Evidence map›Paper›PMID 27800564›Full record

ReviewTranslational behavioral medicine2017

A content analysis of electronic health record (EHR) functionality to support tobacco treatment.

Jennifer M Schindler-Ruwisch, Lorien C Abroms, Steven L Bernstein, Christina L Heminger

Abstract readReview
In one paragraph

Review in Translational behavioral medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

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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

4 authors.

Jennifer M Schindler-RuwischDepartment of Prevention and Community Health, The George Washington University Milken Institute School of Public Health, 950 New Hampshire Avenue, 3rd Floor, Washington, DC, NW, 20052, USA.
Lorien C AbromsDepartment of Prevention and Community Health, The George Washington University Milken Institute School of Public Health, 950 New Hampshire Avenue, 3rd Floor, Washington, DC, NW, 20052, USA. lorien@gwu.edu.
Steven L BernsteinYale University School of Medicine, 464 Congress Ave., Suite 260, New Haven, CT, 06519-1315, USA.
Christina L HemingerDepartment of Prevention and Community Health, The George Washington University Milken Institute School of Public Health, 950 New Hampshire Avenue, 3rd Floor, Washington, DC, NW, 20052, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Implementation of HIT-Enhanced Tobacco Treatment for Hospitalized SmokersR18HL108788 · NHLBI · YALE UNIVERSITY · PI BERNSTEIN, STEVEN L · 2012 to 2015
$3.0M
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS R18 HL108788
6 · The paper itself

Abstract

Government regulations have created new incentives for health systems to implement changes in electronic health records (EHRs) to reduce tobacco use among patients. The purpose of this study is to conduct a content analysis of EHR modifications aimed at supporting tobacco cessation and to document these modifications using a 5 A's framework (i.e., Ask, Advise, Assess, Assist, Arrange). Fourteen trials were identified that met inclusion criteria. A content analysis of EHR functionality in these trials was conducted by two independent reviewers between February and June 2015. For "Ask," all trials provided for the documentation of smoking status in the EHR. For "Advise," 35.7 % of EHRs provided functionality related to helping a clinician provide advice to quit. For "Assess," more than half (57.1 %) of EHRs included a feature to document a patient's willingness to quit. For "Assist," EHRs offered features for medication prescribing (78.6 %), providing educational materials to patients (57.1 %), referring a patient to the quitline (50.0 %), referring a patient to a tobacco treatment specialist (42.9 %), and documenting the provision of counseling (35.7 %). Finally, for "Arrange," EHRs supported the following up of patients (35.7 %) and allowed tobacco treatment specialists to "pass back" patient notes to primary care providers (28.6 %). Studies that have modified EHRs for tobacco treatment have done so across the steps in the 5 As model, with most modifications occurring to support documenting smoking status (Ask) and assisting with medication prescribing (Assist). As health systems attempt to comply with Meaningful Use regulations, an understanding of the range of EHR modifications to support tobacco treatment is warranted.

Indexed as

Electronic Health RecordsTobacco Use CessationHumansTobacco Use DisorderBehavioral informaticsEHRElectronic health recordElectronic medical recordEMRSmoking cessation

Identifiers

PMID27800564
PMCPMC5526802

What OpenQuestion holds

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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.