ISSN :2582-9793

Optimizing Healthcare Ecosystem Performance - A Computational Study of Integrated Patient Assistance in Primary Care

Original Research (Published On: 28-Dec-2024 )
Optimizing Healthcare Ecosystem Performance - A Computational Study of Integrated Patient Assistance in Primary Care
DOI : https://dx.doi.org/10.54364/AAIML.2024.44178

Francesco Nucci and Gabriele Papadia

Adv. Artif. Intell. Mach. Learn., 4 (4):3114-3124

Francesco Nucci : University of Salento

Gabriele Papadia : Department of Innovation Engineering, University of Salento

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DOI: https://dx.doi.org/10.54364/AAIML.2024.44178

Article History: Received on: 20-Oct-24, Accepted on: 21-Dec-24, Published on: 28-Dec-24

Corresponding Author: Francesco Nucci

Email: francesco.nucci@unisalento.it

Citation: Francesco Nucci, Gabriele Papadia. (ITALY) (2024). Optimizing Healthcare Ecosystem Performance - A Computational Study of Integrated Patient Assistance in Primary Care. Adv. Artif. Intell. Mach. Learn., 4 (4 ):3114-3124


Abstract

    

Home health care professionals provide medical services to patients in their homes. With rising demand, it’s crucial to manage operational costs effectively while ensuring satisfaction for  patients. This study presents a bi-objective optimization model aimed at resolving routing and scheduling challenges in home health care, with a focus on both system efficiency and patient accessibility. A Mixed-Integer Linear Programming Model (MILP) is developed. To tackle computational time challenges, we propose a Non-dominated Sorting Genetic Algorithm II to solve the multi-objective optimization problems. The evaluation of Pareto fronts demonstrates the method’s efficiency. We apply the method in a real-world case study to provide managerial implications.

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