Denial risk scoring before submission
A model trained on the organisation's own submitted claims and the insurer's response to each that ranks claims which passed every edit by their likelihood of denial, so billers review the riskiest ones before they are sent.
Supporting approachTechnique: Supervised Learning
A denial risk score looks at each claim that has already passed the eligibility, authorisation, and claim edit checks, and estimates how likely the insurer is to deny it.
It is trained on the organisation’s own history of submitted claims and the insurer’s first response to each, and it produces a ranked list sized to the number of claims billers can review before submission.
Billers work the list from the top, checking the riskiest claims against the insurer’s rules and correcting what they find before the claim is sent.
A study at one health system used a structured model on claim form fields as its baseline for predicting denials, but no source here compares such a model with a mature set of pre-submission edits, so the score has to prove its value against those edits on the organisation’s own claims.
This is the wrong starting point for an organisation whose front-end checks are incomplete, where the model will mainly learn the eligibility and authorisation errors a deterministic check would stop.
It is also the wrong tool for a small practice with few denials per insurer, where a clearinghouse scrubber and a regular denial review do the same job without a model.
Strengths for this problem
- Learns combinations of insurer, plan, procedure, diagnosis, modifier, and provider that are denied more often than any single edit would show.
- Trained on the organisation's own remittance history, so it reflects how its actual insurers behave rather than a generic rule set.
- Sized to biller capacity, it turns a pre-submission review of every claim into a review of the claims most likely to fail.
Limitations for this problem
- Learns from reason codes and outcomes that are often vague, so it can say a claim is risky without saying what to fix.
- Degrades when an insurer changes its authorisation list or coverage policy, because the history it learned from no longer applies.
- Adds little where eligibility and authorisation checks are incomplete, since it mostly relearns the errors those checks would have caught.
- Needs enough denials per insurer and service to learn from, which a small practice rarely has.
Data needed
- Each claim as submitted, not as later corrected: insurer, plan, procedure and diagnosis codes, modifiers, place of service, rendering provider, charges, and authorisation number.
- The insurer's first response to each claim line, with claim adjustment reason codes and remark codes, as the training label.
- The dates of insurer rule changes, so training windows do not straddle them.
- The outcome of any biller review prompted by the score, to learn whether flagged claims really had errors.
How it is measured
- Share of flagged claims on which a biller found and corrected an error, at the number of claims billers can review each day.
- Initial denial rate on scored claims against a comparable period or group without the score.
- Score performance per insurer on claims after the most recent rule change, not only on the full history.
Pitfalls
- Training on claims as they were later corrected rather than as submitted, so the model never sees the error that caused the denial.
- Using fields filled in after submission, such as denial work queue notes, as features, which looks excellent offline and fails on a new claim.
- Training across an insurer rule change and scoring current claims against rules that no longer apply.
- Letting the score trigger automatic changes to codes or modifiers, which turns a review aid into a coding compliance risk.
Human oversight
Billers review every flagged claim and decide whether anything is corrected before submission. Coders decide any change to a diagnosis or procedure code, from the documentation. The revenue cycle manager sets how many claims are flagged each day and reviews the score's hit rate per insurer. The score never holds, edits, or submits a claim on its own.