Evidence map›Paper›PMID 33644496›Full record

ArticleTobacco prevention & cessation2021

Improving smoking cessation referrals among elective surgery clinics through electronic clinical decision support.

Connor Stonesifer, Salvatore Crusco, Sritha Rajupet

Abstract read
In one paragraph

Article in Tobacco prevention & cessation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
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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

3 authors.

Connor StonesiferVagelos College of Physicians and Surgeons, Columbia University, New York, United States.
Salvatore CruscoIcahn School of Medicine at Mount Sinai, New York, United States.
Sritha RajupetVagelos College of Physicians and Surgeons, Columbia University, New York, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPreoperative visits are an exceptional opportunity to encourage smoking cessation, as studies demonstrate the experience of scheduling elective surgery produces an actionable incentive to quit. However, studies suggest surgeons do not regularly assess smoking behavior or offer cessation therapies. Clinical decision support (CDS) is a system in which providers are presented with clinically integrated tools to enhance decision-making.

methodsA CDS tool was designed to facilitate treatment referrals for smoking cessation services among patients seeking elective surgery. Two clinics were selected: the plastic and vascular surgeries. The study objectives were to assess the utilization rate and effectiveness of this system.

resultsNo smoking cessation referrals had been submitted by the plastic surgery or vascular surgery clinics in the year before CDS tool implementation. Providers at the plastic surgery clinic utilized the CDS tool in 95.0% (191 of 201) eligible patient encounters. Of these patients, 16.3% were identified as active smokers, and 16.1% of these smokers accepted treatment referrals. Providers at the vascular surgery clinic utilized the CDS tool in 50.3% (98 of 195) eligible patient encounters. Of these patients, 10.2% were identified as active smokers, and 30.0% of these smokers accepted treatment referrals.

conclusionsThe CDS tool improved the incidence of smoking cessation referrals in two surgical clinics from pretest baselines and achieved satisfactory utilization rates. This report demonstrates the feasibility of CDS tools to actualize the preoperative visit as an opportunity to promote smoking cessation.

Indexed as

clinical decision supportfeasibility studysmoking-cessationsurgerytobacco-related disparitiestobacco-use cessation

Identifiers

PMID33644496
PMCPMC7896627

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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.