A Decision Support System for Smart Health Care

The Smart City has become a renowned opportunity to improve the quality of everyday urban life activities, particularly in smart health-care domain. We address, in this paper, a very recurring problem within hospitals that consists in assigning patients to a limited number of beds. This problem becomes more complex when dealing with real-time requests, and the time factor becomes the most critical. In such situations, a set of patients arriving over time are to be examined, and their clinical states are to be well specified in order to decide whether they need admission and hospitalization or not. In case of hospitalization, the hospital staff should assign patients to beds while taking into account beds availability in terms of specialization and patient needs. All these actions should be well planned in order to maximize the quality of service in the hospitals. This challenging problem can be modeled as a dynamic assignment problem that handles a set of patients to be assigned to a set of beds over a given time horizon while taking into account availability constraints expressed in terms of beds, medical necessity, and patients demands, which are subject to modification over time. To deal with this problem, a decision support system (DSS) is developed to assist the hospital staff in the assignment activity, based on the results of a new hybrid evolutionary approach that combines the genetic algorithm with efficient evolutionary techniques and other methods from the literature. We show, with a true deep experimental study, the effectiveness of our approximate approach to solve several benchmark instances reported in the literature related to the smart health-care system. Our hybrid algorithm also outperforms efficient methods from the literature which have the previously best known results.