Abstract：
OBJECTIVE: Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts.
METHODS: We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility.
RESULTS: We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms.
CONCLUSIONS: Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.
METHODS: We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility.
RESULTS: We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms.
CONCLUSIONS: Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.
摘要：
目的：诊断错误是临床实践中可预防伤害的主要原因。需要可实施的工具来量化和瞄准这个问题。为了解决这个差距，我们旨在通过开发诊断错误的症状-疾病对分析(SPADE)框架的可计算表型来推广该框架,然后演示该模式如何应用于多种临床环境.
方法：我们为SPADE流程创建了一个信息模型，然后将来自电子健康记录(EHR)的数据字段和使用中的索赔数据映射到该模型，以创建SPADE信息模型(意图)和SPADE可计算表型(扩展)。后来，我们验证了可计算表型，并在三个不同卫生系统的四个案例研究中对其进行了测试，以证明其实用性。
结果：我们使用四个不同的案例研究在三个不同的位点定位并测试了SPADE可计算表型。我们表明，用于计算SPADE基本度量的数据字段在EHR数据仓库中完全可用，可用于提取，并且可以从提供商和/或保险公司的角度实施SPADE框架，它们可以在许多卫生系统上实施，以便将来监测误诊相关危害。
结论：SPADE基本措施的数据在EHR和行政索赔中很容易获得。数据提取的方法具有潜在的普遍适用性，并且提取的数据可以在网络系统中方便地获得。需要进一步的研究来验证具有不同数据基础设施的不同设置的可计算表型。
方法：我们为SPADE流程创建了一个信息模型，然后将来自电子健康记录(EHR)的数据字段和使用中的索赔数据映射到该模型，以创建SPADE信息模型(意图)和SPADE可计算表型(扩展)。后来，我们验证了可计算表型，并在三个不同卫生系统的四个案例研究中对其进行了测试，以证明其实用性。
结果：我们使用四个不同的案例研究在三个不同的位点定位并测试了SPADE可计算表型。我们表明，用于计算SPADE基本度量的数据字段在EHR数据仓库中完全可用，可用于提取，并且可以从提供商和/或保险公司的角度实施SPADE框架，它们可以在许多卫生系统上实施，以便将来监测误诊相关危害。
结论：SPADE基本措施的数据在EHR和行政索赔中很容易获得。数据提取的方法具有潜在的普遍适用性，并且提取的数据可以在网络系统中方便地获得。需要进一步的研究来验证具有不同数据基础设施的不同设置的可计算表型。
