A genetic algorithm-based strategic planning framework for optimising accessibility and costs of general practices in Northland, New Zealand

Fulvio D Lopane; Melanie Reuter-Oppermann; Andrea Raith; Daniel J Exeter; Ilze Ziedins; Richard Dawson

doi:10.1080/20476965.2023.2174454

A genetic algorithm-based strategic planning framework for optimising accessibility and costs of general practices in Northland, New Zealand

Health Syst (Basingstoke). 2023 Feb 16;12(3):332-356. doi: 10.1080/20476965.2023.2174454. eCollection 2023.

Authors

Fulvio D Lopane¹, Melanie Reuter-Oppermann², Andrea Raith³, Daniel J Exeter⁴, Ilze Ziedins⁵, Richard Dawson⁶

Affiliations

¹ Centre for Advanced Spatial Analysis, University College London, London, UK.
² Information Systems group, Department of Law and Economics, Technical University of Darmstadt, Darmstadt, Germany.
³ Engineering Science, Faculty of Engineering, University of Auckland, New Zealand.
⁴ Epidemiology and Biostatistics, Faculty of Medical and Health Sciences, University of Auckland, New Zealand.
⁵ Statistics, Faculty of Science, University of Auckland, New Zealand.
⁶ School of Engineering, Newcastle University, Newcastle upon Tyne, UK.

Abstract

Shortage of general practitioners (GP) is a challenge worldwide, not only in Europe, but also in countries like New Zealand. Providing primary care in rural areas is especially challenging. In order to support decision makers, it is necessary to first assess the current GP coverage and then to determine different scenarios and plans for the future. In this paper, we first present a thorough overview of related literature on locating GP practices. Second, we propose an approach for assessing the GP coverage and determining future GP locations based on a genetic algorithm framework. As a use case, we have chosen the rural New Zealand region of Northland. We also perform a sensitivity analysis for the main input parameters.

Keywords: General practitioners; New Zealand; decision support; spatial optimisation.