Reinforcement Learning in Healthcare: Enhancing Treatment and Resource Allocation

Abstract

Reinforcement learning (RL) has become a promising strategy for optimizing patient treatment and resource allocation in changing clinical settings. RL-driven solutions for personalized treatment, hospital resource management, emergency department triage, drug dosage optimization, and adaptive staffing are explored in this research. We use datasets including MIMIC-III, eICU, NHAMCS, DCCT, AHA Annual Survey Database, and Medicare Cost Reports to create realistic healthcare scenarios and assess the effect of RL. Statistical graphs show how RL performs better than conventional approaches when it comes to information in increasing the success in treatment, shortening patient wait, reducing resource utilization, and lowering medical expenditures. These findings demonstrate RL’s ability to improve healthcare decision-making, resulting in better patient outcomes as well as more efficient use of healthcare resources, all while considering the computational and ethical challenges surrounding AI-assisted clinical decision-making.

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