Trial reportImplementation science : IS2019
A randomized trial of decision support for tobacco dependence treatment in an inpatient electronic medical record: clinical results.
Trial report in Implementation science : IS, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01691105 (Implementation of HIT-Enhanced Tobacco Treatment for Hospitalized Smokers), which is not on this map. Cited by 10 papers.
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.
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.
Implementation of HIT-Enhanced Tobacco Treatment for Hospitalized Smokers
Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Pilot Trial of a Behavioral Economics-Informed Clinical Decision Support Alert to Improve Inpatient Tobacco Use Treatment Rates.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025Trial
- Clinical Decision Support for Newborn Weight Loss: A Randomized Controlled Trial.Hospital pediatrics · 2022Trial
- Effectiveness of a Multicomponent Strategy for Implementing Guidelines for Treating Tobacco Use in Vietnam Commune Health Centers.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2022Trial
- Substance Use Disorder Detection Rates Among Providers of General Medical Inpatients.Journal of general internal medicine · 2021Trial
- [Tobacco cessation: one of the most effective medical measures].Innere Medizin (Heidelberg, Germany) · 2024Article
- Improving Tobacco Cessation Rates Using Inline Clinical Decision Support.Applied clinical informatics · 2022Article
- Increased Reach and Effectiveness With a Low-Burden Point-of-Care Tobacco Treatment Program in Cancer Clinics.Journal of the National Comprehensive Cancer Network : JNCCN · 2022Article
- Design and Pilot Implementation of an Electronic Health Record-Based System to Automatically Refer Cancer Patients to Tobacco Use Treatment.International journal of environmental research and public health · 2020Article
- Visualizing implementation: contextual and organizational support mapping of stakeholders (COSMOS).Implementation science communications · 2020Article
- Clinical Decision Support Tool for Parental Tobacco Treatment in Hospitalized Children.Applied clinical informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundSmokers usually abstain from tobacco while hospitalized but relapse after discharge. Inpatient interventions may encourage sustained quitting. We previously demonstrated that a decision support tool embedded in an electronic health record (EHR) improved physicians' treatment of hospitalized smokers. This report describes the effect on quit rates of this decision support tool and order set for hospitalized smokers.
methodsIn a single hospital system, 254 physicians were randomized 1:1 to receive a decision support tool and order set, embedded in the EHR. When an adult patient was admitted to a medical service, an electronic alert appeared if current smoking was recorded in the EHR. For physicians receiving the intervention, the alert linked to an order set for tobacco treatment medications and electronic referral to the state tobacco quitline. Additionally, "Tobacco Use Disorder" was added to the patient's problem list, and a secure message was sent to the patient's primary care provider (PCP). In the control arm, no alert appeared. Patients were contacted by phone at 1, 6, and 12 months; those reporting tobacco abstinence at 12 months were asked to return to measure exhaled carbon monoxide. Generalized estimating equations were used to model the data.
resultsFrom 2013 to 2016, the alert fired for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication, populate the problem list with tobacco use disorder, refer to the quitline, and notify the patient's PCP. In a subset of 1044 patients recruited for intensive follow-up, one-year quit rates for intervention and control patients were, respectively, 11.5% and 11.6%, (p = 0.94), after controlling for age, sex, race, ethnicity, and insurance. Similarly, there were no differences in 1- and 6-month quit rates.
conclusionsAlthough we were able to improve processes of care, long-term tobacco quit rates were unchanged. This likely reflects, in part, the need for sustained quitting interventions, and higher-than-expected quit rates in controls. Future enhancements should improve prescription of medications for smoking cessation at discharge, engagement of primary care providers, and perhaps direct engagement of patients in a more longitudinal approach.
trial registrationClinicalTrials.gov, NCT01691105 . Registered on September 12, 2012.
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Registered trials
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.