TY - JOUR
T1 - Explainable Gradient Boosting Model for Predicting Health Insurance Claim Denials Using Documentation Completeness, Procedure Codes, Diagnosis-Code Consistency, Payer Rules, and Prior Authorization History
AU - Oliver Grant
AU - David Clark
AU - Sophia Nguyen
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2023
VL - 3
IS - 2
DO - 10.68159/w681883871
SP - 80
N2 - Claim denials represent a major source of lost or delayed healthcare revenue. They are commonly driven by documentation gaps, coding inconsistencies, payer rule violations, and missing or invalid prior authorizations. Current denial prevention often depends on manual review and deterministic claim-scrubbing rules. These approaches may not capture complex payer-specific interactions among documentation quality, procedure codes, diagnosis codes, and authorization history. This article proposes an explainable gradient boosting model for estimating the probability that a health insurance claim could be denied before submission. The model is intended to identify the specific claim-level factors contributing to denial risk. The proposed framework uses a gradient-boosted tree ensemble trained conceptually on historical claims and remittance data. SHAP-based explanations would provide both global and local interpretability for denial-risk predictions. Conceptually, the model would return a denial risk score alongside an explanation of contributing factors such as incomplete documentation, diagnosis-code mismatch, expired authorization, or payer rule conflict. These outputs would support targeted pre-billing review rather than broad manual auditing. An explainable denial prediction model could help shift revenue cycle management from reactive appeals toward proactive prevention. Transparent reasoning would be essential for revenue cycle staff, clinical documentation teams, coders, and compliance stakeholders.
UR - https://cirpublications.com/w681883871
ER -