描述一个复杂的初级医疗保健人口,以支持未来的决策支持计划
Jacqueline K Kueper1,2, Jennifer Rayner3,4, Merrick Zwarenstein1,3
1Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada.
这项研究使用了描述性流行病学和机器学习来分析超过20万名初级保健患者的健康记录. 结果描述了复杂的患者群体,以告知医疗保健决策支持工具的发展.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 使用医疗保健数据开发决策支持工具可以改善医疗保健提供和人口健康.
- 描述性流行病学和机器学习对于描述复杂的患者群体和为工具开发提供信息非常有价值.
研究的目的:
- 在健康社区联盟中描述初级保健客户的社会人口统计,临床和医疗保健利用模式.
- 为改进初级保健提供数据驱动决策支持工具的开发提供信息.
主要方法:
- 利用了来自221,047名成人初级保健客户 (2009-2019) 的电子健康记录数据.
- 应用传统的统计总结和三个无监督的机器学习技术.
- 探索了条件共发生的模式,护理提供者团队和护理频率.
主要成果:
- 81%的客户经历了具有可变并发症模式的多病症.
- 调查条件的患病率在1%至63%之间,风险不均.
- 医疗保健使用显示出异质的提供者组合和护理频率的变化.
结论:
- 通过流行病学方法证明了统计和机器学习方法的实用性.
- 提供了复杂的初级保健人口的概述,以支持决策工具的开发.
- 奠定了利益相关者参与医疗保健计划的基础.
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