Abstract
OBJECTIVE:
Smoking increases cardiopulmonary and rheumatic disease risk, yet tobacco cessation intervention is rare in rheumatology clinics. This study aimed to implement a rheumatology staff-driven protocol, Quit Connect, to increase the rate of electronic referrals (e-referrals) to free, state-run tobacco quit lines (TQL).
METHODS:
We conducted a quasi-experimental cohort study of Quit Connect at three rheumatology clinics comparing TQL referrals from four baseline years to referrals during a six-month intervention period. Nurses and medical assistants were trained to use two standardized electronic health record (EHR) prompts to Check readiness to quit smoking within 30 days, Advise cessation, and Connect patients using TQL e-referral orders. We used EHR data to examine the primary outcome of TQL referrals using pre-post design.
RESULTS:
Across 54,090 pre- and post-protocol rheumatology clinic visits, 4,601 were with current smokers. We compared outcomes between 4,078 eligible pre-implementation visits and 523 intervention period visits. Post-implementation, the odds of TQL referral were 26-fold higher compared to our pre-implementation rate (unadjusted OR 26, CI 6–106). Adjusted odds of checking readiness to quit in the next 30 days increased over 100-fold compared to pre-implementation (adjusted OR 132, CI 99–177). Intervention led to e-referrals for 71% of quit-ready patients in <90 seconds; 24% of referred patients reported a quit attempt.
CONCLUSION:
Implementing Quit Connect in rheumatology clinics was feasible and improved referrals to a state-run TQL. Given the importance of smoking cessation to reduce cardiopulmonary and rheumatic disease risk, future studies should investigate disseminating cessation protocols like Quit Connect that leverage TQLs.
INTRODUCTION
In many rheumatologic conditions, smoking predicts higher disease incidence, greater disease severity, and reduced treatment response in addition to increased cardiovascular risk (1–4). A US Surgeon General report on smoking highlighted links between smoking and rheumatic diseases, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) (5). 2020 updates review how smoking cessation reduces adverse outcomes (6). US prevention guidelines recommend intervening with all smokers at all visits, and cessation intervention has been an American College of Rheumatology (ACR) quality measure (7, 8). Yet, while smoking is routinely assessed, cessation interventions are rare in rheumatology (7, 8). Despite 50–60% higher cardiovascular disease (CVD) risk (9, 10), we reported that cessation advice was provided in just 10% of RA visits; quit line support was recommended in 0.6% of visits (11).
Rheumatology clinic visits equal or outnumber primary care visits for half of patients (12), providing a critical opportunity to address smoking and improve CVD and rheumatologic outcomes. However, rheumatologists may be less likely to address smoking than primary care providers (13). Although primary care providers are prevention experts, in one study, only 32% were aware of RA-mediated CVD risk (14). RA patients are also often unaware of causal relationships between RA severity and smoking (15, 16). Patients report that counseling about smoking’s specific effects on rheumatic disease or reduced effectiveness of rheumatology medication are key motivators to quit (15, 16), reinforcing the potential for smoking cessation interventions in rheumatology clinics.
The Chronic Care Model provides a foundation for improving smoking cessation intervention in rheumatology clinics by adapting workflows to address tobacco use (17). One system-level strategy to address smoking is utilizing tobacco cessation quit lines, a free resource available in all 50 states, to provide access to counseling and cessation pharmacotherapy. Patients using a state tobacco quit line are four times more likely to quit compared to unassisted attempts (18, 19). Likewise, compared to passive quit line referrals (e.g., brochures, offering quit line phone numbers to patients), active referrals via orders to a quit line that calls patients increased quit line use by more than 13-fold in a primary care Ask, Advise, Connect study (20).
Developing a system for referrals would enhance the likelihood that rheumatology clinics would meet key quality guidelines and provide an evidence-based cessation intervention for rheumatology patients who smoke. Staff protocols standardize how nurses and medical assistants address CVD risk factors to improve outcomes in primary care (21, 22). We previously reported positive findings of BP Connect, a staff blood pressure protocol adapted for rheumatology clinics (23). We subsequently implemented Quit Connect, our electronic health record (EHR) e-referral protocol for rheumatology clinic staff to offer quit line referrals for rheumatology patients who smoke. The study objectives were to implement Quit Connect to support a Check, Advise, Connect protocol in rheumatology clinics using EHR prompts and to evaluate impact. We used EHR data to study staff process measures (e.g., readiness to quit assessments and referral offers) and implementation fidelity shown by the primary outcome of accepted referrals, and quit line outcomes.
METHODS
Inclusion Criteria and Setting
This quasi-experimental pre-post study occurred in three academic adult rheumatology clinics in one of the 12 largest US multispecialty groups. Patients ≥18 years old with a rheumatology visit and a positive tobacco screen were eligible. Exclusion criteria included patients <18 years old and residing out of state (not eligible for in-state quit line calls). We compared a baseline period (October 2012-March 2016) to a six-month intervention period (April 2016-October 2016). The extended baseline period was chosen to reduce the impact of temporal trends and other clinic-specific CVD prevention projects on the baseline estimates. We additionally described rheumatology visits with condition indicators pre- and post-implementation using International Classification of Disease (ICD) codes for rheumatoid arthritis, spondyloarthritis, connective tissue disease, or other. This was approved by the Health Sciences IRB with exemption as a quasi-experimental improvement evaluation with permission to publish.
Protocol Intervention
Based on the Chronic Care Model (17) and our specialty staff-delivery strategy, our research team developed a specialty clinic smoking cessation intervention and implementation plan with rheumatology nurses and medical assistants (24–26). We adapted Ask, Advise, Connect from primary care (20) for rheumatology. We used two standardized EHR prompts for staff to: 1) Check tobacco use and 30 day readiness to quit and Advise cessation, and 2) Connect willing patients to the TQL with an electronic referral order allowing the quit line to contact the patient directly. Specialty clinic staff advised us on how to tailor Quit Connect protocol workflows and implementation plans for rheumatology clinics in one-hour baseline semi-structured group interviews led by a nurse facilitator and a health systems engineer.
Methods and findings are reported according to the Template for Intervention Description and Replication (TIDieR) (27) and STROBE checklists (28).
Implementation
Staff Engagement Group Interviews
Prior to implementation, two trained facilitators conducted three one-hour semi-structured group interviews (29), each with approximately six staff participants to gather feedback and refine the tobacco cessation protocol for use in rheumatology clinics. We engaged medical assistants (MA) and nurse participants to redesign EHR tools and clinic rooming workflows (staff rooming activities include entering exam room and data collection before the provider enters) (30). Medical assistant and nurse Staff Champions (31) from each clinic helped with interview recruitment. These group interviews were designed to identify potential obstacles and strategies to mitigate them. Two staff group interviews were also conducted mid-implementation to assess feasibility, acceptability, and fidelity to inform refinements.
Staff Education and Training
Pre-implementation, medical assistants and nurses first received educational rationale overview and workflow steps in a 15-minute introductory presentation. Then, as part of a one-hour training, they received a 30-minute didactic session led by a quit line smoking cessation educator, followed by rehearsing talking points of the Check, Advise, Connect protocol using mock scenarios from group feedback. Participants were given printed materials with example dialogue from behavioral health and smoking cessation experts offering suggested responses and a patient brochure with additional prompts. Lastly, on the first day of implementation, each MA and nurse received 15 minutes of one-on-one practice with a study team member to rehearse the EHR workflow. A two-page laminate with EHR navigation reminders was placed in each clinic room. Posters suggesting language for challenging patients were posted in break rooms.
EHR Reminders
Quit Connect required IT teams to construct two decision support alerts, an order set, and an HL7 software interface to send and receive orders and results from quit line. Staff used the two decision support alerts during clinic rooming. The first alert prompted Checking smoking and 30-day readiness to quit. Notably, the field to document 30-day readiness to quit already existed in the EHR (Epic Systems Corp, Verona, WI) but was rarely used. Brief talking points were provided to staff in decision support alerts (e.g., “Are you interested in trying to cut back or quit in the next 30 days?”) and to Advise patients to quit by outlining links between smoking and rheumatic disease. When patients expressed readiness, a second alert directed staff to offer to Connect them with quit line resources, including free counseling and nicotine replacement within one week. When patients agreed to try quit line, staff clicked an order and a cessation referral automatically routed to quit line via HL7 interface. This bidirectional interface enabled secure, direct communication between the quit line and EHR. The order set included a verbal referral agreement. Patients referred to quit line received information on smoking cessation, a list of resources printed post-visit, and diagnosis code (“tobacco use” Z72.0). A desktop brochure was provided by staff to all referred patients as a prompt to discuss quit line offerings and harms of smoking on rheumatic disease. Quit line services included: counseling, two weeks of free nicotine replacement therapy (mailed by Wisconsin Quit Line), and setting a quit date.
Referral acceptance or refusal was recorded in the EHR order set. Referred patients could or could not answer, or decline or accept offered services. Results of the quit line electronic referral call routed back to the EHR via the HL7 outside results interface by the quit line vendor (Optum, Eden Prairie, MN). The alert did not fire for patients referred within 90 days.
Staff audit feedback
Per a Cochrane review on audit feedback, each staff member received four monthly individual in-person audit feedback for six-months, including both clinic and individual EHR-performance data (32). Face-to-face sessions with the study rheumatologist averaged seven minutes. Audit feedback data reported the proportion of visits Checked and Advised, as well as the proportion of eligible ready–to-quit patients offered and Connected via referral to quit line. Sessions were also used to troubleshoot technical issues and gather information on obstacles limiting performance. In these sessions, staff also completed action plans to overcome barriers and set individual goals.
Data Sources
Using EHR data, we analyzed patient- and visit-level process measures and outcomes of Quit Connect using the pre-post design. Rates of pre-implementation visits with quit line referral offers were gathered from abstracted EHR clinical note data, per our prior publication, without discrete EHR data pre-implementation (11). Process measures for Checking readiness to quit, Advising, and Connecting to quit line were compared over time using standard EHR documentation fields. Outcomes included documented e-referrals and quit line result reports. EHR data documented accepting or refusing referral. Results of quit line referrals were later routed back to the EHR via the HL7 outside results interface by the quit line vendor (Optum, Eden Prairie, MN). Referred patients could accept or decline quit line calls within five attempts, and could accept or decline one or more offered services. Services included counseling, two weeks of free nicotine replacement therapy (mailed by the Wisconsin Tobacco Quit Line service), and setting a quit date.
Additional data sources included group transcripts, staff questionnaires, and time records to assess feasibility and acceptability.
Outcomes
Process measures reported rates of visits Checked, Advised, and among smokers ready to quit, rates of offered Connection referral to quit line. Our primary outcome was accepted referral to the quit line among rheumatology patients identified as current smokers. Among referred patients we assessed the percentage of patients referred to the quit line who accepted counseling, nicotine replacement, or both; who set a quit date; and who agreed to be contacted but were unreachable. Likewise, quit attempts or reported smoking cessation within six months of the study period were assessed. Documenting quit attempts and successful cessation required a subsequent clinic visit within 6 months, a typical time period for most return visits to rheumatology clinics.
Covariates included patient sociodemographics, comorbidities, and healthcare utilization from EHR data. The John Hopkins Adjusted Clinical Groups (ACG) System was used to measure comorbidities. This system accurately accounts for the burden of morbidity relative to a mean score of one (33). Multivariable models controlled for age, sex, race, ethnicity, primary language, baseline healthcare utilization, and ACG score. All models were clustered by patient to account for multiple visits from the same patient.
Analysis
First, descriptive statistics (e.g., percentage, Chi square, ANOVA) were used to compare the pre- and post-implementation visit cohorts. Then, we performed multivariable logistic regression and compared the odds ratios and 95% confidence intervals (OR, 95% CI) of assessing tobacco use and readiness to quit before and during intervention, controlling for baseline sociodemographics, comorbidities, and utilization. We compared quit line referral events to our published rates from historic abstraction data from the same clinics (11), and calculated unadjusted odds ratios and 95% CI. Analyses were performed using SAS version 9.4 (Cary, NC).
RESULTS
A total of 54,090 pre- and post-implementation rheumatology specialty clinic visits were identified, of which 4,601 visits were with current smokers as shown in Table 1. Beyond less than a one year difference in mean age, there were no significant differences in demographics or conditions between the eligible pre- and post-implementation populations (n=4,078 pre-, n=523 post-implementation). Post-implementation visits had a mean age of 51.0 ±13.2 years, 68.1% were female, 85.3% were White, and 33.3% had RA.
Table 1.
Description of visit-level patient characteristics pre- and post-protocol implementation
| Tobacco Use n=4601 visits |
||||
|---|---|---|---|---|
| Pre-protocol visits n=4078/47,067 n (%) |
Post-protocol visits n=523/7023 n (%) |
p | ||
| Age (mean, SD) | 50.4 (± 12.9) | 51.0 (± 13.2) | <0.01 | |
| Gender | Female | 2681 (65.7) | 356 (68.1) | 0.3 |
| Race | White | 3499 (86.3) | 442 (85.3) | 0.8 |
| Black | 332 (8.2) | 44 (8.5) | ||
| Other | 225 (5.6) | 32 (6.2) | ||
| Language | English | 4025 (98.7) | 517 (98.9) | 0.8 |
| Non-English | 53 (1.3) | 6 (1.2) | ||
| Marital Status | Married/Partnered | 1858 (45.9) | 224 (43.4) | 0.5 |
| Single | 1454 (35.9) | 197 (38.2) | ||
| Separated/divorced | 735 (18.2) | 95 (18.4) | ||
| Medicaid (Ever) | 1231 (30.2) | 150 (28.7) | 0.5 | |
| BMI Quartile | Lowest (mean, SD) | 29.2 (± 7.3) | 29.1 (± 7.4) | 0.3 |
| Underweight-Normal | 1214 (30.5) | 146 (28.5) | 0.5 | |
| Overweight | 1575 (39.6) | 201 (39.3) | ||
| Obese | 1189 (29.9) | 165 (32.2) | ||
| Rheumatoid Arthritis | 1273 (31.2) | 174 (33.3) | 0.3 | |
| Spondyloarthropathy | 568 (13.9) | 81 (15.5) | 0.3 | |
| Connective Tissue Disease | 632 (15.5) | 83 (15.9) | 0.8 | |
| Other (not RA, SpA, CTDz) | 1772 (43.5) | 211 (40.3) | 0.2 | |
| Baseline Hypertension* | 1804 (44.2) | 220 (42.1) | 0.4 | |
| Cardiovascular disease | 714 (17.5) | 89 (17.0) | 0.8 | |
| Diabetes mellitus | 472 (11.6) | 62 (11.9) | 0.9 | |
| Chronic kidney/ESRD | 92 (2.3) | 12 (2.3) | 1.0 | |
| ACG Comorbidity Score (mean, SD) | 1.0 (± 0.7) | 1.0 (± 0.6) | ||
| Mean Annual Ambulatory visits (mean, SD) | 6.8 (± 6.0) | 6.30 (± 5.8) | ||
| Mean Annual PC visits (mean, SD) | 2.2 (± 3.1) | 2.1 (± 3.2) | ||
| Mean Annual Rheum visits (mean, SD) | 2.0 (± 2.0) | 1.8 (± 1.8) | 1.0 | |
| In Network PC | 2183 (53.5) | 279 (53.4) | 0.9 | |
Prior hypertension diagnosis per algorithm and/or antihypertensive medication.
Primary care follow-up analysis required an in-network PC. Abbreviations: ACG= Johns Hopkins Adjusted Clinical Groups System; BMI=body mass index; BP=blood pressure; CTDz=connective tissue disease; PC=primary care; RA=rheumatoid arthritis; SpA=spondyloarthropathy.
Process Measures: Check and Advise
Examining our process measures revealed that tobacco use assessment was high pre- and post-implementation (96.3% and 96.8%, Table 2). This metric did not significantly change in multivariable analysis (multivariable OR 1.15, CI 0.997–1.32, Table 3). However, assessing readiness to quit increased post-implementation with 80% (n=421/523) of eligible patients Checked for readiness to quit in the next 30 days, compared to 3% of pre-implementation visits (Table 2, p<0.0001). Multivariable odds of asking about readiness to quit increased over 100-fold compared to pre-implementation (multivariable OR 132, CI 99–177, Table 3). Among those asked, 29% (n=122/421) of patients reported being ready.
Table 2.
Visit level steps observed pre- and post-protocol among visits with tobacco users
| Pre-Protocol n= 47067 n (%) |
Post-Protocol n=7023 n (%) |
p | |
|---|---|---|---|
| Tobacco Status Assessed | 45326 (96.3%) | 6797 (96.8%) | 0.045 |
| Tobacco User Visits | 4078 (8.7%) | 523 (7.4%) | <0.001 |
| Readiness to Quit Assessed | 135 (3.3%) | 421 (80.5%) | <0.001 |
| Ready to Quit | 59 (43.7%) | 122 (29.0%) | 0.002 |
| Quit line E-Referral Offered | 1/175 (0.6%)* | 93 (76.2%) | <0.001 |
| E-referral Accepted | 66 (71.0%) | ||
| E-referral Declined | 27 (29.0%) |
Our published abstracted rate for any quit line offers among anyone who smoked before e-referral protocol implementation.
Table 3.
Visit level odds of tobacco intervention steps among visits with tobacco users
| Unadjusted OR (95% CI) |
Adjusted OR (95% CI) |
|
|---|---|---|
| Pre-protocol Tobacco Use Assessed | Ref | Ref |
| Post-protocol Tobacco Use Assessed | 1.16 (1.003, 1.33) | 1.15 (0.997, 1.32) |
| Pre-protocol Ready to Quit Assessed | Ref | Ref |
| Post-protocol Ready to Quit Assessed | 120.55 (91.5, 159) | 132.37 (99.1, 177) |
| Post-protocol Quit Line E-referral Offered* | 26 (6, 106) | NA |
Adjusted for age, sex, race, Adjusted Clinical Groups (ACG) comorbidity.
Compared to our published baseline (Vreede 2017).
Primary Outcome: Connection to Quit Line
The primary outcome analysis compared 4,078 eligible pre-implementation visits with 523 post-implementation visits among rheumatology patients who were current smokers. Pre-implementation, 0.6% visits with patients who smoke included a quit line referral offer, as we previously published (11). After implementation, 17.8% (93/523) of visits with smokers and 76.2% (n=93/122 ready to quit) of eligible patients were offered quit line referral (Table 2). Quit line referral post-implementation was 26-fold higher compared to our published pre-implementation rate (unadjusted OR 26, CI 6–106, Table 3) (11). Among those offered, 71.0% (n=66/93) of patients accepted quit line referrals (Table 2). Overall, 54% (n=66/122) of those ready to quit accepted referrals (Table 2).
Secondary Outcomes: Quit Attempts
Among the 66 patients referred to the quit line, 24% (n=16/66) set a quit date or reported a quit in progress. This included 17% (n=11) who accepted counseling services and nicotine replacement and five more who reported a quit in process at the time of quit line contact (Table 4). Half of referred patients (33 of 66) were unreachable; of those reached, 95% accepted services (Table 4).
Table 4.
Quit Line Outcome Following Quit Connect E-Referral
| n | % Among All Smokers |
% Among Ready to Quit Offered & Referred |
|
|---|---|---|---|
| Offered | 93 | (93/523) 17.8% | (93/122) 76.2% |
| Accepted e-referral | 66 | (66/523) 12.6% | (66/93) 71.0% |
| Set a quit date or quit in progress | 16 | (16/523) 3.1% | (16/66) 24.2% |
| Accepted counseling | 11 | (11/523) 2.1% | (11/66) 16.7% |
| Accepted nicotine replacement | 11 | (11/523) 2.1% | (11/66) 16.7% |
| Already attempting cessation | 5 | (5/523) 1.0% | (5/66) 7.6% |
| Reached but declined services | 3 | (3/523) 0.6% | (3/66) 4.5% |
| Unable to reach | 33 | (33/523) 6.3% | (33/66) 50.0% |
Staff Outcomes
Staff group interviews identified potential obstacles such as the need for language to avoid upsetting patients by asking about tobacco use (16). Staff also helped create talking points that were then built into the EHR decision support tools and desktop brochures.
Time studies and staff questionnaires suggested strong feasibility of our Quit Connect protocol. Time studies of clinic rooming using our previously published approach (34) estimated that on average the Check, Advise, Connect steps took 90 seconds to complete for the approximately 10% of rheumatology patients who smoked.
Staff fidelity to the new protocols was high with 76% of quit-ready patients being offered cessation e-referral, and most of those ready to quit accepting the referral. Additionally, the proportion of rheumatology clinic staff who reported being very or extremely confident in their ability to counsel patients on smoking cessation increased from 10% pre- to 90% post-implementation. After protocol implementation, 100% of staff reported that their clinics were “quite a bit” or a “great deal” willing to try new things to help patients, compared to 70% pre-implementation. On a scale of 1 (not at all) to 5 (extremely), staff rated the ease of protocol use during a busy clinic day at 4.2 ±0.63, showing strong feasibility. Evaluative focus group comments reflected high acceptability and one staff member summarized, “The tobacco protocol has been very user friendly and easy to incorporate into the check-in process.” No major modifications were required beyond reporting both n and percent for monthly audit feedback due to low frequencies of smoking encounters.
DISCUSSION
Quit Connect was designed to examine the effect of rheumatology staff-driven tobacco cessation intervention using EHR referrals to a quit line. Overall, quit line referrals increased 26-fold. Findings were comparable to an Ask, Advise, Connect study in primary care with 13-fold greater connection to a quit line compared to passive referrals (20). Our Quit Connect protocol took staff, on average, 90 seconds, and 24% of referred patients set a quit date, suggesting efficient use of resources. Free tobacco cessation medications and counseling are provided by the quit line, and literature shows that patients are then four times more likely to quit (18). Our findings suggest Quit Connect is an efficient, effective model for chronic care delivery to systematically address smoking at clinic visits.
Acknowledging limits on provider time, our protocol expanded upon previous literature (20) by shifting demand from physicians to clinic staff through protocol-defined steps. We used an implementation plan with rheumatology-targeted education for staff, EHR reminders, staff-engaged workflow redesign including talking points, and monthly in-person audit feedback. All of these implementation strategies have evidence to support their merit and to ensure replicability (35). Details of the Quit Connect intervention and implementation package, including the audit feedback tools, are available at our toolkit website (https://www.hipxchange.org/QuitConnect). In other studies, provider-targeted point-of-care reminders failed to significantly change provider behavior, highlighting the limitations of awareness and education alone to modify clinical practice (36). Likewise, changing the near zero use of existing readiness to quit EHR features through hands-on staff protocol training shows the critical importance of formal implementation strategies to alter workflows beyond simply providing tools (35, 37).
Feasibility and acceptability were highly rated by clinic staff. Quit Connect builds on previous literature by demonstrating the importance of engaging specialty team-based care to improve care delivery (38, 39). Staff also reported an 80% increase in self-efficacy to address smoking with patients (data not shown), demonstrating that Quit Connect and its implementation helped build staff professional capacity.
In our follow-up study assessing patient perspectives on Quit Connect, most rheumatology patients also endorsed the Quit Connect approach (16). Patients identified that knowing more about the effects of smoking specifically on rheumatic disease and therapy would be key motivators to quit, and they requested more help on how to quit. The increase in quit attempts we observed emphasizes the importance of educating patients on the specific health risks of smoking in relationship to rheumatic disease. Patients specifically requested information on how to quit, including the availability of free medication and counseling or connecting them to evidence-based resources. Previous literature has shown that passive referrals to quit lines result in low uptake of cessation resources (20, 40). Our current findings showing increased acceptance of quit line referrals post-implementation support using protocols that provide patients with tangible steps towards quitting. However, we acknowledge the need for further improvement with quit line connection post-referral given many were unreachable.
Smoking cessation protocols are infrequently used in rheumatology clinics, despite the fact that tobacco cessation is recommended by ACR, European League Against Rheumatism, and experts for CVD risk management (41, 42). Estimates from a multinational survey reported that <25% of surveyed rheumatology clinics had a smoking cessation process, and only half of rheumatologists reported providing smoking cessation counseling to their patients (43). Quit Connect is among only two other published smoking cessation protocols in rheumatology clinics. Naranjo et al. first reported a four-fold increase in quit rates after implementing an intervention consisting of a questionnaire, advice from a rheumatologist, and a three-month follow-up visit with a nurse (43). Aimer et al. compared a tailored RA-specific cessation program to standard of care showing no difference, and concluded that brief advice plus nicotine replacement therapy (as per quit line) are best practices (15). A Danish study in progress (clinicaltrials.gov/ct2/show/study/NCT02901886) is recruiting RA patients for a smoking cessation intervention including motivational interviewing and nicotine replacement therapy. The paucity of cessation protocol use in rheumatology clinics shows low cessation support for this high-risk population. Support could be increased by disseminating staff interventions such as Quit Connect that promote offering evidence-based quit line services using specialty clinic implementation strategies.
One may also consider Quit Connect’s cost-effectiveness for health systems based on clinic staff time and wages per guidelines for pragmatic cost-effectiveness (44). Our previous rooming time study (34) estimated 10 s. to ask readiness to quit and 80 s. to offer referral. This amounts to 4210 s. (70.2 min) to ask 421 patients readiness to quit and 9760 s. (232.7 min) to offer referrals to 122 ready patients. Totaling 5.05 hrs times a staff rate of $15/hour, it cost $75.73 to offer 122 patients referrals; 66 accepted. We examined quit outcomes for illustrative purposes despite small numbers. Within year one, quit line follow-up calls reported six of 18 contacted patients reported six month abstinence (33%; 19% intention to treat (ITT) 6/31 attempted calls). Multiplied across 16 referred patients in six months this estimates five sustained quits. Dividing $75.73 by five, equals $15.14/quit ($25.24/quit ITT). Total cost divided by 66 accepted referrals, estimates a cost of $1.15/referral to connect to free counselling and pharmacotherapy suggesting organizational cost-effectiveness.
Improvements in quit line referrals and staff capacity also support Quit Connect, which could be applied in other rheumatology or specialty clinics. We have maintained the protocol over four years, replicated findings in one community rheumatology clinic (45), and in an urban public hospital clinic in another state.
Limitations
Despite the strengths of our quasi-experimental study in three clinics, we acknowledge limitations. First, this was a pre-post design, not a randomized trial. Also, the abstracted baseline referral rate without individual covariates limited us to estimating effects versus adjusted multivariable analysis for the primary outcome of referral. Likewise, recognizing that tobacco users may require 8–10 attempts before quitting permanently, our six-month study provides only short-term outcomes of quit attempts (46). However, experts note that connecting patients to evidence-based resources makes them more likely to permanently quit (18) and supports treating tobacco use with a chronic care approach (17, 24). We also acknowledge the advantage of performing Quit Connect in rheumatology clinics that were already experienced with implementing a prior blood pressure protocol (23). However, this also adds the strength of knowing that both protocols have been maintained more than four years. We also acknowledge the possibility of unmeasured historic events influencing outcomes. Our baseline quit line referral rate was from abstracted records through 2011 which could suffer detection bias versus post-implementation direct rates. Our 2014 blood pressure project could have changed responsiveness to smoking, and temporal trends may contribute. We included 2012–2016 pre-intervention data to mitigate against skewed baseline trends. Recognizing another limitation, the number of unreachable patients, we revised the brochure to add caller ID details for quit line calls (16). Lastly, our study was limited to a single academic center with predominantly White, English-speaking patients. Future studies are in progress to implement across a more diverse population to ensure that our results are generalizable.
CONCLUSION
Our Quit Connect protocol for rheumatology staff was feasible in less than 90 seconds and increased quit line referrals 26-fold. Nearly one in three patients was ready to quit or cut back; among those ready, 71% agreed to quit line referral when offered. Rheumatology visits provide a unique opportunity to address smoking as a chronic modifiable risk factor in populations at high risk for CVD, pulmonary disease, and rheumatic disease progression. Given the importance of smoking cessation to reduce both immune and CVD risk, future studies should investigate wider-scale implementation and efficacy of protocols like Quit Connect that leverage free, state-run tobacco quit lines.
Significance and Innovations.
Although smoking is a risk factor for patients with rheumatic conditions and contributes to greater symptom severity and cardiovascular disease risk, standard smoking cessation intervention is rare in rheumatology clinics.
Primary care uses brief staff protocols to connect patients to free, state-run tobacco cessation quit line resources, but this approach had not been previously tested in rheumatology.
Quit Connect, our rheumatology clinic staff protocol and implementation plan, successfully led to electronic referrals for 71% of quit-ready rheumatology patients to a quit line.
Quit Connect was feasible to be performed by clinic medical assistants or nurses in <90 seconds at point of care and was effective in increasing quit line referrals 26-fold.
Statement of funding support:
Portions of this project were supported in part by Independent Grants for Learning and Change (Pfizer) and by a grant collaboration from the University of Wisconsin (UW) Clinical and Translational Science Award (CTSA) and UW School of Medicine and Public Health’s Wisconsin Partnership Program, through the NIH National Center for Advancing Translational Sciences (NCATS grant UL1TR000427). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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