An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning
Rajesh Kumar Jha, Sujoy Bag, Debbani Koley, Giridhar Reddy Bojja, Subhas Barman
Abstract
Insufficient doctors and nurses enable a weak healthcare system in developing and undeveloped countries. This study aims to mitigate the demand-supply gap of doctor patients of an undeveloped or developing county. We observe people in a rural area, unaware of an appropriate hospital and doctors for their disease, and randomly go to the nearest hospital to check-up their health. However, each doctor has expertise in a specific disease, and hospitals' treatment performance varies. As a result, the patient engages multiple doctors and hospitals to cure their disease. This study develops as an appropriate and cost-effective hospital recommender system for a specific disease to provide the best hospital to a patient using deep reinforcement learning. Hence, the patient's treatment time, insignificant medicine consumption, the side effect of using inappropriate medicine, and a doctor's load can be minimized using the developed hospital recommender system.