Development and validation of a computer-based algorithm to identify foreign-born patients with HIV infection from the electronic medical record
Development and validation of a computer-based algorithm to identify foreign-born patients with HIV infection from the electronic medical record
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DOI：
10.4338/aci-2014-02-ra-0013
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发表时间：
2014-01-01
影响因子：
2.9
通讯作者：
Regan, S.
中科院分区：
文献类型：
--
作者：
Levison, J.;Triant, V.;Regan, S.
目的：为了开发和验证一个有效的和准确的方法，以确定外国出生的患者从一个大型的患者数据登记，以促进人口为基础的健康outcomes research.Methods：我们开发了一个三阶段的算法分类外国出生的艾滋病病毒感染的患者接受治疗的大型美国医疗保健系统（2001年1月1日至2012年3月31日）（n = 9,114）。在第一阶段，我们将那些编码为非英语语言的人归类为外国出生的人。在第二阶段，我们搜索了剩余患者的自由文本电子病历（EMR）笔记，以获得与出生地和语言相关的关键字。没有关键词的患者被归类为美国出生。在第3阶段，我们检索并查看了关键字周围的50个字符的文本窗口（i。e. #21453;，其余患者。为了验证该算法，我们进行了图表审查，并要求所有HIV医生（n = 37）对他们的患者（n = 957）进行分类。我们计算算法的灵敏度和specificity.Results：我们排除了160/957，因为医生表示患者没有HIV感染（n = 54），“不是我的病人”（n = 103），或有未知的出生地（n = 3），留下797进行分析。在第一阶段，提供者同意95名讲外语的人中有71名是在外国出生的。大多数分歧（23/24）涉及出生在波多黎各的患者。在第2阶段，通过病历审查，49/50例无关键词的患者被归类为美国出生。在第3阶段，与全图表审查相比，令牌审查正确分类了55/60例患者（92%），灵敏度为93%（CI：84.4，100%），特异性为90%（CI：74.3，100%）。在应用三阶段算法后，2，102/9，114（23%）例患者被归类为外国出生。当与医生的反应相比，估计的算法的灵敏度为94%（CI：90.9，97.2%）和特异性92%（CI：89.7，94.1%），92%的正确classifed.Conclusion：一个基于计算机的算法分类外国出生的状态在一个大的HIV感染队列有效和准确。这种方法可以用来改善基于EMR的结果研究。
Objective: To develop and validate an efficient and accurate method to identify foreign-born patients from a large patient data registry in order to facilitate population-based health outcomes research.Methods: We developed a three-stage algorithm for classifying foreign-born status in HIV-infected patients receiving care in a large US healthcare system (January 1, 2001-March 31, 2012) (n = 9,114). In stage 1, we classified those coded as non-English language speaking as foreign-born. In stage 2, we searched free text electronic medical record (EMR) notes of remaining patients for keywords associated with place of birth and language spoken. Patients without keywords were classified as US-born. In stage 3, we retrieved and reviewed a 50-character text window around the keyword (i. e. token) for the remaining patients. To validate the algorithm, we performed a chart review and asked all HIV physicians (n = 37) to classify their patients (n = 957). We calculated algorithm sensitivity and specificity.Results: We excluded 160/957 because physicians indicated the patient was not HIV-infected (n = 54), "not my patient" (n = 103), or had unknown place of birth (n = 3), leaving 797 for analysis. In stage 1, providers agreed that 71/95 foreign language speakers were foreign-born. Most disagreements (23/24) involved patients born in Puerto Rico. In stage 2, 49/50 patients without keywords were classified as US-born by chart review. In stage 3, token review correctly classified 55/60 patients (92%), with 93% (CI: 84.4, 100%) sensitivity and 90% (CI: 74.3, 100%) specificity compared with full chart review. After application of the three-stage algorithm, 2,102/9,114 (23%) patients were classified as foreign-born. When compared against physician response, estimated sensitivity of the algorithm was 94% (CI: 90.9, 97.2%) and specificity 92% (CI: 89.7, 94.1%), with 92% correctly classified.Conclusion: A computer-based algorithm classified foreign-born status in a large HIV-infected cohort efficiently and accurately. This approach can be used to improve EMR-based outcomes research.