Policy as Executable Code: Technically Precise with Audit-Defensible

The Neuro-Symbolic Proof Layer for Payment Integrity

This article continues the series that began with "Policy as Executable Code: Guided AI + Rules + Knowledge Graphs." In that article, I shared that payers don't have a data problem so much as a computability problem: policy and contract clauses and rules must become machine-executable logic with lineage.

In this article, I go a bit deeper and show how we make that real at Nēdl Labs - technically precise, business-ready, and audit-defensible.

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Healthcare payment decisions live at the intersection of prose and programs. Medical policies and provider contracts are written for humans; claims engines demand exact rules. Most organizations bridge the gap with people, spreadsheets, and point edits.

The result is familiar: plateaued first-pass adjudication, rising appeals, and post-pay recoveries that feel adversarial. When dollars are at stake, "we think" is not enough; you need provable decisions that explain themselves, survive scrutiny, and scale.

At Nēdl Labs, we operationalize this with a neuro-symbolic architecture: Neural models do pattern recognition (reading, labeling, normalizing), while a symbolic reasoner enforces typed constraints, effective dates, and conflict resolution.

The end product isn't a score; it's a decision with receipts - clause-level citations, version diffs, and a replayable proof of why a particular claim qualified, denied, or re-priced, with required documentation.

From reading to reasoning

The practical problem is not "extract text from PDFs." It's "extract typed facts that a logic engine can trust."

In our platform, source documents, including policies, contracts, fee schedules, and clinical guidelines, flow through an extraction plane that is strict on schema and loose on language.

AI models guided by templates that force every candidate fact to carry types (number, code, date, boolean), units (%, kg/m², RVU), code-system anchors (CPT/HCPCS, ICD-10-CM/PCS, MS-DRG), and scope (effective-from, effective-to, jurisdiction, plan).

If a statement emerges without type safety or a citation down to section/paragraph, it doesn't enter the fact store.

Those facts populate a knowledge graph that encodes relationships human experts reason about but databases rarely capture: requires, excludes, overrides, supersedes, inherits, except-when.

• Contracts aren't flat lists of rates; they're hierarchies of base terms, riders, addenda, carve-outs, and time-boxed exceptions.
• Policies aren't static; they evolve with clinical evidence and operational learnings.
• The graph is how the system remembers those relationships and how we traverse them deterministically.

Then there is symbolic reasoner, think Datalog/Prolog-style rules, compiled from policy and contract templates.

This is where ambiguity is removed. Numeric thresholds are checked with units; temporal windows are applied "as of" the date of service; code inclusions/exclusions are evaluated against the correct vocabulary version; conflicts are resolved explicitly (e.g., "§3.2 rider R-12 overrides §3.1 for outpatient TKA after 2025-04-01").

When the engine returns a result, it returns the proof tree: which facts and clauses fired, which were considered and rejected, and why.

What this looks like on an actual claim

Take a straightforward but high-dollar scenario: Total Knee Arthroplasty (CPT 27447), billed inpatient, grouped to MS-DRG 470. A typical stack might check a few edits and, if the patient is older or has common comorbidities, wave it through. Our flow is different.

The claim is normalized into typed inputs - diagnoses, procedures, modifiers, dates, demographics, site of service, and length of stay. If documentation is present, our clinical layer extracts structured signals (BMI value and measurement date; radiographic evidence phrases mapped to controlled concepts; conservative therapy start/stop dates; contraindications). The repricer loads the right contract terms for this provider and service line, because the Contract Library keeps clause-level versions and effective windows, and produces a rate with its rationale ("1.15× Medicare IPPS per §3.2; rider R-12 excludes implant carve-out on/after 2025-04-01").

The policy engine then evaluates coverage logic compiled from the policy family: "BMI < 40 within 180 days and radiographic evidence of end-stage osteoarthritis; unless patient age < 40 or six months of failed conservative therapy documented."

The engine binds claim facts to those predicates and evaluates them against effective dates. Suppose BMI is 41, but the conservative-therapy exception is satisfied. In that case, the decision is approve with exception, and the evidence pack shows both the failed threshold and the satisfied exception, each citing its clause and effective range. If BMI is 38 but measured 215 days earlier, the result is pend for current BMI, again with precise rationale.

There's no "model confidence," only why.

Why payers care: provability compels better outcomes

This is not academic neatness. In day-to-day operations, provability translates directly into:

Lower abrasion

Provider conversations become evidence-based: "Per ACME Policy v3 §2.1.b, effective 2024-07-01, BMI must be measured within 180 days; your value was measured at 215 days. Please supply a recent measurement or confirm exception §2.1.c." Clarity reduces back-and-forth and improves win rates on legitimate denials.

Higher first-pass accuracy

Because logic executes the same way every time and is versioned, you reduce variance across teams and auditors. The knowledge base compounds, new policies, and contracts increase the coverage surface without re-inventing rules.

Faster dispute resolution

When appeals arise or an NSA IDR filing looms, the evidence pack you used to decide is the evidence pack you use to defend. Time-to-resolution drops because there's one source of truth traversed by humans and systems alike.

Regulatory resilience

Effective-dated reasoning lets you re-run any claim "as of" a prior date against the exact rules in force that day. That's critical when auditors or regulators ask, "What did you know, and which policy version did you apply, then?"

Engineering patterns that make code stable in the wild

Two hard-won lessons shape our implementation.

First, guided extraction beats free-form prompting. We never ask an LLM to "write the rule." We ask it to fill a template and generate code with typed fields, bounded vocabularies, and explicit citations. Static checkers then validate unit consistency, code-set membership, and temporal sanity. That rejects a considerable class of hallucinations upfront and keeps reasoning deterministic.

Second, lineage is a first-class entity, not metadata. Every fact and rule carries pointers to its source document, section, paragraph, and version; every change is a new node, not an in-place mutation. That's how we can show version diffs in human terms ("§2.1.b BMI threshold changed from 40 to 38 effective 2026-01-01") and in machine terms (a new constraint compiled and activated in the rule graph). It is also how we keep trust when clinical committees or contract teams update logic on Friday for Monday enforcement.

Coexisting with your current stack

Nēdl Pulse is built to compose, not replace.

1. The extraction plane is ingested from your policy portal, CMS, and other payers' public websites, and provider contracts from your contract repository.
2. The reasoning plane exposes a pluggable knowledge base post-pay or pre-pay.
3. The repricer can run side-by-side with your DRG grouper and legacy adjudicator.
4. The evidence pack lands in the provider portal and your appeals tooling.

If you have already invested in predictive detection, you can keep it. Our proof layer converts high-risk scores into defensible actions.

If your prior auth modernization is underway, our policy rules are machine-readable rationales that plug into API-driven workflows.

Security and governance are integral.

Facts and rules are tenant-scoped; PHI is isolated; decision artifacts are immutable; human overrides create explicit "counter-facts" with reasons, not silent edits. That's how you scale without losing the audit trail.

Moving left---without losing trust

The long pole is to shift more logic left into pre-pay. Neuro-symbolic makes that safe.

1. Neural components continue to read the messy world of PDFs and notes;
2. Symbolic components continue to decide within guardrails;
3. Knowledge graph keeps institutional memory coherent as policies, contracts, and codes evolve.

Clinicians and coders are augmented; they become authors of computable policy and reviewers of cases with speed and scale.

That's how you increase catch rates and preserve provider relationships.

In the first article, we made the case for treating policy as code. This one shows the execution path: extract precisely, reason deterministically, and serve decisions with receipts.

For payer leaders, this is the practical route to reducing leakage, shortening appeals, and meeting rising expectations for transparency; claim by claim, day by day.