ArticleApplied clinical informatics2014
Development and validation of a computer-based algorithm to identify foreign-born patients with HIV infection from the electronic medical record.
Article in Applied clinical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Development and Evaluation of Clinical Decision Support for Immigrant Child Health Screening in Primary Care.Applied clinical informatics · 2025Article
- Automated HIV Case Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database.JAMIA open · 2022Article
- A Phenotyping Algorithm to Identify People With HIV in Electronic Health Record Data (HIV-Phen): Development and Evaluation Study.JMIR formative research · 2021Article
- Algorithm to identify transgender and gender nonbinary individuals among people living with HIV performs differently by age and ethnicity.Annals of epidemiology · 2021Article
- Using Electronic Health Record Data to Study Latino Immigrant Populations in Health Services Research.Journal of immigrant and minority health · 2020Article
- Risk Factors for Indeterminate Outcome on Interferon Gamma Release Assay in Non-US-Born Persons Screened for Latent Tuberculosis Infection.Open forum infectious diseases · 2018Article
- Development and validation of an electronic medical record (EMR)-based computed phenotype of HIV-1 infection.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Foreign-born status as a predictor of engagement in HIV care in a large US metropolitan health system.AIDS care · 2017Article
- PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.Journal of the American Medical Informatics Association : JAMIA · 2016Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo 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.
methodsWe 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.
resultsWe 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.
conclusionA 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.
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