Building a Per‑Payer Claim Denial Predictor from 835 Remittances

Описание и применение

Создание модели предсказания отказа по заявкам для каждого страховщика на основе файлов 835 позволяет специалистам по биллингу заранее выявлять рискованные заявки и уменьшать затраты на апелляции.

Задача

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

Результат

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

Рабочий процесс

• 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.

Доказательства

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

Источник

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

Источник