Development of an electronic medical record-based algorithm to identify patients with unknown HIV status.
Individuals with unknown HIV status are at risk for undiagnosed HIV, but practical and reliable methods for identifying these individuals have not been described. We developed an algorithm to identify patients with unknown HIV status using data from the electronic medical record (EMR) of a large hea...
| Publicado en: | AIDS Care Vol. 26; no. 10; pp. 1318 - 1326 |
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| Autores principales: | , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Oct2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103971773&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103971773 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09540121 AAE jtl: AIDS Care issn: 09540121 maglogo: N pubinfo: dt: Oct2014 vid: 26 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 103971773 96952960 10.1080/09540121.2014.911813 NLM24779521 103971773 ppf: 1318 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Development of an electronic medical record-based algorithm to identify patients with unknown HIV status. aug: au: Felsen, Uriel R. Bellin, Eran Y. Cunningham, Chinazo O. Zingman, Barry S. affil: Division of Infectious Diseases, Montefiore Medical Center, Bronx, NY, USA sug: subj: HIV Infections Prevention and Control Patient Record Systems AIDS Serodiagnosis Disease Surveillance Algorithms HIV Infections Diagnosis Human Funding Source New York Confidence Intervals Sensitivity and Specificity Billing and Claims ab: Individuals with unknown HIV status are at risk for undiagnosed HIV, but practical and reliable methods for identifying these individuals have not been described. We developed an algorithm to identify patients with unknown HIV status using data from the electronic medical record (EMR) of a large health care system. We developed EMR-based criteria to classify patients as having known status (HIV-positive or HIV-negative) or unknown status and applied these criteria to all patients seen in the affiliated health care system from 2008 to 2012. Performance characteristics of the algorithm for identifying patients with unknown HIV status were calculated by comparing a random sample of the algorithm's results to a reference standard medical record review. The algorithm classifies all patients as having either known or unknown HIV status. Its sensitivity and specificity for identifying patients with unknown status are 99.4% (95% CI: 96.5–100%) and 95.2% (95% CI: 83.8–99.4%), respectively, with positive and negative predictive values of 98.7% (95% CI: 95.5–99.8%) and 97.6% (95% CI: 87.1–99.1%), respectively. Using commonly available data from an EMR, our algorithm has high sensitivity and specificity for identifying patients with unknown HIV status. This algorithm may inform expanded HIV testing strategies aiming to test the untested. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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