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

Fulvio D LopaneMelanie Reuter-OppermannsAndrea RaithDaniel John ExeterIlze ZiedinsRichard J Dawson
Published in: Health systems (Basingstoke, England) (2023)
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.
Keyphrases
  • primary care
  • healthcare
  • machine learning
  • deep learning
  • genome wide
  • systematic review
  • climate change
  • copy number
  • south africa
  • health insurance
  • affordable care act
  • gene expression
  • dna methylation
  • decision making