Can Machine Learning Algorithms use Contextual Factors to Detect Unwarranted Clinical Variation from Electronic Health Record Encounter Data during the Treatment of Children Diagnosed with Acute Viral Pharyngitis

Rationale, Aims and ObjectivesUnwarranted clinical variation (UCV) in patient care often arises from contextual factors and contributes to increased costs, unnecessary treatments, and deviations from evidence-based practice. Detecting UCV is challenging due to the complexity of care decisions. Current approaches rely on centralized data aggregation and mixed-effects regression, which estimate relative variation but cannot detect absolute variation. Moreover, machine learning (ML) methods leveraging contextual factors for UCV detection are lacking. The objective is to demonstrate the feasibility of ML for identifying absolute UCV using contextual features extracted from electronic health records (EHR) and identify the factors correlated with UCV in treating acute viral pharyngitis in children. MethodsWe conducted a retrospective study of pediatric ambulatory visits (ICD-10 J02.8) at an academic health system. The use case focused on unwarranted antibiotic prescriptions for acute viral pharyngitis. We trained ensemble ML models--Random Forest, CatBoost, and Explainable Boosting Machine (EBM)--using encounter-level EHR data. Performance was evaluated using nested cross-validation and AUC metrics. We also compared CatBoost models trained on curated (gold-standard) versus weak labels. ResultsAll three ML models demonstrated robust performance, with a median AUC of 0.91, using data from 24 clinics, 81 providers, and 122 patients within an academic health system. CatBoost models trained on weak labels exhibited performance comparable to those trained on gold-standard labels. Feature importance analysis indicated that site-level and provider-level case volumes were the most influential predictors, followed by provider credential, years of experience, and encounter type. Notably, lower provider case volumes were associated with a reduced likelihood of inappropriate treatment. ConclusionsClassical ML models can effectively detect absolute UCV using contextual EHR features. Explainable models such as EBM offer interpretability critical for clinical adoption. These findings support ML-based approaches as scalable alternatives to traditional statistical methods for UCV detection without requiring centralized data analysis.