TY - JOUR
T1 - Interpretable Machine Learning Model for Predicting Prior Authorization Approval Delays Using Payer-Specific Rules, Clinical Documentation Features, Procedure Type, Medical Necessity Indicators, and Historical Approval Timelines
AU - Nguyen Van Nam
AU - Tran Thi Hoa
AU - Le Minh Duc
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2025
VL - 5
IS - 2
DO - 10.68159/o275241982
SP - 115
N2 - Prior authorization delays impede timely patient care and contribute to administrative pressure across clinical and revenue cycle workflows. These delays can affect scheduling, medication access, procedural planning, and patient confidence in the care process. Current authorization management tools are largely reactive and often focus on tracking request status after submission. They rarely predict which requests are likely to experience approval delays or explain the operational, clinical, or payer-specific reasons behind those delays. This article proposes an interpretable machine learning model for predicting the likelihood of prior authorization approval delays. The model is designed to provide transparent, request-level explanations that can guide pre-submission correction and authorization preparation. The proposed framework uses a gradient-boosted tree model trained conceptually on historical authorization requests. Inputs include payer-specific rules, clinical documentation features, procedure type, medical necessity indicators, and historical approval timelines, with SHAP used to attribute predicted delay risk to individual features. Conceptually, the model would output a delay probability and an explanation of the dominant drivers of that prediction. These drivers could include incomplete documentation, mismatch with payer medical necessity criteria, procedure categories associated with additional review, or payer-procedure combinations with historically slow turnaround. An interpretable prior authorization delay model could support earlier correction of incomplete requests, reduce administrative waste, and improve patient access. By aligning predictive analytics with transparent explanations, the framework could make authorization preparation more proactive and accountable.
UR - https://cirpublications.com/o275241982
ER -