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

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

Visualizing implementation: contextual and organizational support mapping of stakeholders (COSMOS)

“…Domains adapted from Stange and Glasgow [ 20 ]. Table adapted from Bernstein et al [ 21 ] Domain Findings Implications for E-STOPS design and implementation Relevant theory or participant mental models Push-pull-capacity model for guideline implementation [ 22 ] Provided conceptual model for study and means of framing E-STOPS for various stakeholders National, state, local public policy HITECH act encourages adoption of EHRs; tobacco screening, treatment as early publicly reported core measure Important “push” factors that facilitated framing of intervention to hospital leadership Pertinent community norms, resources Primary care access is modest in local community; care often fragmented between hospital, outpatient providers Use of health IT/EHR designed to facilitate communication between providers Healthcare system organization, payment systems, IT, other support systems IT reports to finance; new EHR installed near planned launch of E-STOPS need to address potential return on investment for tobacco treatment, re: pay-for-performance and public reporting of core measures; compliance with CMS, Joint Commission mandates Need to address potential return on investment for tobacco treatment, re: pay-for-performance and public reporting of core measures; compliance with CMS, Joint Commission mandates Practice culture, staffing Physicians, nurses want to treat tobacco dependence; may have limited skills, knowledge, resources to do so E-STOPS designed to minimize provider workload, provide choice, but make treatment the default choice. Patient populations, subgroups Many adult smokers admitted to hospital; hospitalization as period of enforced abstinence, “teachable moment” for tobacco E-STOPS limited to inpatient units on medical services, to capitalize on Relevant historical factors, recent events Steady decline in prevalence of smoking, but undertreatment still common in healthcare settings; growth of value-based performance models Used to provide rationale for E-STOPS to physicians, nurses, administrators Culture, motivations surrounding monitoring, evaluation Physicians want to treat smokers; some concerns about added workload, role of hospital-based personnel in treating tobacco dependence; concerns about performance assessment Physicians assured that feedback was confidential, would not be shared with supervisors.…”

“…Domains adapted from Stange and Glasgow[20]. Table adapted from Bernstein et al[21] Physicians want to treat smokers; some concerns about added workload, role of hospital-based personnel in treating tobacco dependence; concerns about performance assessment Physicians assured that feedback was confidential, would not be shared with supervisors.…”

“…With a study population of 254 physicians and 1044 patients, we demonstrated statistically significant improvement in the provision of all components of the intervention. However, tobacco cessation rates, assessed via telephone report at 1, 6, and 12 months, and exhaled measurement of breath carbon monoxide at 12 months, did not differ between intervention and controls[15]. The study was approved by the institution's Human Investigations Committee, and is registered in www.clinicaltrials.gov as NCT01691105.To design the intervention, the study team spent nearly 2 years developing the COSMOS for the project, displayed inFigure 5.…”

Substance Use Disorder Detection Rates Among Providers of General Medical Inpatients

“…Prior single-site research demonstrates that prompts in the EHR lead to high rates of alcohol and drug use screening 47 ) scale ranges from 0 to 100 with higher scores indicating better health; Motivation Scale Score was the average of three items that measured importance, ability, and commitment to refrain from using substances; for nicotine models, severity was measured with Heaviness of Smoking Index on a scale ranging from 0 to 6 with higher scores indicating more severe addiction; for alcohol and drug models, severity was measured using Addiction Severity Index (ASI) composite scores, ranging from 0 (no problems) to 1 (severe problems), which were rescaled by multiplying the original scores by 10 to allow for a more meaningful interpretation of the odds ratios; substance use disorder based upon DSM-5 criteria using the Mini-International Neuropsychiatric Inventory (MINI) nicotine treatment engagement. 48 Multi-site pragmatic research is needed to examine if such prompts for substance use screening might similarly lead to improvements in overall detection and concomitant attempts to intervene with appropriate treatments.…”

Delivery of Smoking Cessation Services and Cessation Attempts Across a Public, Safety-Net Primary Care System

“…For subpopulations with high burden of comorbidities, basic cessation services may be insu cient, highlighting a need for more intensive efforts. 8,16 Such efforts may include counseling from care providers of multiple disciplines, combining referrals from different encounters or providers, and streamlined infrastructure to ensure e cient delivery of cessation resources.…”