Healthcare & Life Sciences
Healthcare Billing Validation API
A claim that comes back denied has already cost you the work of submitting it. Most of the reasons were visible before it went out.
- Revenue cycle software
- Billing service providers
- Hospital finance teams
- Practice management vendors
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Denials cluster around a small number of recurring causes: a procedure code that does not pair with the diagnosis submitted alongside it, a modifier missing, documentation that does not support the level of service claimed, an eligibility check that was never run, a duplicate from a resubmission nobody tracked. Each one is checkable before submission. In practice the check happens after the denial, by a person reading a remittance advice.
Billing teams already work from rule sheets, payer bulletins and institutional memory. The problem is coverage — the rules change per payer and per quarter, and no human reviewer holds all of them for every claim passing through. Volume means the checks that get applied are the ones that fit in the time available.
What is buildable here is a validation pass: deterministic rule checks where rules exist, and statistical anomaly detection where they do not. The second part needs framing carefully. An anomaly is a claim that looks unlike its peers. That is a reason to look, not a finding, and certainly not an allegation about the clinician who coded it.
Who buys this
Revenue cycle software, Billing service providers, Hospital finance teams, Practice management vendors
What the API returns
What comes back
- Code pairing checks between diagnosis and procedure codes on the same claim
- Modifier validation against the procedure, place of service and payer rules
- Documentation sufficiency checks: which claims assert a service level the attached notes do not evidence
- Missing prerequisite flags — eligibility verification, prior authorisation, referral on file
- Duplicate and near-duplicate claim detection across submission history
- Statistical outliers against peer distributions by specialty, procedure and site
- Expected denial reason codes where a payer rule is likely to trigger
- A ranked review queue with the rule or comparison that produced each item
Endpoints
| Method | Path | Returns |
|---|---|---|
| POST | /v1/claims/validate | Validate one claim before submission |
| POST | /v1/claims/batch | Validate a submission batch |
| GET | /v1/claims/{id}/findings | Findings with the rule or comparison behind each |
| GET | /v1/anomalies | Statistical outliers queued for review |
| GET | /v1/rules | Active rule set with payer and effective dates |
| POST | /v1/findings/{id}/disposition | Record a reviewer's decision for calibration |
Request and response
illustrativeRequest
{
"claim_id": "clm_7731204",
"payer_id": "payer_0412",
"place_of_service": "11",
"lines": [
{ "procedure": "99214", "modifiers": [], "units": 1, "diagnoses": ["E11.9", "I10"] },
{ "procedure": "93000", "modifiers": ["26"], "units": 1, "diagnoses": ["I10"] }
],
"documentation_refs": ["doc_914772"],
"prior_auth": null
}Response
{
"claim_id": "clm_7731204",
"validated_at": "2026-10-22T10:18:00Z",
"submission_recommendation": "hold_for_review",
"findings": [
{
"finding_id": "fnd_01",
"type": "documentation_insufficient",
"severity": "high",
"line": 1,
"detail": "Level 99214 claimed; attached note documents no review of systems",
"basis": "rule:doc_support_99214",
"action": "biller_review"
},
{
"finding_id": "fnd_02",
"type": "missing_prior_authorisation",
"severity": "high",
"line": 2,
"detail": "Payer 0412 requires authorisation for 93000 at place of service 11",
"basis": "rule:payer_0412_pa_list_2026q4"
},
{
"finding_id": "fnd_03",
"type": "statistical_outlier",
"severity": "low",
"line": 1,
"detail": "99214 share for this clinician is at the 94th percentile among peers",
"basis": "comparison:peer_specialty_distribution",
"note": "Observation for review. Not a finding of error or misconduct."
}
],
"expected_denial_codes": ["197", "50"]
}Where the data comes from
| Your claims history | Submitted claims with their remittance outcomes — the only way to learn which checks actually predict denial in your payer mix |
| Published code sets and edit pairs | Procedure and diagnosis code sets plus published edit relationships; several carry licensing conditions on redistribution inside a commercial product |
| Payer policy bulletins | Prior authorisation lists, coverage policies and modifier rules, which change quarterly and have to be tracked per payer |
| Eligibility and authorisation transaction logs | So a missing prerequisite is detected from the absence of a real transaction rather than an empty field |
| Clinical documentation | The notes attached to the encounter, which is what documentation sufficiency is assessed against — read inside your environment, not exported |
| Reviewer dispositions | What your billers decided on past findings, which is how precision gets tuned to your tolerance for false positives |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Deterministic rule evaluation first, because a published payer rule should never be handled by a probabilistic model
- Rules stored with payer, effective date and source citation, so a finding can be defended with the bulletin it came from
- Documentation sufficiency assessed by matching required elements against extracted note content, with abstention where the note is unreadable
- Peer comparison with cohort minimums, so a single-clinician specialty at one site never becomes its own outlier
- Anomaly scoring against specialty and site-matched distributions rather than a global average, since case mix drives most apparent variation
- Denial prediction trained on your own remittance history, reported with precision and recall at the threshold you choose to run
- Explicit output framing: every statistical item is labelled an observation for review, with no finding of intent anywhere in the schema
- Monitoring for rule staleness, with an alert when a payer bulletin supersedes a rule currently in force
What it changes for the business
Fewer avoidable denials
Checks that currently run after a remittance arrives move to before submission, where fixing them is cheap.
Review time concentrated
A ranked queue puts biller attention on claims where a specific rule or comparison says something is wrong.
Defensible decisions
Each finding carries its rule and source, which is what an internal audit or a payer appeal actually needs.
Rule knowledge made institutional
Payer rules live in a maintained, dated rule set rather than in the memory of two experienced billers.
How it fits into your product
How it embeds
- Runs inside your own environment; claims and clinical notes stay there
- Synchronous validation in the submission path, with a latency budget that fits a clearinghouse handoff
- Batch validation for pre-submission sweeps and retrospective review
- Findings delivered as work items into your existing biller queue, or as prebuilt review components
- Webhooks on high-severity findings so a claim can be held automatically before transmission
- Rule set versioned and auditable, with per-payer enable and disable controls
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 12 weeksDiscovery and data review
Weeks 1–2
We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.
Architecture and prototype
Weeks 3–4
You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.
Production build
Weeks 5–10
Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.
Integration and handover
Weeks 11–12
We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
Is this upcoding assistance?
No, and we would refuse to build it that way. The service checks whether a claim is consistent and supported by documentation. It does not suggest higher-paying codes, identify opportunities to raise service levels, or optimise toward revenue. Where documentation does not support the level claimed, the finding is that the claim is unsupported — the resolution is better documentation or a lower code, decided by your coders.
Does an anomaly mean fraud?
No. An anomaly means a claim or a pattern looks unlike comparable ones, which has many innocent explanations: an unusual case mix, a sub-specialty interest, a site serving a particular population. Every statistical item is labelled an observation for review and carries the comparison that produced it. We deliberately do not emit anything that reads as an allegation, because a system that does invites being used as one.
Will it catch every denial reason?
No. Payer rules are numerous, unevenly published and change quarterly, and some denials come from adjudication logic no bulletin describes. Expect good coverage of documented rules and the denial patterns present in your own history, and expect a long tail it does not see. We report measured coverage against your remittance data rather than claiming completeness.
Who makes the final decision on a claim?
A human biller or coder, always. The API recommends hold or submit and explains why; it does not transmit, suppress or alter a claim. Automating submission on the strength of these findings is not something we would design toward.
Can the code sets and payer rules be redistributed inside our product?
That depends on the licence attached to each. Some code sets are freely usable, some require a licence for commercial redistribution, and payer bulletins carry their own terms. We establish the position for each source during discovery and design around anything that does not permit your intended use.
Related blueprints
Medical Document Extraction API
Structured fields pulled from clinical notes, lab reports and referral letters, returned with per-field confidence for review.
Provider Directory Intelligence API
Clinician and facility directory data with specialties, locations, accepted coverage and a measured accuracy score per record.
Appointment No-Show Prediction API
Cancellation and no-show probabilities per appointment, built to add capacity through reminders and overbooking rather than to ration care.
Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.