Evidence map›Paper›PMID 27378268›Full record

ArticleBMC medical informatics and decision making2016

Assessment and improvement of HIV screening rates in a Midwest primary care practice using an electronic clinical decision support system: a quality improvement study.

Jasmine R Marcelin, Eugene M Tan, Alberto Marcelin, Marianne Scheitel, Praveen Ramu, Ronald Hankey, Pritesh Keniya, Majken Wingo, Stacey A Rizza, Frederick North and 1 more

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
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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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

11 authors.

Jasmine R MarcelinDivision of Infectious Diseases, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. marcelin.jasmine@mayo.edu.
Eugene M TanDivision of Infectious Diseases, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Alberto MarcelinDepartment of Family Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Marianne ScheitelDepartment of Information Technology Administration, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Praveen RamuDepartment of Information Technology Administration, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Ronald HankeyDepartment of Information Technology Administration, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Pritesh KeniyaDepartment of Information Technology Administration, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Majken WingoDepartment of Internal Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Stacey A RizzaDivision of Infectious Diseases, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Frederick NorthDepartment of Internal Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Rajeev ChaudhryDepartment of Internal Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.

Funding

Mayo Clinic Center for Translational Science ActivitiesUL1TR000135 · NCATS · MAYO CLINIC ROCHESTER · PI KHOSLA, SUNDEEP · 2012 to 2015
$41.2M
NCATS NIH HHS UL1 TR000135
6 · The paper itself

Abstract

backgroundUniversal human immunodeficiency virus (HIV) screening remains low in many clinical practices despite published guidelines recommending screening for all patients between ages 13-65. Electronic clinical decision support tools have improved screening rates for many chronic diseases. We designed a quality improvement project to improve the rate of universal HIV screening of adult patients in a Midwest primary care practice using a clinical decision support tool.

methodsWe conducted this quality improvement project in Rochester, Minnesota from January 1, 2014 to December 31, 2014. Baseline primary care practice HIV screening data were acquired from January 1, 2014 to April 30, 2014. We surveyed providers and educated them about current CDC recommended screening guidelines. We then added an HIV screening alert to an existing electronic clinical decision support tool and post-intervention HIV screening rates were obtained from May 1, 2014 to December 31, 2014. The primary quality outcome being assessed was change in universal HIV screening rates.

resultsTwelve thousand five hundred ninety-six unique patients were eligible for HIV screening in 2014; 327 were screened for HIV. 6,070 and 6,526 patients were seen before and after the intervention, respectively. 1.80 % of eligible patients and 3.34 % of eligible patients were screened prior to and after the intervention, respectively (difference of -1.54 % [-2.1 %, -0.99 %], p < 0.0001); OR 1.89 (1.50, 2.38). Prior to the intervention, African Americans were more likely to have been screened for HIV (OR 3.86 (2.22, 6.71; p < 0.001) than Whites, but this effect decreased significantly after the intervention (OR 1.90 (1.12, 3.21; p = 0.03).

conclusionsThese data showed that an electronic alert almost doubled the rates of universal HIV screening by primary care providers in a Midwestern practice and reduced racial disparities, but there is still substantial room for improvement in universal screening practices. Opportunities for universal HIV screening remain abundant, as many providers either do not understand the importance of screening average risk patients or do not remember to discuss it. Alerts to remind providers of current guidelines and help identify screening opportunities can be helpful.

Indexed as

Clinical Decision-MakingDecision Support TechniquesAdolescentAdultFemaleHIV InfectionsHumansMaleMiddle AgedMinnesotaPrimary Health CareQuality ImprovementYoung AdultClinical decision support systemsHIVQuality improvementUniversal screening

Identifiers

PMID27378268
PMCPMC4932674

What OpenQuestion holds

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LicenceCC BY
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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.