Optimizing Clinical Resource Intelligence and Cross-Hospital Allocation Redundancies in the United States

• Abey City

  Texas University

  Author

Maternal mortality and morbidity in the United States remain at crisis levels, with rates more than doubling over the past three decades and significant geographic and racial disparities persisting. The proliferation of maternal care deserts—counties lacking hospital-based obstetric services or maternity care providers—has created urgent challenges for healthcare delivery and resource allocation. Existing approaches to predicting maternal healthcare access gaps rely primarily on static measures that fail to capture the dynamic interplay between population mobility, healthcare facility capacity, and temporal service utilization patterns. This study addresses this critical gap by proposing a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal dependency modeling to predict regional maternal care desert emergence. The framework incorporates multi-scale spatial features, including hospital service area delineations, emergency response corridors, and healthcare facility distributions, alongside temporal features capturing seasonal utilization patterns and demographic shifts. Our model achieved 89.4% accuracy (AUC = 0.94) in predicting care desert emergence 90 days in advance, outperforming baseline models (random forest: 78.2%, logistic regression: 74.1%). Feature importance analysis identified provider-to-patient ratio, distance to nearest obstetric facility, and Medicaid acceptance rates as the strongest predictors. The framework's cross-hospital allocation redundancy optimization demonstrated a 23.4% improvement in resource allocation efficiency and a 15.7% reduction in simulated emergency response times. This research provides a replicable, data-driven approach for healthcare administrators and policymakers to proactively identify emerging care gaps and optimize clinical resource distribution, potentially reducing preventable maternal deaths through earlier intervention.

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