TY - JOUR
T1 - Development of an electronic medical record-based algorithm to identify patients with unknown HIV status
AU - Felsen, Uriel R.
AU - Bellin, Eran Y.
AU - Cunningham, Chinazo O.
AU - Zingman, Barry S.
N1 - Funding Information:
This study was supported in part by the Center for AIDS Research at the Albert Einstein College of Medicine and Montefiore Medical Center [grant number NIH AI-51519], the National Instititues of Health [grant numbers NIH R25DA023021, NIH R01DA032110, NIH R34DA031066], the NCRR, a component of the NIH [gant numbers UL1RR025750, KL2RR025749, and TL1RR025748], and the HIV Prevention Trial Network study 065.
PY - 2014/10/3
Y1 - 2014/10/3
N2 - 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.
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.
KW - HIV/AIDS
KW - algorithm
KW - electronic medical record
KW - expanded HIV testing
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U2 - 10.1080/09540121.2014.911813
DO - 10.1080/09540121.2014.911813
M3 - Article
C2 - 24779521
AN - SCOPUS:84904063645
SN - 0954-0121
VL - 26
SP - 1318
EP - 1325
JO - AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV
JF - AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV
IS - 10
ER -
