使用电子健康记录来识别喘相关的急性护理遭遇
Michelle L Stransky1, Miriam Bremer-Kamens2, Caroline J Kistin3
1Center for the Urban Child and Healthy Family (ML Stransky and M Bremer-Kamens), Boston Medical Center, Boston, Mass; Department of Pediatrics (ML Stransky, RT Cohen), Boston University Chobanian and Avedisian School of Medicine, Boston, Mass.
Academic pediatrics
|May 18, 2024
概括
电子健康记录 (EHR) 算法可以准确识别与喘相关的紧急访问和住院治疗. 这些工具通过超越简单的诊断代码来改善喘护理评估.
科学领域:
- 医疗信息学 医疗信息学
- 临床研究 临床研究
- 在医疗保健中的数据科学.
背景情况:
- 电子健康记录 (EHR) 数据对于改善医疗保健至关重要,但准确识别喘等特定疾病需要的不仅仅是诊断代码.
- 仅仅依靠诊断代码可能导致与喘相关的遭遇被错误分类,阻碍有效的护理评估.
研究的目的:
- 开发和验证使用EHR数据的原型算法,以准确地将急诊室 (ED) 遭遇和住院治疗归类为与喘相关的.
- 创建可行,透明和精简的算法,在喘护理评估的大数据中运行临床概念.
主要方法:
- 专家临床医生对467例儿科ED遭遇和136例住院喘诊断代码进行了多阶段图表审查.
- 使用EHR数据 (文本和非文本字段) 开发了基于规则的算法,并根据临床医生衍生的参考标准进行了验证.
- 使用灵敏度,特异性,正预测值 (PPV) 和负预测值 (NPV) 来评估性能.
主要成果:
- 临床医生认为38%的ED遭遇绝对或可能与喘有关,49%被认为与喘无关.
- 使用文本和非文本EHR字段的基于规则的算法在识别喘相关的ED遭遇时实现了>95%的准确性 (灵敏度,特异性,PPV,NPV).
- 仅使用非文本字段的算法达到>87%的准确性,另一个算法识别了与喘相关的住院治疗.
结论:
- 仅仅是诊断代码就不足以准确地识别电子健康记录中的喘相关访问.
- 基于EHR的原型算法结合了额外的数据字段,可以可靠地预测临床医生识别的与喘相关的访问,准确度很高.
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