They are not recorded in order to re-run the decision (see Section 10). They are recorded because without them, variation cannot be attributed. A system operating at a non-zero temperature will not always return the same answer on the same facts. Where that is recorded, the variation is disclosed and expected. Where it is not, an identical case producing a different outcome is indistinguishable from inconsistency or manipulation, and the deployment has no way to answer the charge.

3.4 The determination

The record MUST contain:

  • The decision itself, and whether it was favorable or adverse.
  • What was requested versus what was decided, wherever they differ.
  • The stated basis, with principal reasons for any adverse outcome.
  • Any reasoning the system generated, in full and untruncated. Where a system can emit reasoning it SHOULD; a token limit MUST NOT cut it off; where it emits none, the record notes that.
  • The decider: whether the determination was issued by a model or by deterministic code, and where it was code, the identity and version hash of that code.

The decider is recorded because it is the first question a regulator asks and because the two cases are provable to different degrees, as Section 10 sets out.

3.5 Where a separate model or pass produces a score

Where a second model or a distinct scoring step generates a confidence, risk, or priority score, whether used to rank, prioritize, or decide which cases reach a human, the record MUST additionally capture the score and its scale (for example, 6 of 6, or 95 of 100); the model and version that produced it; and what the score was used for. A bare number without its scale and source is non-conformant.

4

The AI pipeline

Normative

A coverage determination is rarely a single call. A chart is assembled, facts are extracted from it, those facts are condensed into a summary a human or a downstream engine reads, a policy is codified into the rule the case is measured against, and only then is an outcome produced. A record that captures only the final determination cannot show whether a disqualifying fact was never found, was found and then dropped from the summary, or was correctly considered and outweighed. Those are different failures with different remedies, and only one of them is a decision failure.

Why this section exists
A fact can be extracted successfully and still never reach the reviewer. Where that happens, every downstream artifact is internally consistent and wrong, and nothing in a record of the final determination alone reveals it.

4.1 The requirement is a chain of custody, not a fixed pipeline

This Profile does not enumerate stages, because doing so would encode one vendor's architecture and fail on the next. It treats every point at which an AI system transformed the material as a stage with an input and an output, and requires three properties that hold regardless of what the stages are, how many there are, or what they are called:

  • Continuity. Each stage MUST record a digest of its input and a digest of its output. Each stage's input digest MUST equal the preceding stage's output digest.
  • Closure. The first stage's input digest MUST equal the digest of the evidence manifest, and the determination's input MUST be the final stage's output. Every byte the determination rests on must trace back to the sealed set.
  • Attribution. Each stage MUST record the model identity and version, or the code version, that performed it.

A break in the chain means material entered or left the pipeline unrecorded. This is detectable without knowing anything about what any stage does, which is precisely what makes the requirement architecture-independent. A deployment declaring that no AI system touched the material before the determination MUST record that declaration. It is a testable claim, not an exemption.

4.2 Stage content

Where a stage's output is relied on downstream, the output itself MUST be retained, not only its digest:

  • Extraction output. The decision-relevant facts extracted, each carrying a pointer to its location in the source packet. A fact used in a determination but not traceable to the packet MUST be marked ungrounded.
  • Summarization output. Any condensed representation actually presented to a human reviewer or consumed by a downstream component, retained in full alongside the extraction output it was derived from, so the two can be compared.
  • Codification artifacts. Where a coverage policy was converted into a machine-executable rule: the source policy text hash-pinned to its version, the generated rule, and an element-level comparison showing every required criterion, exception, and branch as present or absent.
  • Packet composition. The documents assembled into the decision packet, in order, with total size, and whether any content was truncated or dropped before the model saw it.

A digest alone is sufficient to prove a stage's output was not altered. It is not sufficient to detect that the output was wrong — that a disqualifying fact was dropped in summarization, or that a codified rule omitted an exception. Detecting those requires comparing the stage's output against its input, which requires the content.

4.3 Disqualifier disposition

For each extracted fact that would independently defeat the request, the record MUST state whether it appears in the summary or representation passed downstream. Where it does not, that omission MUST be recorded as an explicit event rather than left implicit in the absence.

4.4 Evidence resolution

Where the record contained conflicting values for the same decision-relevant fact, the record MUST state which source was treated as authoritative and which value was used. Where no conflicts were found, an explicit empty finding is the conformant way to say so.

5

Human accountability for non-approvals

Normative

Health-coverage law across jurisdictions is converging on a firm requirement: an AI system may assist, but a licensed human must be accountable for an adverse medical-necessity decision. This Profile makes that accountability a matter of record.

For any adverse decision — a denial, a reduction, or a pend that operates as one — the record MUST identify the accountable licensed reviewer and MUST record what that reviewer did: whether they made the decision independently, confirmed the system's recommendation, or overrode it. An adverse decision recorded with no accountable licensed reviewer is non-conformant.

Reviewer competence must be inspectable. The record MUST capture the reviewer's credential and specialty and, alongside it, the clinical issue the decision concerned, so that whether the reviewer's competence matched the case is directly visible. This Profile does not itself adjudicate whether a given credential was adequate — that judgment belongs to the plan and its regulator — but it requires that the two facts sit side by side, so that a mismatch cannot be hidden and can be found on inspection.

The review threshold is a control on outcomes
Where a deployment uses a confidence score, a second model, or any automated signal to decide which decisions reach a human, that logic MUST be recorded and held tamper-evident. In a common configuration the AI may only approve, and everything it does not approve is routed to a human; lowering the approval threshold then sends more cases to human review, where more can be denied, without any single decision appearing to change. A change to that threshold is a change to the denial rate. A deployment MUST NOT be able to change it, retroactively or silently, without that change being detectable in the record.
6

Routing of every non-approval

Normative

An approval ends the decision; a non-approval begins a chain, and that chain is where accountability is most often lost. For every non-approval, the record MUST capture what happened next, in full:

  • Where it went, and to whom. The person or step the case was routed to, identified.
  • Their competence. The receiver's credential and specialty, recorded beside the clinical issue of the case, so that a specialty match or mismatch is visible.
  • What they decided. The receiver's decision, and whether it confirmed, modified, or overrode the AI's output.
  • Their reasoning. The basis the receiver gave, recorded in full.
  • Whether it was final. Whether the decision is now final, or was routed onward again, in which case the same capture applies at each step, so the complete path from the AI's output to the final determination is reconstructable end to end.

This holds regardless of how the final decision is issued, whether a human enters it directly or it is returned through an automated interface. Where a coverage decision is transmitted through a standardized electronic prior-authorization interface, the provenance record MUST still capture the full chain above; the mode of transmission does not reduce what must be recorded.

7

Assurance state

Normative

A record that shows what a system did is not the same as a record that shows whether that system was, at that moment, operating inside the envelope in which it was tested. Every decision record MUST carry the assurance state in force when the decision was made:

  • The certificate identifier and version governing the deciding system, or an explicit statement that the system is uncertified.
  • The verdict of each certified capability line at decision time, at line granularity. An aggregate grade is not sufficient: a system may be certified for extraction and not for summarization, and the record MUST distinguish them.
  • Any condition attached to the certificate that applies to this decision, and whether it was satisfied. A decision made in violation of a certificate condition MUST be recorded as such.
  • The monitoring specifications in force, their status, and any capability line suspended or lapsed at decision time.
  • The configuration fingerprint observed at decision time, compared against the fingerprint recorded at certification.
Certification decays
Certification is point-in-time: the model can be updated behind an endpoint, the policy text can be revised, and the input distribution can shift. A certificate is therefore conformant only while its paired monitoring specifications are reporting. Where monitoring lapses, the capability lines it covers MUST be recorded as lapsed rather than as certified.
8

Individual-circumstances basis

Normative

Emerging law requires that a coverage decision be based on the enrollee's medical condition, medical necessity, and individual clinical circumstances, rather than on group or population data, or on any non-clinical factor such as cost or utilization targets.

The record MUST be sufficient to show which of the member's own clinical facts were before the system, so that a reviewer can confirm the decision rested on that member's circumstances and not on factors the law excludes. The evidence manifest (Section 3.1) and the input surface (Section 3.2) together satisfy this requirement; neither alone does, because a fact on file that was never presented to the system was not before it.

9

Integrity, custody, retention, disclosure, and privacy

Normative

9.1 Integrity

Every record MUST be committed to a signed, append-only, hash-chained ledger, so that any entry can be shown to have been created when claimed and not altered since. Corrections are made by appending a new, linked record, never by overwriting a committed one. The logic governing each decision's routing and disposition is held tamper-evident on the same basis. Any implementation providing append-only, cryptographically verifiable integrity of the kind used in recognized secure-logging and audit-trail practice satisfies this requirement; no specific vendor is mandated.

Integrity derives from when the commitment was made, not from how much was stored. A digest written into the chain at decision time makes any later alteration detectable regardless of how much content the deployment retains.

9.2 Verification against content

An unbroken chain proves that the entries were not altered. It does not, by itself, prove that retained content still matches the digest recorded for it. Where a deployment retains content, verification MUST recompute the digest of the retained content and compare it against the digest committed at decision time. A record whose content no longer matches its committed digest MUST be reported as altered, not as verified.

9.3 Custody of content

Every artifact required by this Profile MUST be digested into the ledger, and MUST remain retrievable. Where it is retrieved from is a property of the deployment, not of conformance. A deployment MUST record, for each decision, where the readable content is retained. Two custody arrangements conform:

  • Retained by the recording system. The content is held alongside the proof. The system is a PHI system and is governed accordingly.
  • Retained by the plan, referenced by the record. The content remains in the plan's systems and the record carries a resolvable reference to it. The recording system holds proof without holding the content.

A record that carries a digest for content that no party retains is non-conformant. A digest of something nobody kept proves nothing, and a conformance claim that rests on it is a claim that cannot be tested.

9.4 Retention

Records MUST be retained, and remain verifiable, for at least the period required by applicable coverage and appeals law. Deployments SHOULD NOT retain readable clinical content beyond that period; the integrity record may be retained longer, because it contains no clinical content.

9.5 Disclosure

A provenance record is evidence, and evidence has its own chain of custody. Where a record is disclosed — to an appeals reviewer, a regulator, counsel, or the member — the deployment MUST record as a ledger event: what was disclosed, to whom, when, and under what authority.

Disclosure access SHOULD be scoped to the determinations at issue and SHOULD expire. A standing export of the whole population is not a disclosure; it is a copy, and it defeats the custody record this section establishes.

Why disclosure is part of provenance
Without it, a record produced on appeal cannot be shown to be the record as it stood before anyone went looking, and the party producing it is the party asserting that. Recording the disclosure closes that gap: the access is timestamped in the same chain as the determination, so the record can be shown to predate the request that produced it. This also aligns with the audit-control obligations that already apply to any system holding protected health information.

9.6 Privacy

Verifying a record's integrity, and auditing AI involvement and human accountability, MUST NOT require exposing protected health information. Integrity verification operates on digests, and digests contain no clinical content. Deletion obligations are satisfied by removing the readable content while retaining the committed digest: the chain is never rewritten, and the record remains verifiable against whatever copy is later produced.

10

What conformance proves, and what it does not

Normative

A standard that overstates what it establishes is worse than no standard, because the overstatement is the first thing an opposing expert will attack. This Profile is precise about its claim.

10.1 Conformance does not mean the decision can be re-run

Bit-exact re-execution of a large language model is not achievable in production, and this Profile does not require or imply it. Providers update models behind stable identifiers; numerical non-determinism means identical inputs can yield different outputs on identical settings; and where a sampling seed is offered at all, it is typically documented as best-effort rather than guaranteed. A conformance claim MUST NOT be represented as a guarantee that the determination can be reproduced.

10.2 What conformance does establish

  • Exhaustiveness of influence. Everything that could have influenced the determination is in the record. There was no undisclosed instruction, no unrecorded retrieved content, no hidden context. This is what makes prompt manipulation, context injection, and undisclosed steering detectable rather than deniable.
  • Integrity over time. Every recorded artifact is byte-identical to what existed at the moment of the determination, or the discrepancy is detected and reported.
  • Accountability. Who decided, on what basis, under what configuration, reviewed by whom, and where the case went next.

10.3 Deterministic deciders are provable to a further degree

Where the determination was issued by deterministic code rather than a model, the decision step is reproducible: the recorded rule version can be re-executed against the recorded extracted representation and the outcome compared to the one issued. For such deployments a conformant record supports a stronger claim, that the determination followed correctly from its inputs, and a conformance report SHOULD state which claim applies. This is a property of the architecture, not a difference in the requirements.

11

Conformance reporting and public attestation

Normative

Conformance is a property of a deployment over a period, not of a record selected for inspection. A deployment that produces a hundred flawless records alongside a hundred thousand determinations that produced none is not conformant, and nothing in a per-decision requirement detects that.

11.1 Coverage

For each reporting period, a deployment MUST measure and report:

  • The number of in-scope determinations the deployment issued.
  • The number of those that produced a conformant record.
  • The resulting coverage rate.
  • Every shortfall attributed to the specific requirement that failed, so that a coverage rate below 100% names its cause rather than reporting a bare number.
The denominator must come from outside the record
The count of in-scope determinations MUST be derived from the deployment's own determination volume, the source or claims system, and MUST NOT be derived from the provenance records themselves. Counting only the determinations that produced a record yields 100% coverage by construction, and a metric that cannot fall is not a control. Where the two counts disagree, the difference is itself a finding and MUST be reported.

11.2 Continuous verification

A record committed once and never checked again establishes nothing about the present. A deployment MUST:

  • Verify the integrity of the hash chain on a defined cycle, and at least daily.
  • Re-verify retained content against its committed digests on a defined cycle, and at least quarterly across the full retained population.
  • Record each verification run, its scope, and its result as an event in the ledger. A verification a deployment cannot prove it performed is not a control.
  • Report any lapse in either cycle. Where verification has lapsed, the affected period MUST be reported as unverified rather than as conformant.

11.3 Public attestation

A deployment MAY publish an attestation of its conformance. Where it does, the attestation MUST contain all of the following, and MUST NOT state more than has been measured:

  • The Profile and version claimed, and the reporting period covered.
  • The coverage rate for that period, and the scope of decision types included.
  • Chain verification status, with the time of the most recent successful verification.
  • Certification status per capability line, including any line lapsed or suspended.
  • The custody arrangement in force.
  • An as-of date. An attestation without one is non-conformant, and an attestation whose most recent verification falls outside its own stated cycle MUST be presented as stale.

A published attestation MUST be independently verifiable: a third party MUST be able to confirm the integrity claims against the ledger without being granted access to clinical content. It MUST NOT contain protected health information, per-determination data, or outcome statistics such as approval or denial rates. It attests to the provenance posture of a deployment, not to the merits of its determinations.

12

Conformance

A deployment conforms to the APS Healthcare Coverage Profile if and only if it produces, for every decision in scope, a record containing:

  • the evidence manifest, input surface, execution configuration, and determination required by Section 3;
  • the unbroken, closed, attributed pipeline chain and stage content required by Section 4;
  • the human-accountability and reviewer-competence records of Section 5;
  • the full routing chain of Section 6 for every non-approval;
  • the assurance state of Section 7;

all committed to a tamper-evident ledger, with every artifact retrievable under one of the custody arrangements of Section 9.3, and if and only if it measures, verifies, and reports its coverage as required by Section 11.

A conformance claim MUST name this Profile and its version, MUST state which custody arrangement is in use, MUST state the coverage rate and reporting period it rests on, and MUST NOT be represented as broader than what was actually recorded, nor as establishing more than Section 10 sets out.

Partial conformance is reportable, and is not failure
A deployment covering 62% of its in-scope determinations conforms for those determinations and MUST say so plainly rather than claiming the whole. A precise partial claim is conformant; a rounded-up whole claim is not.
§

How this maps to regulation

A companion Regulatory Crosswalk maps each requirement of this Profile to the specific federal and state instrument whose evidence it produces — CMS-0057-F, CMS-4201-F, HIPAA, ERISA, the NAIC Model Bulletin, and the state AI utilization-review statutes — and states plainly which obligations the Profile does not satisfy.

APS Regulatory Crosswalk
Obligation to evidence, section by section, with the gaps named.
Read the crosswalk
A

Appendix A, Conformance checklist

For each decision in scope, a conformant record answers yes to every applicable item. Configuration items are recorded once per version, not per decision.

Evidence manifest
  • One entry per document: stable identifier and source.
  • Digest of each document's exact content at decision time.
  • Time each document was received.
  • Presented-to-model flag on each document.
  • Presented-to-reviewer flag on each document, where a reviewer was involved.
  • The manifest itself digested and committed to the ledger.
Input surface
  • Request: service, units, duration, member, date and time.
  • Exact prompt and instructions, as worded, including system-level instructions.
  • Coverage rule or policy full text, hash-pinned to the version in force.
  • Tools and retrieval available, and results actually returned by those used.
  • Memory or additional context as retrieved, or an explicit attestation that the decision was made on the request alone.
Execution configuration (once per version; change = new dated ledger entry)
  • Model identity and version, including provider-side fingerprint where returned.
  • Temperature, top-p, and other sampling parameters.
  • Sampling seed, where the provider accepts one.
  • Token limits and stop conditions.
  • Confidence or routing threshold applied.
Determination
  • The decision; favorable or adverse; requested versus decided.
  • Stated basis, with principal reasons if adverse.
  • Generated reasoning in full and untruncated, or noted as none.
  • Decider: model or deterministic code, with version hash if code.
Pipeline
  • Input and output digest recorded for every stage.
  • Each stage's input digest equals the preceding stage's output digest.
  • First stage's input digest equals the evidence-manifest digest.
  • Determination's input equals the final stage's output.
  • Model or code identity and version recorded per stage.
  • Packet composition: order, size, and any truncation or dropped content.
  • Extraction output, each fact pointed at its location in the source packet; ungrounded facts marked.
  • Summary actually shown to a reviewer or consumed downstream, retained in full.
  • Source policy text hash-pinned, generated rule, and element-level comparison.
  • Disposition of every disqualifying fact; omission recorded as an explicit event.
  • Authoritative source identified wherever the record conflicted.
  • Or: an explicit declaration that no AI system touched the material before the determination.
If a separate scoring model is used
  • The score and its scale, for example 95 of 100.
  • The model and version that produced it.
  • What the score was used for.
Assurance state
  • Certificate identifier and version, or an explicit uncertified statement.
  • Verdict of each capability line at decision time, at line granularity.
  • Applicable certificate conditions and whether each was satisfied.
  • Monitoring specifications in force, their status, and any lapsed line.
  • Configuration fingerprint observed, compared against the certified fingerprint.
Every non-approval
  • Accountable licensed reviewer identified.
  • Reviewer credential and specialty, beside the case's clinical issue.
  • Where it was routed and to whom, at each step.
  • The receiver's decision and full reasoning.
  • The complete path to the final decision.
Integrity and custody
  • Signed, append-only, hash-chained ledger.
  • Retained content verified by recomputation against its committed digest.
  • Custody arrangement recorded, and every artifact retrievable.
  • Verifiable without exposing protected health information.
  • Retained for the required period.
  • Every disclosure recorded as a ledger event: what, to whom, when, under what authority.
  • Disclosure access scoped to the determinations at issue, and expiring.
Per reporting period (deployment-level, not per decision)
  • In-scope determinations counted from the source system, not from the provenance records.
  • Conformant records counted, and the coverage rate reported.
  • Every shortfall attributed to the requirement that failed.
  • Any disagreement between the two counts reported as a finding.
  • Chain integrity verified at least daily, each run recorded in the ledger.
  • Retained content re-verified against digests at least quarterly across the full population.
  • Any lapse in either cycle reported; affected periods marked unverified.
If an attestation is published
  • Profile and version claimed, and the reporting period covered.
  • Coverage rate and the scope of decision types included.
  • Chain verification status, with the time of the most recent successful verification.
  • Certification status per capability line, including any lapsed or suspended line.
  • Custody arrangement in force.
  • As-of date present; marked stale if verification falls outside the stated cycle.
  • Integrity claims independently verifiable without access to clinical content.
  • No PHI, no per-determination data, no approval or denial statistics.
Draft for public comment

We are proposing this to the industry, not imposing it.

Version 1.0 is open for comment from plans, vendors, auditors, clinicians, and regulators. Capture scope, reviewer-competence records, and the routing chain are exactly the places where the field's input should shape the specification.