Analysis of identifier performance using a deterministic linkage algorithm.

Proceedings. AMIA Symposium Pub Date : 2002-01-01
Shaun J Grannis, J Marc Overhage, Clement J McDonald
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引用次数: 0

Abstract

As part of developing a record linkage algorithm using de-identified patient data, we analyzed the performance of several demographic variables for making linkages between patient registry records from two hospital registries and the Social Security Death Master File. We analyzed samples from each registry totaling 6,000 record-pairs to establish a linkage gold-standard. Using Social Security Number as the exclusive linkage variable resulted in substantial linkage error rates of 4.7% and 9.2%. The best single variable combination for finding links was Social Security Number, phonetically compressed first name, birth month, and gender. This found 87% and 88% of the links without any false links. We achieved sensitivities of 90% to 92% while maintaining 100% specificity using combinations of social security number, gender, name, and birth date fields. This represents an accurate method for linking patient records to death data and is the basis for a more generalized de-identified linkage algorithm.

使用确定性链接算法分析标识符性能。
作为使用去识别患者数据开发记录链接算法的一部分,我们分析了几个人口统计变量在两个医院登记处的患者注册记录与社会保障死亡主文件之间建立链接的性能。我们分析了来自每个注册表的样本,总共6000个记录对,以建立一个链接金标准。使用社会保险号作为唯一的联动变量,导致了大量的联动错误率,分别为4.7%和9.2%。查找链接的最佳单变量组合是社会安全号码、语音压缩的名字、出生月份和性别。分别有87%和88%的链接没有虚假链接。使用社会保险号、性别、姓名和出生日期字段的组合,我们实现了90%至92%的灵敏度,同时保持了100%的特异性。这代表了一种将患者记录与死亡数据联系起来的准确方法,并且是更广泛的去识别链接算法的基础。
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