Evidence map›Paper›PMID 35705318›Full record

ArticleBMJ health & care informatics2022

Designing an electronic medical record alert to identify hospitalised patients with HIV: successes and challenges.

Walid El-Nahal, Thomas Grader-Beck, Kelly Gebo, Elizabeth Holmes, Kayla Herne, Richard Moore, David Thompson, Stephen Berry

Open access · goldAbstract read
In one paragraph

Article in BMJ health & care informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact, top 92% of its field
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 1 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Walid El-NahalDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA welnaha1@jh.edu.ORCID http://orcid.org/0000-0001-6834-0405
Thomas Grader-BeckDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Kelly GeboDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Elizabeth HolmesDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Kayla HerneDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Richard MooreDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
David ThompsonDepartment of Anesthesiology and Critical Care Medicine, John Hopkins University School of Medicine, Baltimore, Maryland, USA.
Stephen BerryDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Johns Hopkins University · US

Funding

RESEARCH TRAINING IN MICROBIAL DISEASEST32AI007291 · NIAID · JOHNS HOPKINS UNIVERSITY · PI JOEL N BLANKSON, Sara Elizabeth Cosgrove · 1986 to 2026
$9.1M
Hospital HIV/HCV Support Team to Improve Medication Safety and Engagement in CareR01HS024079 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI BERRY, STEPHEN ARTHUR · 2015 to 2019
$1.2M
AHRQ HHS R01 HS024079NIAID NIH HHS T32 AI007291
6 · The paper itself

Abstract

objectivesElectronic medical record (EMR) tools can identify specific populations among hospitalised patients, allowing targeted interventions to improve care quality and safety. We created an EMR alert using readily available data elements to identify hospitalised people with HIV (PWH) to facilitate a quality improvement study intended to address two quality/safety concerns (connecting hospitalised PWH to outpatient HIV care and reducing medication errors). Here, we describe the design and implementation of the alert and analyse its accuracy of identifying PWH.

methodsThe EMR alert was designed to trigger for at least one of four criteria: (1) an HIV ICD-10-CM code in a problem list, (2) HIV antiretroviral medication(s) on medication lists, (3) an HIV-1 RNA assay ordered or (4) a positive HIV-antibody result. We used manual chart reviews and an EMR database search to determine the sensitivity and positive predictive value (PPV) of the overall alert and its individual criteria.

resultsOver a 24-month period, the alert functioned as intended, notifying an intervention team and a data abstraction team about admissions of PWH. Manual review of 1634 hospitalisations identified 18 PWH hospitalisations, all captured by the alert (sensitivity 100%, 95% CI 82.4% to 100.0%). Over the 24 months, the alert triggered for 1191 hospitalisations. Of these, 1004 were PWH hospitalisations, PPV=84.3% (95% CI 82.2% to 86.4%). Using fewer criteria (eg, using only ICD-10-CM codes) identified fewer PWH but increased PPV.

conclusionAn EMR alert effectively identified hospitalised PWH for a quality improvement intervention. Similar alerts might be adapted as tools to facilitate interventions for other chronic diseases.

Indexed as

Electronic Health RecordsHIV InfectionsDatabases, FactualHospitalizationHumansMedication ErrorsCommon Data ElementsElectronic Health RecordsInfectious Diseases MedicinePatientIdentification Systems

Identifiers

PMID35705318
PMCPMC9204398
OpenAlexW4283029209

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.