Regain Accreditation
Most facilities still navigate accreditation with spreadsheets and manual document compilation. Regain reads facility EMR data via FHIR R4 and evaluates it against published Standards continuously, as care happens. Roughly 85% of routine compliance checks automate; the rest stays with the reviewer.
Standards as versioned rule packs. 64 rules drawn from a published 2025 echo accreditation Standards document. Each finding cites a clause, a rule version, a metric, and a dataset window.
Evidence as continuous flow. Clinical data is evaluated deterministically as care happens.
One loop, two readings. The same connection that evaluates a facility against published Standards also surfaces how clinical AI is used in practice. Override patterns and AI-vs-final-reading concordance accrue to the accrediting body by the structure of the relationship.
Standards authority unchanged. The pipeline executes published Standards, never authors them. Peer review and the accreditation decision stay with the accrediting body.
How it plays out
A modality program inside a facility runs procedures, writes reports, captures reads, services equipment, and renews credentials at volume, every working day, year after year. Each of those procedures, reports, and reads is evidence, for or against the Standards the program is accredited to.
The Standards say what the evidence must show. Appropriate use. Technical quality. Interpretive quality. Report completeness and timeliness. Personnel credentials current. Equipment maintained. Volumes met. Many programs carry accreditation as a voluntary quality signal, not a regulatory floor, but a mark a program elects to maintain. The Standards have been refined for decades by the societies that write them. They are not the problem.
The Standards are not the problem.
The evidence already exists. It sits in the EMR, the credentialing system, and the equipment logs. The gap is that the evidence is compiled by hand once every three years, and nothing watches it in between. Gaps accumulate silently. Renewal arrives as a scramble. Peer reviewers spend their time on completeness, not clinical judgment.
Standards encoded as version-controlled rules. FHIR-native pull from the facility's clinical systems. Evidence accumulating continuously against the rule pack for that program area. Findings that cite the clause, the rule version, the metric, and the data the metric was computed from. The cycle becomes continuous because the evidence is continuous. The Standards do not move. The evidence catches up.
The Standards do not move. The evidence catches up.
The architecture
01
Compliance evaluation is a deterministic rule engine. The same inputs produce the same finding. Reasoning that proposes actions sits on a separate codebase from the evaluator that judges them. The audit trail does not pass through a model.
FHIR R42
Standards-compliant FHIR R4 ingest with code-system mapping for clinical observations. Derived evidence observations are hashed before evaluation. Tested end to end against a public vendor sandbox; production integration is gated on each facility's data-use and information-security review.
Multi-
The engine has been run against more than one published standards framework. Modality-agnostic. Country-agnostic. New frameworks ship as rule-pack releases, not engine releases.
How the substrate works
The same five movements run on every program at every facility, every day. None of them require a surveyor on-site. None of them require a spreadsheet. Every movement writes provenance to the audit trail.
01
Secure ingest from the facility's clinical systems via FHIR R4. Volumes, reports, credentials, equipment QC, and QI process records read directly. No re-entry, no manual abstraction.
02
Deterministic rule evaluation against the Standards for that program area. No LLM in the compliance path. Same inputs, same finding.
03
Findings cite the clause, the rule version, the facility-level metric the engine computed, and the dataset window the metric was computed over. The audit surface is the finding.
04
Facility-side views of the program's compliance posture in near-real time. Accreditor-side views aligned to the Standards the program is accredited to. Sampled case studies surfaced for peer review when the data points to a clause that needs human judgment.
05
At the cycle boundary, the evidence is already compiled. Reaccreditation becomes a review, not a scramble. Findings that needed remediation between cycles are already closed, with the trail of when and by whom.
Platform
Compliance evaluation belongs next to the clinical data it measures. Clinical data, deterministic compliance evaluation, and reviewer-facing tooling run on a shared substrate, a unified audit model, coordinated role-based access, and one evaluation surface, with protected health information held behind its own storage boundary by design. Standards encoded as code. Evidence accumulating continuously. Findings cite the clause that produced them.
Figure 2.1
Deterministic rule evaluation. The same inputs produce the same finding. No LLM in the compliance path. Findings cite the clause, the rule version, the facility-level metric, and the dataset window the metric was computed over. Reproducible end to end.
Explore the Compliance Engine →FHIR R4-native ingest from the facility's clinical systems. Derived evidence observations are hashed before evaluation. Volumes, reports, credentials, and equipment QC flow into compliance evaluation without manual abstraction.
Explore the Data Pipeline →Reviewer-facing tooling for the work that requires judgment, not completeness checking. Mock surveys against encoded Standards. Gap analysis tied to the underlying metric. Cross-program standards mapping. Reviewers spend their time on what only reviewers can do.
Explore the Reviewer Surface →Standards encoded as version-controlled rule packs by program area. Semantic versioning. Priority-based composition. Conflict detection at load time. The engine is fixed; the Standards are configuration. New standards versions ship as rule-pack releases, not engine releases.
Explore Standards as Code →Modality- and country-agnostic standards runtime. The engine has been run against more than one published standards framework, with cross-framework mapping where rules overlap.
Explore Multi-Accreditor Runtime →Each rule traces to an authoritative source through a five-layer hierarchy: regulatory floor, standards from the societies that write them, peer-reviewed evidence, professional guidelines, emerging evidence. Higher layers take precedence. Enforced programmatically.
Explore Clinical Grounding →A note on AI evidence
The patterns that show how a tool is actually being used (when measurements get overridden, where thresholds cluster, how AI-suggested values compare to final human readings) emerge from the same evidence pipeline that supports compliance evaluation. The substrate makes those patterns visible. What to do with them is a question for the standards bodies that already define what good practice looks like.
Figure 5.1
The thesis
When compliance is downstream of the clinical system, an evaluation depends on a chain of integrations, exports, and manual abstractions that stay aligned only if someone is watching them. When compliance is co-resident with the clinical data, evaluation is continuous and automatic, and the compliance posture stays current as the underlying data updates, because the data that drives clinical care is the same data that drives compliance evaluation.
The Standards are not the bottleneck. The evidence pipeline is.
Figure 3.1, Rule-pack decomposition across five program-area packs
Figure 4.1
Use Cases
Eight scenarios drawn from the operational pain of clinical accreditation: between-cycle gap accumulation, manual case sampling, peer-reviewer assignment friction, application completeness failures, equipment QC self-reporting, the slow revision cycle, the silent remediation window, and manual QI documentation. Each scenario is generic; each one is recognizable. Explore Use Cases →
Insights
Long-form pieces from the substrate vantage point: the visibility gap, manual abstraction costs, the voluntary-standards erosion question, the position where AI-use evidence becomes visible, and the transition to continuous monitoring. Read the Insights →
Trust
Deterministic evaluation with no LLM in the compliance path. Structural independence between reasoning and supervision. Open standards where it matters, proprietary depth where it counts. Tooling that frees peer reviewers to spend their time on judgment, not on completeness checking. Building toward HIPAA compliance with an engineered separation of PHI and PII. See the trust architecture →
About
Regain, Inc. is a Delaware C-Corporation building clinical AI infrastructure for healthcare accreditation. Self-funded. Field experience deploying clinical systems at medical centers in Central Asia. Building toward HIPAA compliance with engineered separation of PHI and PII. Read about Regain →
We will walk through your Standards framework and show you what continuous compliance monitoring looks like for the program area you accredit.
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