Building a Per‑Payer Claim Denial Predictor from 835 Remittances

说明与用途

使用835报销文件构建每个付款方的拒付预测模型，使账单专家能够在提交前标记高风险索赔，从而降低拒付成本。

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

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

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.

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

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