Changing Practices: Models
Targeting Primary Care Referrals to
Smoking Cessation Clinics Does Not
Improve Quit Rates: Implementing
Evidence-Based Interventions into
Elizabeth M. Yano, Lisa V . Rubenstein, Melissa M. Farmer,
Bruce A. Chernof, Brian S. Mittman, Andrew B. Lanto,
Barbara F. Simon, Martin L. Lee, and Scott E. Sherman
Objective. To evaluate the impact of a locally adapted evidence-based quality improve-
ment (EBQI) approach to implementation of smoking cessation guidelines into routine
Data Sources/Study Setting. We used patient questionnaires, practice surveys, and
administrative data in Veterans Health Administration (VA) primary care practices across
five southwestern states.
Study Design. In a group-randomized trial of 18 VA facilities, matched on size and ac-
smoking cessation care following structured evidence review, local priority setting, quality
improvement plan development, practice facilitation, expert feedback, and monitoring.
Control practices received mailed guidelines and VA audit-feedback reports as usual care.
Data Collection. To represent the population of primary care-based smokers, we ran-
domly sampled and screened 36,445 patients to identify and enroll eligible smokers at base-
interviewing to collect smoking behavior, nicotine dependence, readiness to change, health
status, and patient sociodemographics. We used practice surveys to measure structure and
process changes, and administrative data to assess population utilization patterns.
Principal Findings. Intervention practices adopted multifaceted EBQI plans, but had
difficulty implementing them, ultimately focusing on smoking cessation clinic referral
strategies. While attendance rates increased (po.0001), wefound nointervention effect on
Conclusions. EBQI stimulated practices to increase smoking cessation clinic referrals
and try other less evidence-based interventions that did not translate into improved quit
rates at a population level.
rHealth Research and Educational Trust
Key Words. Smoking cessation, quality of health care, veterans
Tobacco use is the leading preventable cause of mortality, accounting for
435,000 deathsin the United States (Mokdad et al. 2004) and 4.8million deaths
due to tobacco worldwide (Ezzati and Lopez 2003). In the United States alone,
smoking is responsible for $157 billion in annual health-related economic loss-
es,which translatesintoeach packofcigarettessoldintheUnitedStatesleading
to$3.45in medicalexpendituresand $3.73in lostproductivity(MMWR2002).
last 40 years chiefly through public health interventions (MMWR 2003), we
have not seen further declines in the past decade despite the availability of an
increasing array of efficacious treatments (Ranney et al. 2006). Routine treat-
ment of smokers by physicians has been a national health objective, but phy-
sician detection of smokers, counseling of smokers to quit, and prescription of
pharmacotherapy to aid them in quitting have been well below quality stan-
dards (Thorndike et al. 1998). Dissemination of smoking cessation clinical
practice guidelines during the mid-1990s offered substantial promise for mak-
ing greater inroads by promoting evidence-based recommendations targeting
changesin physicianbehaviorand adaptive changes inhealth care settings. In
particular, the guidelines reflect strong empirical evidence of the value of
systematic screening for tobacco use, advising smokers to quit, and providing
smoking cessation treatment (both counseling and medications) (Cromwell
Address correspondence to Elizabeth M. Yano, Ph.D., M.S.P.H., VA Greater Los Angeles
HSR&D Center of Excellence, Sepulveda VA Ambulatory Care Center (152), 16111 Plummer
Street, Sepulveda, CA 91343; e-mail: firstname.lastname@example.org. Elizabeth M. Yano, Ph.D.,
M.S.P.H., Lisa V. Rubenstein, M.D., M.S.P.H., Melissa M. Farmer, Ph.D., Brian S. Mittman,
Ph.D., Andrew B. Lanto, M.A., Barbara F. Simon, M.A., and Martin L. Lee, Ph.D., are with VA
Greater Los Angeles HSR&D Center of Excellence, Sepulveda, CA. Lisa V. Rubenstein, M.D.,
M.S.P.H., is with the Department of Medicine, VA Greater Los Angeles Healthcare System and
UCLA School of Medicine, Los Angeles, CA. She is also with RAND Health, Santa Monica, CA.
Elizabeth M. Yano, Ph.D., M.S.P.H., Melissa M. Farmer, Ph.D., and Brian S. Mittman, Ph.D., are
with the Department of Health Services, UCLA School of Public Health, Los Angeles, CA. Bruce
A. Chernof, M.D., was with Los Angeles County Department of Health Services, Los Angeles,
CA. He is now with The SCAN Foundation, Long Beach, CA. Martin L. Lee, Ph.D., is with the
and initial manuscript preparation, Scott E. Sherman, M.D., M.P.H., was with VA Greater Los
UCLA School of Medicine, Los Angeles, CA. He is now with VA New York Harbor Healthcare
System, New York, NY and the Section of Geriatric Medicine, New York University School of
Medicine, New York, NY.
1638 HSR: Health Services Research 43:5, Part I (October 2008)
et al. 1997; Fiore 2000). The guidelines offer an explicit roadmap for inte-
(Fiore, Jorenby, and Baker 1997; Raw, McNeil, and West 1999).
success. While early dissemination encouraged primary care physicians to ask
about smoking and advise their patients to quit, few practicing physicians met
criteria for adequate counseling to help smokers quit and fewer still provided
smokers with necessary assistance or arranged follow-up services (Goldstein
et al. 1998; DePue et al. 2002). System-level interventions have generally been
recommended to help individual clinicians adopt guideline-adherent practices
(Bero et al. 1998; Solberg 2000). These include improved forms of documen-
tation to record smoking status (e.g., intake forms, ‘‘vital sign’’ stamps, stickers),
clinician prompts or reminder systems for fostering guideline-adherent actions,
provision of on-site educational materials, designation of a coordinator or clin-
ical ‘‘champion,’’ training of nurses/support staff in person or by phone to
replace physician counseling, audit-and-feedback of clinician guideline adher-
ence, computerized decision support, and incentives (Lichtenstein et al. 1996;
Katz et al. 2004; An et al. 2006). Adoption of these approaches into practice
settings, however, involves organizational change (Wensing and Grol 1994;
Oxman et al. 1995). There is growing consensus that implementing research
into practice through organizational change(Stone et al. 2002) depends in large
part on the degree to which they account for or are adapted to the context of
individual practices (Grol 1992, 1997) to facilitate diffusion (Rogers 1995).
Quality improvement (QI) methods assist practices in implementing
Evidence-based quality improvement (EBQI) methods are based on the
premise that practices will have greater success in achieving true improve-
ments through organizational change using prior evidence from the literature
as a guide for their activities. The objective of this study was to evaluate the
effectiveness of an EBQI method for enabling health care managers, rather
than researchers, to implement evidence-based smoking cessation interven-
determine its impact on practice-level smoking cessation.
Study Design and Settings
We approached for participation all facilities with three or more primary care
Primary Care Referrals to Smoking Cessation Clinics1639
geographically proximal Veterans Health Administration (VA) health care
networks in the southwestern United States. Three facilities declined to par-
ticipate due to competing demands and time constraints. The research team’s
home institution was excluded from trial participation. We paired the re-
maining 20 eligible VA facilities on size and academic affiliation blocked on
VA network to control for the influence of network-level policies, resource
allocation, and leadership differences. One site’s Institutional Review Board
(IRB) wasclosed, eliminatingthesite anditsmatching facility, leaving 18 sites.
We used a group-randomized trial design to evaluate the impact of this
EBQI approach to smoking cessation guideline implementation (Campbell
(n59) or control group (n59). IRB approval was maintained at all final
EBQI Approach to Guideline Implementation
Using the U.S. Public Health Service smoking cessation guidelines as our
foundation for evidence-based practice recommendations, we designed and
used an organizational QI intervention comprised of physician and patient
educational materials, structured evidence review, local priority setting, QI
plan development and adaptation, and site-specific audit-and-feedback, sup-
plemented with ongoing expert review. At each intervention practice, 30-
minute didactic sessions on population-based approaches to smoking cessa-
tion were delivered, followed by implementation planning using an expert
panel process to help local opinion leaders set institutional priorities. Partic-
ipating sites and their QI teams, rather than researchers, were the decision
makers, ensuring local control of the adaptation of intervention features
(Rubenstein et al. 1995). In addition to promoting detection of smokers
through screening, smoking cessation experts from the research team pro-
vided evidence summaries for the main approaches advocated by the
Department of Defense (DoD)/VA clinical practice guidelines for smoking
cessation——namely, referral to a smoking cessation program, treatment within
primary care, or telephone counseling (DoD/VA, 2004)——and offered rec-
ommendations for minimum protocols and implementation strategies that
could be used to achieve organizational benefit (Sherman et al. 2005).
Following thepanel, eachintervention practice received a compendium
of smoking cessation resource materials and tools for patients and providers
processes and linking sites with research team assistance in obtaining and
1640 HSR: Health Services Research 43:5, Part I (October 2008)
implementing any of the best practices included in the compendium. Aca-
demic experts participated in monthly audio or video conferences with site
into explicit QI plans that laid out specific steps and resources necessary to act
upon their chosen strategies (e.g., telephone outreach, provider profiling). Site
leaders were encouraged but not required to revise their QI plans based on
expertreview and input.Bimonthly newsletters highlightedpractice successes
and challenges among participating sites. Each practice also received quar-
terly audit-and-feedback progress reports, including comparisons with other
sites. Table 1 describes implementation characteristics.
Control sites received guideline copies. Because smoking cessation was
reports from externally audited random patient records, which rank ordered
site- and network-level performance (Kizer 1999).
Sampling Strategy, Patient Approach, Screening for Smoking Status,
and Data Collection
We enrolled a representative cross section of smokers by screening random
samples of all practice patients with three or more visits in the previous 12
months. Patients were contacted by trained interviewers using computer-
assisted telephone interviewing, who described the study, administered in-
formedconsent usingstandardizedprotocols,andscreenedforsmoking status
(California Tobacco Surveys 2002). Current smokers were enrolled and fur-
ther surveyed in the same call between March 22, 2000 and February 23,
2001; 12-month follow-up interviews were conducted on approximate anni-
versary dates between April 9, 2001 and January 2, 2002.
Following enrollment, trained telephone interviewers administered a
20–30-minute survey instrument including measures of smokers’ sociodemo-
graphics (e.g., age, gender, race–ethnicity, insurance), health status (including
the Veterans’ SF-36 [ Jones et al. 2001], Mental Health Index [Veit and Ware
1983], depression [Andresen et al. 1994], physical activity [Ainsworth, Jacobs,
and Leon 1993], alcohol use [Saunders et al. 1993]), smoking behavior, nic-
otine dependence (Heatherton et al. 1991), readiness to change (DiClemente
et al. 1991), and prebaseline treatment experience. Random interviews were
in accordance with California law. At follow-up, smokers were resurveyed
regarding their smoking behavior and treatment experience. Site surveys and
intervention logs were used to track the implementation decisions of each
primary care practice (Sherman et al. 2006). The number of unique patients,
Primary Care Referrals to Smoking Cessation Clinics1641
Pre- and Postimplementation Characteristics of Participating
Preimplementation Structure and Process Measures among
System in place to ask about smoking every visit
Providers trained about smoking cessation on regular
Provider feedback about smoking cessation
Smoking cessation clinic in same building as
? Smoking status part of previsit assessment vitals
? Smoking status reminders in electronic record
? Screening performance tracked locally
? All/almost all smokers counseled
? Counseling reminders in place
? Counseling performance tracked locally
? Routine follow-up
? Patient educational materials
? Primary care providers can prescribe smoking
? All/almost all smokers referred to smoking
? Onsite smoking cessation clinic
? Smokingcessationclinicwaitingtimes ? 1month
Postimplementation Characteristics of Intervention Practicesw
Quality improvement implementation process
Qualifications of practice-level site champion (advanced degree) 4 Ph.D., 3 R.N., 1 M.D.,
9 sites (100%)
9 sites (100%)
9 sites (100%)
Chose and endorsed top 5 smoking cessation priorities at kickoff
Given tailored expert advice to help develop QI plans
QI plan focused on referral to smoking cessation program (instead of
treatment in primary care or telephone counseling)
Expert feedback on QI plans provided (no modifications made)9 sites (100%)
Content of local smoking cessation QI plans by type of evidence-based practice (EBP)
Provider education (e.g., CME workshops, Grand Rounds, posters)
Patient education/activation (e.g., letters, marketing, facilitator)
1642HSR: Health Services Research 43:5, Part I (October 2008)
visits and mean visits per patient for primary care and smoking cessation
clinics for intervention and control practices were obtained using designated
clinic stop codes (301 and 323 for primary care; 707 for smoking cessation)
from the VA Outpatient Clinic file.
Baseline characteristics of enrolled patients were compared by intervention
group using t-tests for continuous variables and w2tests for categorical vari-
ables. Variables hypothesized to be predictive of smoking cessation were
tested; predictor variables significantly correlated with smoking cessation
were evaluated for collinearity and subsequently included in the logistic
regression model in an intent-to-treat analysis (Mickey and Greenland 1989).
We used random hot deck imputation (Little 1988) to handle missing
covariate values (fewer than 1–2 percent). We weighted patient data for the
probability of enrollment (i.e., to better represent the target population of all
primary care users) and attrition (i.e., to adjust for nonresponse between
baseline and follow-up) using recursive logistic regression models (Rubin
1987). Predictor variables for enrollment weights included study randomiza-
Provider feedback (e.g., # referrals, # on pharmacotherapy)
Changes to primary care counseling, referral, and follow-up procedures
(e.g., brief counseling program, computerized referral)
Changes to smoking cessation program (e.g., counselor/nurse hired)
Provider incentives (e.g., referral incentives)
Level of QI plan implementation by intervention practice
# of Planned
# of Planned EBPs
# of New Elements
# New Elements
nData source: VA Smoking Cessation Coordinator and Ambulatory Care Manager Surveys,
administered as paper-and-pencil organizational surveys at baseline (Sherman et al. 2006).
wData source: Practice-level process evaluation logs and patient surveys.
Primary Care Referrals to Smoking Cessation Clinics1643
weights included patient age, readiness to change, access to other public or
private insurance, disability status (i.e., recipient of worker’s compensation or
Social Security Income), and reliance on VA care (i.e., exclusive VA use
versus non-VA and low-VA use). Final weights were the reciprocal of the
product of the model-predicted probabilities for the stages of enrollment and
We used weighted logistic regression (SPSS version 11.5) to evaluate
intervention effects,adjusting for patient-levelpredictorsof smoking cessation
(301 days without smoking) at 12-months follow-up. We assessed the intra-
class correlation coefficient (ICC) to determine the need for cluster adjust-
ment; because the ICC was not statistically significant from zero, an
unadjusted analytic approach was used. We assessed goodness of fit with
the Hosmer–Lemeshow statistic (Hosmer and Lemeshow 2000).
We analyzedsmokingcessation clinic attendanceratesby identifyingall
codes in VA administrative data files for 12 months before (fiscal year or
cohort), and after EBQI implementation (FY2001). We divided these by the
estimated number of smokers in each practice (estimated prevalence of smok-
ing) multiplied by the number of primary care patients seen in the same time
periods as rates per 1,000.
Implementation of Evidence-Based Smoking Cessation Care
Randomized practices were equivalent at baseline, with exceptions of higher
use of provider incentives and tracking of screening performance in inter-
vention practices and more provider counseling feedback and pharmaco-
therapy prescription authority in control practices (Table 1). Ranging from
five to 13 participants, kickoff intervention meetings all included the heads of
Medicine and/or Primary Care, while senior leadership (i.e., director) was
present in six of nine practices. Practice-level site champion qualifications
varied (four Ph.D.s, three R.N.s, one M.D., and one other). All intervention
practices (100 percent) chose and endorsed their top 5 priorities for smoking
cessation, received tailored expert advice to help develop locally customized
QI plans and then received expert feedback on them. All QI plans focused on
smoking cessation clinic referrals as the main guideline implementation strategy.
1644 HSR: Health Services Research 43:5, Part I (October 2008)
Local QI plans for accomplishing this goal varied, with emphasis on smoking-
related provider education, feedback, and patient education (Table 1).
Intervention practices’ actual levels of implementation varied (Table 1).
Provider and patient education activities were most commonly implemented
(both rated as ‘‘not too difficult’’ to implement). While more broadly pro-
posed, provider feedback (e.g., # of referrals, # on nicotine replacement
therapy) was only partially implemented in a single site (rated ‘‘very difficult’’
in sites unable to implement). Changes to primary care (e.g., brief counseling,
computerized reminders) and to smoking cessation clinics (e.g., counselors or
‘‘health techs’’ hired) were each planned and implemented in only two sites
(and rated as ‘‘very difficult’’). Provider incentives linked to referral behavior
were only partially implemented in one site (rated ‘‘difficult’’). Five practices
(threeof which wereunable to implement themajority of the activities in their
QI plans) added new activities not part of their original plans. These new
activities were generally not part of the evidence-based toolkit that they were
provided and thus varied in the degree to which they were evidence based
(e.g.,addition of10hoursofpsychtech time,increasedavailabilityofavariety
of behavioral programs, establishment of a separate smoking cessation phar-
macy clinic, addition of clinical pharmacists to provide individual counseling,
and tobacco cessation clinical reminder).
Practice-Level Smoking Cessation Process Changes
referrals (from 1,137 to 1,926 smokers seen) compared with control practices
(from 1,979 to 1,993) (po.0001) between baseline and 12-month post-EBQI
implementation (Table 2).Intervention practices alsodemonstrated increased
attendance rates (from 56.0 to 72.6 per 1,000) versus control practices whose
attendance rates actually declined in the face of influxes of more primary care
patients to the system (from 77.0 to 68.3 per 1,000) (po.0001). Among those
who attended, smokers in intervention practices had more visits post-EBQI
(3.9 versus 3.5, po.001).
Identification and Enrollment of Smokers at Intervention and Control Practices
We screened over 36,000 randomly sampled primary care patients to identify
percent response rate at 12-month follow-up (Figure 1). Overall, 77 percent of
primary care patients had smoked in their lifetimes, while 20 percent were
current smokers. Enrolled patients tended to be men (94 percent), with a high
Primary Care Referrals to Smoking Cessation Clinics1645
Practice-Level Smoking Cessation Process Changes
Unique Patients and Visit Ratesn
Unique Patients and Visit Rates
1,214 (4.2 ? 3.8)
91,741 (3.0 ? 2.6)
1,582 (3.5 ? 3.0)
80,775 (2.6 ? 2.9)
1,137 (4.4 ? 4.4)
85,274 (3.2 ? 2.8)
1,979 (3.2 ? 3.6)
100,830 (3.0 ? 3.1)
1,926 (3.9 ? 3.3)
111,536 (3.4 ? 3.0)
1,993 (3.5 ? 3.8)
114,357 (2.9 ? 3.0)
numbers ofuniquepatients seen in primary care clinics(usingclinic stopcodesfor 301 generalmedicine and323primary care), in addition to meanand
SD visits among those patients, respectively.
wSmoking cessation attendance rates were calculated as a function of the number of unique patients seen in smoking cessation clinics divided by the
estimated number of smokers ? 1,000 to arrive at smoking cessation attendance rates per 1,000 patients. We estimated the number of smokers by
multiplying the number of primary care patients and the estimated prevalence of smoking obtained from the VA Office of Quality & Performance for
intervention and control practices (238 and 255 per 1,000, respectively).
1646 HSR: Health Services Research 43:5, Part I (October 2008)
We found no baseline differences in sociodemographics, health habits,
more likely to smoke everyday, wake up to smoke, and to have tried nicotine
patches, attended a smoking cessation program, and tried other ways to quit
preintervention. They reportedlowergeneral health statusand greaterreceipt
of disability or Social Security Income, and were less likely to live alone. They
were also more likely to use primary care providers for their health care
compared with specialists.
Practices caring for
primary care patients
Current Smokers Enrolled
Current Smokers Enrolled
• 6,065 not eligible (e.g., non-smokers) (33.4%)
• 4,042 refused (22.4%)
• 2,940 phone problems (16.3%)
• 4,042 not located/no response (22.4%)
• 6,337 not eligible (34.4%)
• 4,139 refused (22.4%)
• 3,198 phone problems (17.4%)
• 3,741 not located/no response (20.3%)
Enrollees at Follow-up
Enrollees at Follow-up
# Lost to Follow-up:
• 17 not eligible (1.8%)
• 92 refusal at follow-up (9.9%)
• 111 phone problems (12.0%)
• 190 not located/no response (20.5%)
# Lost to Follow-up:
• 57 not eligible (5.6%)
• 93 refusal at follow-up (9.2%)
• 137 phone problems (13.5%)
• 164 not located/no response (16.1%)
Random cross-section of all
Random cross-section of all
VA Health Care Facilities
Practice pairs matched
on academic affiliation
Figure1: Screening and Enrollment of Practice-Based Cohort of Smokers
Primary Care Referrals to Smoking Cessation Clinics 1647
Baseline Characteristics of Enrolled Smokers by Intervention
Age, mean years (SD)
Gender, % women
Education, mean years (SD)
Employment status (%)
Fulltime or parttime for pay
Unable to work due to health or injury
Other (e.g., unemployed, student, active military)
Annual household income (%)
Lives alone (%)
Receives Medicare (%)
Receives Medicaid (%)
Has private insurance (%)
Disability or Supplemental Security Income (%)
Most or all care through VA (%)
Usually sees primary care doctor or nurse (%)
Smoking frequency (everyday versus some days) (%)
Level of nicotine dependence (past 6 months) (%)
Smokes within 15 minutes upon wakening
Wakes up, smokes 1–2 times/month or more
Ever tried to quit smoking (%)
Ever used nicotine skin patches (%)
Ever used nicotine gum (%)
Talked to M.D. or M.H. professional about quitting (%)
Attended cessation program (past 12 months) (%)
Tried other ways to quit (past 12 months) (%)
Excellent to very good health (%)
Alcohol use (AUDIT score), mean (SD)
Depression (CES-D score), mean (SD)
Have problems caused by smoking (%)
1648HSR: Health Services Research 43:5, Part I (October 2008)
Patient-Reported Smoking Cessation Process and Outcome Changes
At 12-month follow-up, 8.7 and 9.0 percent of enrolled smokers quit in in-
tervention and control sites, respectively (Table 4). Control practice smokers
reported more nicotine patch prescriptions and more referrals to smoking
cessation programs, consistent with their self-reported histories pre-EBQI im-
plementation; attendees, in turn, were more likely to be prescribed Zyban or
Wellbutrin. Adjusting for baseline differences (noted in Table 3), we found no
intervention effect on quit rates (Table 4). We had a 71 percent chance of
detecting a statistically significant difference at the po.05 level between in-
tervention and control smokers in post hoc power analyses (assuming a two-
sided test). Across all practices, patients who quit were more likely to see
primary care providers for their usual care (OR52.68, 95 percent CI 1.41–
5.68) and less likely to be everyday smokers (OR50.47, 95 percent CI 0.28–
0.79) (Table 4).
We found that EBQI approaches to helping practices implement guideline-
adherentsmoking cessationcare intoroutinepractice achievedalimitedset of
evidence-based process changes but failed to improve patient quit rates. We
explore a number of possible explanations for our results and their implica-
tions for implementing evidence-based practices.
First, we found that practices, as they had intended, succeeded in im-
plementing increased smoking cessation clinic referrals. Practices rated such
referrals as their top-ranked QI strategy (as opposed to primary care or tele-
phone-based guideline alternatives), despite advice from the study’s expert
cessation supports smoking cessation clinic referral as a virtual gold standard
(Fiore, Jorenby, and Baker 1997), using this approach to improve outcomes
For example, QI teams may not have adequately considered the impact of
attempting to direct a larger flow of patients to a scarce resource, such as the
potential for ‘‘bottlenecks’’ due to limited capacity. Experts also cautioned
teams about the substantial evidence that many patients do not agree to re-
ferral,and that many who agree do notactually attend (Thompson et al. 1988;
for large segments of the primary care population. In addition, referral delays
may reduce the immediacy of PC physician responsiveness to patient readi-
Primary Care Referrals to Smoking Cessation Clinics1649
Evidence-Based Quality Improvement (EBQI) Implementation of Smoking
Patient-Reported Smoking-Related Processes and Outcomes after
Unadjusted 12-Month Patient-Reported Smoking-
Related Processes and Outcomes
Smokers in Intervention
Practices (n5515) (%)
Smokers in Control
Practices (n5565) (%)
Physician talked about quitting smoking
If yes, prescribed nicotine gum
If yes, prescribed nicotine patches
If yes, mentioned self-help classes
Physician referred to smoking cessation
Of referrals, patients who recalled
attending program referred by physician
Of attendees, patients who recalled being
prescribed Zyban or Wellbutrin
Smoking cessation (i.e., last smoked on
regular basis 430 days)
Multivariate Predictors of Smoking Cessation at
Smoking Cessation (i.e., Last
on Regular Basis 430 days)
Odds Ratio (95% CI)
White (versus nonwhite)
Lives with other people (versus alone)
Higher perceived general healthw
Everyday smoker (versus some days)nn
Nicotine dependence (night-time awakening to smoke)z
Smoking cessation program attendance with or without M.D.
Nicotine skin patch use
Usually sees primary care M.D. or nurse for health care§nn
0.90 (0.58, 1.39)
1.08 (0.66, 1.76)
1.42 (0.90, 2.33)
1.08 (0.84, 1.39)
0.47 (0.28, 0.79)
0.93 (0.82, 1.05)
0.53 (0.26, 1.10)
0.83 (0.52, 1.33)
2.83 (1.41, 5.68)
nnnpo.001. Model Wald’s w2(smoking cessation)531.57, po.0005, for weighted logistic regres-
sion model. Hosmer–Lemeshow demonstrated goodness of fit.
wHigh score5higher general health perception (excellent, very good, good, fair, poor).
zHigh score5higher frequency with which smokers report waking up and smoking a cigarette at
night in the past 6 months using a seven-point Likert scale (every night, most nights, a few times a
week, once a week, once or twice a month, once or twice, not at all).
§Compared with non-PC providers (i.e., specialist, mental health professionals, or pharmacist).
1650 HSR: Health Services Research 43:5, Part I (October 2008)
ness to change. Recent evidence regarding the effectiveness of smoking ces-
sation helplines (i.e., Quitlines) and e-mail messaging suggest that immediate
On one level, the choice of the referral strategy by QI teams is under-
standable. Busy PC physicians may have found the referral option, accom-
panied by rigorous evidence of smoking cessation clinic effectiveness,
especially attractive. Study practices also experienced substantial increases
in patient volume over the course of the study, making referral, with its low
require more PC-based participation. Increasing patient volume may also
have contributed to generally reduced levels of organizational slack (Rogers
1995) for undertaking more complex or difficult strategies. The referral strat-
egy, however, may not have had a large enough reach across primary care
patients to impact population smoking cessation outcomes.
We expected our EBQI approach to enable participating sites to im-
plement packages of recommended evidence-based strategies geared to ac-
commodating the full range of needs of primary care smokers. Instead, the
EBQI process resulted in QI plans with a mix of evidence-based and non-
evidence-based interventions, many of the most promising of which did not
get implemented as intended. The practices tried to add several PC-based
activities into their QI plans (e.g., provider feedback reports) on top of the
focus on smoking cessation program referrals in response to expert feedback.
Ultimately, however, few practices succeeded in implementing these addi-
tional strategies, with the exception of provider and patient education, neither
of which are considered sufficient in and of themselves. Most practices ended
up trying to incorporate additional unplanned strategies that were not really
evidence based (i.e., good ideas but lacked prior empirical evidence of their
So why did the intervention practices listen selectively to the evidence
and the advice of smoking cessation ‘‘experts’’? One possibility is that our
intervention practices’ more ambitious QI plans were not accompanied by
adequate resources. In applying EBQI to depression care improvement, for
implement their proposed QI strategies (Rubenstein et al. 2006). The study
also provided QI team members with paid release time and on-site QI fa-
cilitation. In contrast, our study provided only education and facilitation from
a distance. Without more support, stakeholder teams may accomplish the
‘‘plan-do’’ (PD) phase of PDSA cycles, without investing in the remaining
processes necessary to accomplish true change (Walley and Gowland 2004).
Primary Care Referrals to Smoking Cessation Clinics1651
While we provided considerable data on local smoking cessation-related per-
formance to the QI teams (e.g., smoking cessation visit rates), we did not
provide information technology (IT) tools for them (e.g., no reminders or
templates). IT capabilities might have boosted practice success (Hawe et al.
2004). Overall, more attention should be paid in future smoking cessation QI
efforts to the level and types of resources needed to accomplish major change
(Flottorp, Havelsrud, Oxman 2003).
Another possibility is that, while we tried to stack the QI processes in
favor of the evidence, it was easier for participants to expand on something
that was already in place (the smoking cessation clinic) than take on new
initiatives focused on counseling and treatment in primary care. Many par-
ticipants in the initial priority-setting meetings voiced comfort with having a
smokingcessationclinictosolvetheirperformance problems. Consistentwith
findings from depression care improvement, practices tended to choose pas-
sive strategiessuchaseducationrather than active changestrategies(Sherman
et al. 2007a), whereas the active strategies that encompass organizational
change may be most effective (Stone et al. 2002).
In addition, tension exists between wanting to learn from an expert and
the realities of not wanting to be told what to do and the notion that the expert
does not know ‘‘our patients’’ or ‘‘our place.’’ The EBQI process also relies on
local authority to make organizational changes, and is thus dependent on the
strength oflocal leadership (Rubensteinetal.2002).Also,thesepracticeswere
is some evidence that practices choosing to buy help, rather than partake of
offered help, fare better (Parker et al. 2007). The high level of practice choice
may have helped stakeholders to ‘‘own’’ the process and outcomes of their
EBQI experience, and might be expected to increase the likelihood of their
sustaining adopted changes, but also supported the observed variability
After this intervention, our next studies focused instead on premade
is where the handoffs in this strategy occur. In other words, when does the
researcher walk away and the practice stand alone? Unlike QI models pro-
meeting time within the context of ‘‘collaboratives’’ (Pearson et al. 2005),
EBQI is designed to leverage initial planning meetings into local innovation
and ownership. The literature is not clear on how these alternate QI models
differ (Mittman 2004), but one issue remains central to all of them and that is
the need for better insights on how one gets the QI process to reflect real life.
1652 HSR: Health Services Research 43:5, Part I (October 2008)
By the luck of the draw, intervention practices also appeared to be at an
early disadvantage compared with control practices. Control practices ap-
peared more likely to be early adopters of smoking cessation interventions at
counseling and gave PC clinicians authority to prescribe smoking cessation
medications (e.g., nicotine patches), and their baseline rates of smoking ces-
on administrative data and patient self-report. By 12-month follow-up, inter-
vention practices had significantly increased their attendance rates, while
may have helped convince managers and providers of the value of smoking
cessation improvement (Michie et al. 2004) and in turn given them a struc-
tured process for successfully implementing at least one facet of evidence-
based care they had targeted (i.e., increased referrals to smoking cessation
programs). However, because the groups were not balanced at baseline
despite randomization, it is difficult to interpret with certainty the cause for
equivalence at follow-up. If intervention practices were in fact later adopters,
then we may be observing a natural catch-up process independent of EBQI.
While intervention practices accomplished higher attendance rates
practice-wide, EBQI-fostered changes failed to have an impact on patient
outcomes in the form of smoking cessation. Control practices’ efforts, without
support from EBQI implementation, accomplished equivalent quit rates.
There are several possible explanations for this result. First, our central out-
practices. If the best possible ‘‘evidence-based treatment’’ achieved a 10 per-
cent increase in cessation rates (i.e., a very reasonable intervention) and the
implementation method (here, EBQI) improved delivery of this intervention
by 30 percent (i.e., a very successful QI implementation strategy), at best we
can achieve a 3 percent increase in population cessation rates. While effects of
this size can be difficult to measurein the context of a scientific study,an effect
of this order would be important from a public health standpoint. Second,
both intervention and control practices were operating under national VA
performance measures incentivized at network and facility levels. Thus, con-
of an EBQI process to foster priority-setting, external expert review, and
practice feedback. Instead, their practice feedback came in the form of na-
tionally provided measures of local smoker identification and tobacco coun-
seling rates that may have resulted in their higher levels of PC-based smoking
cessation interventions at baseline. The value of PC-based changes is further
Primary Care Referrals to Smoking Cessation Clinics1653
supported by our patient-level trial results demonstrating that the strongest
independent predictor of smoking cessation was usually being seen by a pri-
mary care provider for their health care.
Our study lends itself to several teaching points for research–clinical
partnerships. First, researchers must be cautious in overselling the potential
absolute impacts (e.g., percent change) of evidence-based practice when
applied to the practice or population of patients served. In essence, pushing a
large volume of patients into a small ‘‘box’’ (i.e., smoking cessation clinics)——
even if it is a great ‘‘box’’——is not going to work and only a fraction of smokers
will be affected. Second, practicing clinicians and managers must be mindful
that even small but consistently positive impacts at the population level may
still yield important benefits (i.e., 3 percent of 46,000 smokers translates into
almost 1,400 fewer smokers, with the concomitant improvements in health
status and potential cost savings over time). Viewing these efforts as learning
partnerships and using them to confront barriers, address local resources (hu-
man and financial) and refine processes in the spirit of continuous improve-
yield of future initiatives.
This study has a number of notable limitations. First, in the absence of a
practice registry of smokers, we had to screen thousands of patients to identify
a systematic sample of smokers. We used enrollment weights to address pat-
terns of refusal and noncontact. We also incurred significant sample losses at
follow-up; we used attrition weights to address potential response biases that
et al. 2000). We empirically found that patients’ readiness to change was not
predictive of participation at follow-up. Consistent with the veteran popula-
tion of VA users, our sample of smokers also over-represented older men,
limiting the generalizability of patient-level results to similar groups. We
measured smoking cessation attendance rates using national VA administra-
tive data files, and may not have captured all visits due to local coding differ-
We also found discrepancies in rates of smoking cessation clinic atten-
dance between administrative and survey data at follow-up. Administrative
data demonstrated comparable attendance rates between intervention and
control practices (i.e., intervention sites had ‘‘caught up’’), while patient-re-
ported attendance was higher in control practices. While we randomly sam-
pled clinic visitors, it is possible that enrolled smokers represented more
frequent users (Lee et al. 2002). We also had access only to age and gender of
nonparticipants, limiting the precision of our ability to weight to the popu-
1654 HSR: Health Services Research 43:5, Part I (October 2008)
lation of smokers. Patient-reported histories of referral and attendance were
also higher in control practices at baseline, so it should not be surprising that
they remained higher at follow-up. Time windows also differed somewhat
guidelines, made possible by computerized reminders, routine feedback
of chart-based audits and performance incentives since the mid-1990s (Ward
et al. 2003). While the focus on Ask and Advise has helped the VA achieve
remarkable results on screening for tobacco use and physician counseling, we
believe our findings point to the fact that attention to Assess, Assist, or Arrange
in the ‘‘5 A’s’’ is now warranted as smoking cessation treatment remains low
(Anderson et al. 2002; Jonk et al. 2005). VA’s common purpose and priorities
are also important vehicles for knowledge creation when QI is armed with
research evidence (Francis & Perlin 2006). EBQI holds promise for overcom-
ing barriers to translating evidence into practice (Shojania and Grimshaw
2005), by making relevant research knowledge, data and tools accessible to
practicesnotprepared tosupporttheir priorities withorganizational resources
for training, IT support, and protected time to design and implement planned
QI activities (Solberg et al. 2000; Feifer et al. 2004).
Joint Acknowledgment/Disclosure Statement: This study was funded by the VA
HSR&D Service (Project #CPG 97-012). The trial was registered through
ClinicalTrials.gov (Registry No. NCT00012987). Dr. Farmer was supported
by a VA HSR&D career development award (Project #MRP 04-221). Dr.
Yano was supported by the original grant, and subsequently by the VA
Greater Los Angeles HSR&D Center of Excellence (Project #HFP 94-028)
and a VA HSR&D Research Career Scientist Award (Project #RCS 05-195).
We acknowledge the support of our site principal investigators: Timothy
Carmody, Ph.D., Carol Chavez, N.P., Linda Ferry, M.D., Michael Gould,
M.D., Betty Hedrick, N.P., Linda Hill, R.N., James Howard, M.D., Susan
Blair Knepper, F.N.P., Charles McCreary, M.D., Matthew Meyer, Ph.D.,
Celia Michael, Ph.D., Nicole Miller, Ph.D., Sharon Rapp, Ph.D., Mitch Rice,
R.N., M.N., Robert Smyer, M.D., David Webb, M.D., Brian Yee, M.D., and
Sheila Young, Ph.D. We also acknowledge MingMing Wang, M.P.H.,
for administrative data acquisition and analysis, Ismelda Canelo, M.P.A., for
Primary Care Referrals to Smoking Cessation Clinics1655
administrative support, and the many interviewers and staff at California
thank the editors and reviewers for their extremely helpful, substantive input
leading to improved reporting of these trial results.
Disclosures: The authors have no relevant financial interests or advocacy
positions pertaining to this manuscript. VA policy requires submission of a
press releases as needed in anticipation of publication, but they do not un-
dergo or require internal peer review or comment periods. Preliminary ver-
sions of this work were presented at the VA HSR&D Annual Meeting (2004,
2006) and the Society for General Internal Medicine (2003).
Disclaimers: Views expressed in this article are those of the authors and
do not necessarily represent the views of the Department of Veterans Affairs.
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Primary Care Referrals to Smoking Cessation Clinics1661