在初级保健数据中定义心理健康状况:采用混合定性和定量方法的验证研究
Juan Carlos Bazo-Alvarez1, Christina Avgerinou1, Danielle Nimmons1
1Research Department of Primary Care and Population Health, University College London (UCL), London, UK.
Journal of evaluation in clinical practice
|December 11, 2024
概括
这项研究使用初级保健数据验证了12种心理健康 (MH) 条件的医学阅读代码列表. 结合医生共识和统计分析的混合方法方法确保了准确的分类,以改善MH条件的识别.
科学领域:
- 医疗信息学医学信息学
- 临床流行病学临床流行病学
- 健康数据科学健康数据科学
背景情况:
- 在初级保健中准确识别心理健康 (MH) 状况对于有效的患者管理至关重要.
- 现有的读取代码列表需要验证以获得可靠的MH条件表型.
- 初级保健数据为开发和验证临床规范提供了宝贵的资源.
研究的目的:
- 通过使用英国初级保健数据,验证阅读代码列表来定义一系列心理健康状况.
- 开发一个强大的MH条件分类系统,而不依赖外部数据集.
- 将定性临床专业知识与定量数据分析相结合,以进行全面的代码列表验证.
主要方法:
- 一个三步验证过程,包括定性和定量方法.
- 定性步骤:由六名医生组成的小组对Read代码进行深入审查和选择.
- 定量步骤:对英国初级保健数据进行探索性因子分析,以将阅读代码分为MH状况类别.
主要成果:
- 1638 阅读代码是通过协商一致选择的,形成12个MH障碍类别.
- 量化分析选择了1364个读取代码,并确定了五个不同的类别.
- 两种分类方法都显示出很好的一致性,其中的差异指导了重新分类的决定.
结论:
- 成功生产了一组经过验证的阅读代码列表,用于12种心理健康状况.
- 该研究采用了混合方法,将临床共识与定量集群分析相结合.
- 开发的代码列表为在初级保健数据中识别MH疾病提供了可靠的工具.
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