评估电子临床数据对高成本患者的预测和分析能力
Saathvika Diviti1, Adam Wilcox1
1Washington University School of Medicine, St Louis, Missouri.
概括
为了降低医疗保健成本,热点被电子健康记录 (EHR) 数据问题所阻碍. 预测性患者信息往往无法获得或不一致,这限制了其在识别高风险个人的有效性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健管理的管理
- 预测分析是一种预测分析.
背景情况:
- 热点战略旨在通过识别大量使用患者来降低高医疗保健成本.
- 在获得和利用电子健康记录 (EHR) 中的全面数据方面存在挑战.
- 热点的有效性取决于预测性患者信息的可用性和质量.
研究的目的:
- 评估EHR中预测信息的可用性和可访问性.
- 评估这些信息在识别负面健康结果的患者中的有用性.
- 了解预测医疗保健模型当前数据的局限性.
主要方法:
- 在巴恩斯犹太医院进行了一项描述性研究.
- 手动审查100个高资源和100个控制患者的电子表格.
- 为了分析数据,生成了描述性统计数据.
主要成果:
- 大多数患者记录缺乏一致的预测信息.
- 发现预测信息的来源是不一致的.
- 虽然一些患者子组显示了预测价值,但它们仅占总数的一小部分,而"其他"类别既大又具有预测性.
结论:
- 目前的EHR数据对有效的患者热点进行了重大限制.
- 不一致的数据可用性和可访问性阻碍了对负面健康结果的预测.
- 需要进一步的研究来应对数据挑战,并改进预测性医疗保健策略.
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