Predictive and Explainable Analytics for EHR Data Quality, Operational Risk, and Resource Allocation in U.S. Healthcare Systems

Abstract

This quantitative methodological study developed and validated an integrated predictive and explainable analytics framework for electronic health record (EHR) data quality, hospital operational risk, and resource allocation in U.S. healthcare systems. The study used a synthetic multi-site benchmark designed to reproduce realistic variation in EHR completeness, plausibility, interface latency, terminology mapping, patient flow, occupancy, staffing, discharge processes, and short-horizon resource demand without using protected health information. The final benchmark contained 526,480 encounter-day observations across 24 simulated hospitals in four U.S. regions and 52,560 hospital-day resource observations. Three linked analytical tasks were evaluated: prediction of critical EHR data-quality defects, prediction of 24-hour operational-risk events, and forecasting of next-day occupied-bed equivalents. Logistic regression, Random Forest, Support Vector Machine, artificial neural network, Gradient Boosting, and XGBoost models were compared for classification, while Elastic Net, Random Forest regression, XGBoost regression, long short-term memory, and a simple ensemble were assessed for resource forecasting. Explainability incorporated multivariable effect estimation, SHAP values, partial-dependence analysis, calibration, and subgroup evaluation. XGBoost achieved the strongest independent-test discrimination for critical data-quality defects (ROC-AUC = 0.971; sensitivity = 90.1%; specificity = 93.6%) and operational risk (ROC-AUC = 0.968; sensitivity = 90.6%; specificity = 93.1%). For 24-hour resource forecasting, XGBoost produced a mean absolute error of 6.1 occupied-bed equivalents and a mean absolute percentage error of 5.4%, while the XGBoost-LSTM ensemble improved MAPE to 5.1%. Interface latency, missingness volatility, terminology-mapping coverage, unit inconsistency, occupancy above 95%, discharge backlog, ED boarding load, nurse workload, and data-quality defect burden were among the strongest explainable predictors. Geographic and temporal holdouts showed modest performance deterioration, indicating useful but incomplete transportability. The results support treating data quality, operational risk, and resource allocation as a connected analytical system rather than independent informatics problems. The framework demonstrates how health systems can combine continuous data-quality intelligence, calibrated risk prediction, explainable resource forecasting, and governance controls within a scalable enterprise decision-support architecture.