Building a Per‑Payer Claim Denial Predictor from 835 Remittances

Description and use

Building a per‑payer claim denial predictor from 835 remittances enables billing specialists to flag high‑risk claims before submission, reducing denial costs.

Problem

Claims are often denied after submission, costing time and money. Predicting denials before submission can reduce these costs.

Outcome

Implemented per‑payer denial prediction reduced post‑submission denial handling time and improved claim acceptance rates.

Workflow

• 1. Extract 835 remittance data and join to 837 claim submissions to create a labeled dataset.
• 2. Define a binary denial label using CARC codes that represent preventable denials.
• 3. Engineer features such as payer×plan×CPT interactions, modifier combos, prior authorization presence, provider documentation patterns, diagnosis–procedure pairing frequency, and recency‑weighted denial rates.
• 4. Train per‑payer LightGBM models (fallback to a pooled model for low‑volume payers).
• 5. Deploy models to score new claim lines and generate a denial flag for billing specialists.
• 6. Use CARC/rarc codes to produce human‑readable denial reasons and train a secondary category model if needed.

Evidence

The hard part is not the algorithm. It is the join.

Source

Payer behaviour differs too much for a single global model to serve everyone well.

Source