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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedZubiago J, Murphy M, Guardado R
Increased HIV testing in people who use drugs hospitalized in the first wave of the COVID-19 pandemic.
During the COVID-19 pandemic, due to lapses in harm reduction services, several public health experts forecasted subsequent increases in diagnosis of HIV in PWUD. As many inpatient hospitals reworked patient flow during the COVID-19 surge, the investigators hypothesized that HIV testing in PWUD would decrease. To answer this question, they compiled a deidentified list of hospitalized patients with electronic medical record indicators of substance use-a positive urine toxicology screen, prescribed medications to treat opioid use disorder, a positive CIWA score, or a positive CAGE score-admitted between January, 2020 and August, 2020.
AHRQ-funded; HS026008.
Citation: Zubiago J, Murphy M, Guardado R .
Increased HIV testing in people who use drugs hospitalized in the first wave of the COVID-19 pandemic.
J Subst Abuse Treat 2021 May;124:108266. doi: 10.1016/j.jsat.2020.108266..
Keywords: Human Immunodeficiency Virus (HIV), Opioids, Substance Abuse, Alcohol Use, Hospitalization, COVID-19, Public Health, Screening
Afshar M, Sharma B, Bhalla S
External validation of an opioid misuse machine learning classifier in hospitalized adult patients.
This study looks at new methods to make opioid misuse screening in hospitals less resource-intensive, which causes it to occur rarely. The objective of this study is to externally validate the author’s previously published and open-source machine learning classifier by implementing it a different hospital to identify cases of opioid misuse. An observational cohort of 56,227 adult hospitalizations from October 2017 to December 2019 was used during a hospital-wide substance use screening program with manual screening. A manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classified with coded word embedding features to capture electronic health record (EHR) clinical notes. Manual screening was completed in 67.8% of patients with 1.1% identified with opioid misuse. The opioid misuse classifier had good discrimination during external validation and may help overcome manual screening barriers.
AHRQ-funded; HS026385.
Citation: Afshar M, Sharma B, Bhalla S .
External validation of an opioid misuse machine learning classifier in hospitalized adult patients.
Addict Sci Clin Pract 2021 Mar 17;16(1):19. doi: 10.1186/s13722-021-00229-7..
Keywords: Opioids, Medication, Substance Abuse, Screening, Hospitalization