Department of Pediatrics, University of California Los Angeles, Los Angeles, United States.
Department of Medicine, Clinical and Translational Science Institute, University of California Los Angeles, Los Angeles, United States.
Appl Clin Inform. 2020 Oct;11(5):725-732. doi: 10.1055/s-0040-1718374. Epub 2020 Nov 4.
Patients often seek medical treatment among different health care organizations, which can lead to redundant tests and treatments. One electronic health record (EHR) platform, Epic Systems, uses a patient linkage tool called Care Everywhere (CE), to match patients across institutions. To the extent that such linkages accurately identify shared patients across organizations, they would hold potential for improving care.
This study aimed to understand how accurate the CE tool with default settings is to identify identical patients between two neighboring academic health care systems in Southern California, The University of California Los Angeles (UCLA) and Cedars-Sinai Medical Center.
We studied CE patient linkage queries received at UCLA from Cedars-Sinai between November 1, 2016, and April 30, 2017. We constructed datasets comprised of linkages ("successful" queries), as well as nonlinkages ("unsuccessful" queries) during this time period. To identify false positive linkages, we screened the "successful" linkages for potential errors and then manually reviewed all that screened positive. To identify false-negative linkages, we applied our own patient matching algorithm to the "unsuccessful" queries and then manually reviewed a sample to identify missed patient linkages.
During the 6-month study period, Cedars-Sinai attempted to link 181,567 unique patient identities to records at UCLA. CE made 22,923 "successful" linkages and returned 158,644 "unsuccessful" queries among these patients. Manual review of the screened "successful" linkages between the two institutions determined there were no false positives. Manual review of a sample of the "unsuccessful" queries ( = 623), demonstrated an extrapolated false-negative rate of 2.97% (95% confidence interval [CI]: 1.6-4.4%).
We found that CE provided very reliable patient matching across institutions. The system missed a few linkages, but the false-negative rate was low and there were no false-positive matches over 6 months of use between two nearby institutions.
患者经常在不同的医疗机构寻求治疗，这可能导致重复的检查和治疗。一个名为 Epic Systems 的电子健康记录 (EHR) 平台使用了一种名为“无处不在的关怀”（Care Everywhere，CE）的患者链接工具，以匹配机构间的患者。在某种程度上，这种链接可以准确地识别不同组织之间的共享患者，从而有可能改善护理。
本研究旨在了解加利福尼亚州南部的两个相邻学术医疗保健系统（加州大学洛杉矶分校 [UCLA] 和雪松西奈医疗中心）之间，默认设置的 CE 工具识别相同患者的准确性如何。
我们研究了 2016 年 11 月 1 日至 2017 年 4 月 30 日期间，UCLA 从 Cedars-Sinai 收到的 CE 患者链接查询。我们构建了包含链接（“成功”查询）和非链接（“失败”查询）的数据集。为了识别假阳性链接，我们筛选了“成功”链接以查找潜在错误，然后手动审查所有筛选出的阳性链接。为了识别假阴性链接，我们将自己的患者匹配算法应用于“失败”查询，然后手动审查样本以识别错过的患者链接。
在 6 个月的研究期间，Cedars-Sinai 试图将 181567 个独特的患者身份链接到 UCLA 的记录中。CE 成功链接了 22923 次，并在这些患者中返回了 158644 次“失败”查询。对这两个机构之间筛选出的“成功”链接进行手动审查，结果没有发现假阳性。对“失败”查询的样本（n=623）进行手动审查，结果表明推断的假阴性率为 2.97%（95%置信区间 [CI]：1.6-4.4%）。
我们发现 CE 在机构间提供了非常可靠的患者匹配。该系统错过了一些链接，但假阴性率较低，在两个附近机构之间使用 6 个月的时间内没有假阳性匹配。
