一个基于遗传算法的战略规划框架,以优化新西兰诺斯兰的一般实践的可访问性和成本
Fulvio D Lopane1, Melanie Reuter-Oppermann2, Andrea Raith3
1Centre for Advanced Spatial Analysis, University College London, London, UK.
Health systems (Basingstoke, England)
|October 20, 2023
概括
一般医生 (GP) 短缺在全球范围内影响农村初级保健. 本研究使用遗传算法来评估目前的GP覆盖范围,并计划未来的实践地点,以新西兰诺斯兰州为案例研究.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 地理信息系统 地理信息系统
- 运营研究 运营研究
背景情况:
- 全球缺乏全科医生 (GPs) 严重影响医疗保健的可获得性,特别是在农村地区.
- 对初级保健服务的有效规划对于应对全科医生分发挑战至关重要.
- 对GP实践位置的现有文献进行了审查,以告知未来的策略.
研究的目的:
- 开发和提出一种方法来评估当前的全科医生 (GP) 覆盖范围.
- 通过使用遗传算法框架来确定最佳的未来GP实践地点.
- 支持决策者在缺乏服务的地区规划初级保健服务.
主要方法:
- 关于全科医生 (GP) 实践位置模型的文献综述.
- 开发基于遗传算法的框架,用于GP覆盖率评估和未来的位置规划.
- 在新西兰农村Northland地区的案例研究应用.
- 关键输入参数的灵敏度分析.
主要成果:
- 拟议的遗传算法框架有效评估当前的GP覆盖范围.
- 确定了北兰未来GP实践的潜在最佳地点.
- 灵敏度分析提供了对各种参数对GP分布的影响的见解.
结论:
- 基因算法方法为规划GP服务分发提供了一个强大的方法.
- 调查结果可以为改善农村初级保健服务的政策和资源配置提供信息.
- 该研究强调,需要进行战略规划,以缓解农村地区的GP短缺问题.
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