Reinforcement Learning-Based Clinical Pathway Optimization and Its Economic Impact on Healthcare Operations
Keywords:
reinforcement learning, clinical pathway optimization, healthcare operations, decision-making under uncertainty, hospital resource allocation, operational efficiencyAbstract
Intelligent decision-support mechanisms improve patient outcomes and operational efficiency in complex healthcare delivery systems with clinical capacity and budgetary constraints. This study analyzes reinforcement learning–based clinical pathway optimization frameworks' dynamic treatment sequence, care transition, and resource allocation under uncertainty. By modeling clinical operations as sequential decision-making problems, reinforcement learning agents find optimal policies that balance patient risk profiles, treatment efficacy, and operational constraints. The suggested technique uses clinical and administrative data to discover real-world correlations between treatment options, stay length, staffing utilization, and bed occupancy. Research examines upgraded routes' downstream economic effects on operational expenditures, personnel deployment, treatment resource utilization, and hospital throughput beyond clinical performance criteria. Simulation and retrospective approaches assess complex healthcare system policy robustness, safety, and generalizability. Reinforcement learning–driven route optimization may improve service quality and cost. Healthcare reinforcement learning deployment, governance, and scalability finish the study.
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References
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