Evidence map›Paper›PMID 42660563›Full record

ArticleBMJ open2026

Performance characteristics of an electronic medical record alert system to identify hospitalised patients with active hepatitis C virus infection: a cross-sectional analysis from two large academic centres.

Patrick Francis Pryal, Walid El-Nahal, Kelly Gebo, Thomas Grader-Beck, Grant A Wilson, Bradley Beatson, Oluwaseun Falade, Shanshan Huang, Stephen Berry

Abstract readMulticenter Study
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Patrick Francis PryalAnesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA ppryal1@jh.edu.ORCID 0009-0000-0821-8432
Walid El-NahalInova Fairfax Medical Campus, Falls Church, Virginia, USA.
Kelly GeboJohns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Thomas Grader-BeckMedicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Grant A WilsonJohns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0009-0009-6955-6190
Bradley BeatsonUniversity of Southern California Keck School of Medicine, Los Angeles, California, USA.
Oluwaseun FaladeJohns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Shanshan HuangJohns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Stephen BerryMedicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Funding

RESEARCH TRAINING IN MICROBIAL DISEASEST32AI007291 · NIAID · JOHNS HOPKINS UNIVERSITY · PI JOEL N BLANKSON, Sara Elizabeth Cosgrove · 1986 to 2026
$9.1M
AHRQ HHS R01 HS024079NIAID NIH HHS T32 AI007291
6 · The paper itself

Abstract

objectivesDirect-acting antivirals are highly effective in curing hepatitis C virus (HCV) infection but linkage to care remains a barrier to population-wide treatment. Hospitalisation for any cause may be an opportunity to identify candidates for treatment. Here, we present the test characteristics of an electronic medical record (EMR) alert system to identify hospitalised patients with HCV infection.

designWe created two alerts based on HCV antibody and RNA values and HCV-specific International Classification of Diseases codes: an ever-alert designed to detect patients with any history of HCV infection (resolved or active) and an active-alert designed to only detect active infections. We performed manual chart review of alerted patients to determine positive predictive value (PPV). We reviewed consecutively hospitalised patients to determine sensitivity.

settingTwo affiliated academic tertiary care hospitals in Baltimore, Maryland, USA. PRIMARY AND SECONDARY OUTCOME MEASURES: PPV and sensitivity of the alerts for patients with active HCV infection.

participantsHospitalised adults (aged ≥18 years).

resultsDuring 12 weeks in 2022 and 2023, the ever-alert fired for 1218 hospitalisations. The ever-alert detected 438 active infections among 1218 alerted hospitalisations (PPV 36.0% (95% CI 33.3% to 38.7%)) compared with 415 active infections among 591 alerts (PPV 70.1% (95% CI 66.4% to 73.8%)) for the active-alert. Both alerts detected 37 of 44 active cases (sensitivity 81.4% (95% CI 70.6% to 92.1%)) among 1115 consecutively hospitalised patients. DISCUSSION: The active-alert demonstrated a higher PPV than the ever-alert with a negligible decrease in case detection and similar sensitivity for active infection.

conclusionsThe active-alert may represent an efficient method of identifying candidates for HCV linkage to care using hospital EMR data. Future directions include deploying the active-alert with a formal linkage to care team.

Indexed as

Electronic Health RecordsHepatitis CHospitalizationAcademic Medical CentersAdultAgedBaltimoreCross-Sectional StudiesFemaleHepacivirusHepatitis C AntibodiesHumansMaleMiddle AgedPredictive Value of TestsRNA, ViralHepatitis C AntibodiesRNA, ViralDisease ManagementElectronic Health RecordsHospitalsPublic health

Identifiers

PMID42660563
PMCPMC13536018

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.