TY - JOUR
T1 - Machine Learning Model for Predicting High Administrative Burden Encounters Using Documentation Complexity, Billing Requirements, Insurance Rules, Care Coordination Needs, and Provider Workload Indicators
AU - Mohamed Salah
AU - Youssef Karim
AU - Ahmed Nabil
AU - Mahmoud Adel
AU - Karim Hassan
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2026
VL - 6
IS - 2
DO - 10.68159/n429666118
SP - 134
N2 - Administrative tasks surrounding a clinical encounter include documentation, coding, billing, insurance verification, prior authorization, and care coordination. These tasks are unevenly distributed across encounters and can consume substantial clinical and operational capacity. Health systems often detect administrative overload only after coding backlogs, payer denials, unanswered messages, or staff overtime have already emerged. The absence of an encounter-level prediction tool limits the ability of practices to intervene before administrative work accumulates. This article proposes a machine learning model that predicts whether an encounter is likely to become a high-administrative-burden event. The model uses documentation complexity, billing requirements, insurance rules, care coordination needs, and provider workload indicators as core predictors. A gradient-boosted classification framework is conceptually specified using historical encounter, billing, scheduling, payer, and workload data. The model would generate an encounter-level burden risk score and provide interpretable feature-domain contributions to support operational decisions. Conceptually, the model could identify encounters likely to require additional coding review, prior authorization follow-up, payer documentation, or multidisciplinary coordination. The resulting risk score would support proactive staffing, pre-visit review, and workflow routing. A predictive model for high administrative burden encounters could help shift healthcare administration from reactive queue management to anticipatory operational planning. Such a model may support revenue integrity, reduce avoidable rework, and lessen administrative strain on clinicians and staff.
UR - https://cirpublications.com/n429666118
ER -