The Healthcare system of a country is a crucial infrastructure that requires long-term capacity planning. The covid 19 outbreak pointed to the necessity of adequate hospital capacity, especially for developing countries like Bangladesh. The existing infrastructure planning of these countries emphasizes short-term goals and lacks vision planning for a long time horizon. It is in the country's best interest to make long-term capacity expansion plans, a strategy the developed countries banked to provide adequate healthcare facilities to their residents. However, no single solution is appropriate for a different region. Hence, it is required to comprehensively study the situation and constraints of the specific region before providing expensive capacity expansion plans. This work focuses on applying a deep Reinforcement Learning based long-term hospital bed capacity expansion plan. We utilize the RNN-LSTM based population forecast, deep Reinforcement Learning (RL) based policy-making, and state-of-the-art Artificial Intelligence techniques to provide a solution. We perform a case study for the Abhaynagar Upazila of Jessore, one of the largest cities in the southwest part of Bangladesh, to analyze the benefits of such an approach compared to existing myopic policies. The experiment results show that the deep RL-based policy significantly minimizes cost over a 30-year expansion plan.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.