TY - JOUR
T1 - Interpretable Random Forest Model for Predicting Diagnostic Test Overuse in Outpatient Clinics Using Visit Type, Physician Ordering History, Patient Complexity, Prior Test Results, and Guideline-Based Appropriateness Indicators
AU - Nguyen Thanh Huy
AU - Pham Quang Minh
AU - Le Thi Bich
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2022
VL - 2
IS - 2
DO - 10.68159/c143657617
SP - 72
N2 - Diagnostic test overuse in outpatient settings contributes to wasteful spending and may expose patients to unnecessary downstream testing, anxiety, and treatment cascades. Common examples include low-value imaging, repetitive laboratory testing, and routine preoperative or annual tests without clear clinical indication. Existing approaches often rely on manual review, retrospective measurement, or rigid rule-based filters. These approaches may miss context-sensitive overuse shaped by visit type, clinician habit, patient complexity, and recent test history. This manuscript proposes an interpretable random forest model that predicts whether a diagnostic test order could represent overuse. The model integrates visit-level, physician-level, patient-level, prior-result, and guideline-based appropriateness features. The proposed model would use historical outpatient orders labelled through appropriateness criteria or expert review. SHAP-based explanations would be used to make each prediction interpretable to clinicians and quality leaders. Conceptually, the model would estimate an overuse probability and identify the relative contribution of physician ordering history, patient complexity, prior results, visit type, and guideline indicators. These explanations would support targeted feedback rather than opaque surveillance. An interpretable random forest model could function as a personalised overuse screening tool within clinical decision support. Its value would depend on transparent explanations, careful guideline alignment, and thoughtful integration into outpatient workflows.
UR - https://cirpublications.com/c143657617
ER -