Implementing Optimization Algorithms in Swedish Healthcare AI-Based Simulation and Optimization of Internal Patient Trans- ports at Sahlgrenska University Hospital

Abstract

Artificial Intelligence (AI) is increasingly being integrated into the field of medicine to improve treatment outcome, enhance efficiency and streamline internal hospi- tal transports. This study investigates the application of AI to optimize patient transport at Sahlgrenska University Hospital. The goal is to identify different meth- ods that can help to improve patient flow and resource allocation. Interviews at Sahlgrenska along with an expert within AI were conducted to gain a better un- derstanding of how AI can be altered and used. Anonymous important data from Sahlgrenska with patient transport times, different departments and porter avail- ability were analyzed over the course of a year’s time. The main challenge was creat- ing an AI-based simulation system that accurately represents Sahlgrenska and also showcases a model of how the porters transport patients throughout the hospital. This was done with genetic algorithms (GA) as well as integer linear programming (ILP) and for the systems to give valid data the hospital transport data must closely replicate the actual size and operations of the hospital. The results show that the AI-based system could substantially improve hospital transports in regards to distributing the workload more equitably, maximizing ef- ficiency and reducing cost as well as preventing further financial losses. Finally a conclusion that an AI-based system for coordinating patient transport could be drawn. Challenges for implementations include system integration, data privacy and ethical task allocation. Nevertheless, strategic planning along with investments, AI offers considerable potential when it comes to modernizing hospital logistics and fostering a more efficient and patient-oriented healthcare system. Keywords: Artificial Intelligence (AI), Patient Transport Optimization, Healthcare Logistics, Simulation Modeling, Genetic Algorithms (GA), Integer Linear Program- ming (ILP), Workload Balancing, Resource Allocation, Efficiency Improvement, Sahlgrenska University Hospital