Published 12-02-2024
Keywords
- Deep Reinforcement Learning,
- Hospital Recommender System
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
This study addresses the critical healthcare challenges in rural Kenya by developing an innovative hospital recommender system using deep reinforcement learning. The system aims to optimize patient-hospital matching, considering factors such as hospital capacity, treatment performance, and cost-effectiveness. By analyzing data from 50 rural Kenyan hospitals and 10,000 patient records, we demonstrate that our system can reduce average treatment time by 37%, decrease inappropriate medication use by 42%, and improve overall patient satisfaction by 28%. The successful implementation of this system in rural Kenya validates the algorithm's accuracy and adaptability, and we plan to integrate it into more rural healthcare systems across the country. Our findings suggest that this AI-driven approach has the potential to significantly improve healthcare delivery in resource-constrained environments, particularly in developing countries.
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