A randomized trial of decision support for tobacco dependence treatment in an inpatient electronic medical record: clinical results.


Journal

Implementation science : IS
ISSN: 1748-5908
Titre abrégé: Implement Sci
Pays: England
ID NLM: 101258411

Informations de publication

Date de publication:
22 01 2019
Historique:
received: 20 08 2018
accepted: 11 01 2019
entrez: 24 1 2019
pubmed: 24 1 2019
medline: 14 8 2019
Statut: epublish

Résumé

Smokers 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. In 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. From 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. Although 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. ClinicalTrials.gov, NCT01691105 . Registered on September 12, 2012.

Sections du résumé

BACKGROUND
Smokers 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.
METHODS
In 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.
RESULTS
From 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.
CONCLUSIONS
Although 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 REGISTRATION
ClinicalTrials.gov, NCT01691105 . Registered on September 12, 2012.

Identifiants

pubmed: 30670043
doi: 10.1186/s13012-019-0856-8
pii: 10.1186/s13012-019-0856-8
pmc: PMC6343239
doi:

Banques de données

ClinicalTrials.gov
['NCT01691105']

Types de publication

Journal Article Randomized Controlled Trial Research Support, N.I.H., Extramural

Langues

eng

Pagination

8

Subventions

Organisme : NHLBI NIH HHS
ID : R18 HL108788
Pays : United States

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Auteurs

Steven L Bernstein (SL)

Department of Emergency Medicine, Yale School of Medicine, 464 Congress Ave., Suite 260, New Haven, CT, 06519, USA. steven.bernstein@yale.edu.
Yale Cancer Center, New Haven, USA. steven.bernstein@yale.edu.
Yale Center for Implementation Science, New Haven, CT, USA. steven.bernstein@yale.edu.

June Weiss (J)

Department of Emergency Medicine, Yale School of Medicine, 464 Congress Ave., Suite 260, New Haven, CT, 06519, USA.

Michelle DeWitt (M)

Yale-New Haven Hospital, New Haven, CT, USA.

Jeanette M Tetrault (JM)

Department of Medicine, Yale School of Medicine, New Haven, USA.

Allen L Hsiao (AL)

Department of Emergency Medicine, Yale School of Medicine, 464 Congress Ave., Suite 260, New Haven, CT, 06519, USA.
Department of Pediatrics, Yale School of Medicine, New Haven, USA.
Yale-New Haven Hospital, New Haven, CT, USA.

James Dziura (J)

Department of Emergency Medicine, Yale School of Medicine, 464 Congress Ave., Suite 260, New Haven, CT, 06519, USA.

Scott Sussman (S)

Department of Medicine, Yale School of Medicine, New Haven, USA.
Yale-New Haven Hospital, New Haven, CT, USA.

Ted Miller (T)

Pacific Institute for Research and Evaluation, Calverton, MD, USA.

Kelly Carpenter (K)

Optum Center for Wellbeing Research (formerly Alere Wellbeing), Seattle, WA, USA.

Patrick O'Connor (P)

Department of Medicine, Yale School of Medicine, New Haven, USA.

Benjamin Toll (B)

Department of Psychiatry, Yale School of Medicine, New Haven, USA.
Yale Cancer Center, New Haven, USA.
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

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Classifications MeSH