Advancing competitive position in healthcare: a hybrid metaheuristic nutrition decision support system

Abstract

In the highly competitive environment of healthcare industry, managers incorporate patient centered care as a major component in the healthcare mission. Management information systems have gained increasing attention to develop effective plans for patient centered care, and quality improvement in healthcare organizations. Consumers are becoming actively involved in their health, which is great for patient engagement, but to meet this demand, healthcare providers need to make significant changes. Information technology and artificial intelligence can significantly improve workflows for healthcare professionals, and support the development of new services to meet changing consumer demands while maintaining costs. In this study, we aimed to build a decision support system including Genetic algorithm and Simulated Annealing metaheuristic machine learning methods for nutritionists to regulate a hospitals’ nutrition cycle and provide the best possible diet menu solutions while decreasing costs. Our main reason of using hybrid metaheuristic machine learning methods is to obtain a better quality solution on the problem dealt with study. Experimental results showed that; the proposed system is a novel, smart, cost-effective, flexible and machine learning based decision support system for nutritionist and healthcare managers.