Using a Mixed Methods Approach to Understand Shared Decision-Making in Lung Cancer Screening
Conditions: Smoking, Smoking, Tobacco, Smoking, Cigarette, Lung Cancer
Sponsor: Hackensack Meridian Health

CLINICAL STUDY PROTOCOL 
 
 
 
Understanding Shared Decision-Making in Lung Cancer Screening 
(DECIDE) 
 
Protocol Version 
10.31.2022 
version 2 
 
 
 
 
 
Please note the data for this study has already been collected at my prior 
institution and the only activity occurring at present is data analysis. 
 
 
 
 

 
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Study Personnel 
 
PI: Lisa Carter-Bawa, PhD, APRN, ANP-C, FAAN, Director 
Cancer Prevention Precision Control Institute at the Center for Discovery & Innovation, 
Lisa.CarterBawa@hmh-cdi.org 
Sub-I(s): n/a 
Statistical support (if applicable): Dr. Patrick Monahan (Indiana University); Mr. James 
Slaven (Indiana University) 
Protocol Development support (if applicable): Name, Title 
Name and information of sponsor: National Cancer Institute, Grant #R01CA222090 
Research Locations (all non-HMH locations): 
Kaiser Permanente Washington Health Research Institute 
1800 Minor Avenue 
 
 

 
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Abbreviations 
Abbreviation  
Explanation 
SDM 
Shared Decision Making 
LCS 
Lung Cancer Screening 
LDCT 
Low-dose computed tomography 
KPWA 
Kaiser Permanente Washington 
 
 
 
Revision History  
 
Revision # 
Version Date 
Summary of 
Changes 
Consent Change? 
 
 
 
 
 
 
 
 
 
 
 

 
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Summary  
Please note the data for this study has already been collected at my prior institution 
and the only activity occurring at present is data analysis. 
 
Study Title 
Understanding Shared Decision-Making in 
Lung Cancer Screening (DECIDE Study) 
Study Design 
Cross-sectional, survey 
Primary Objective 
1) Decision quality,  
2) Lung cancer screening completion, 
and 
3) Advances in stage of adoption for 
smoking cessation among 
individuals who currently smoke 
Secondary Objective(s) 
n/a 
Research Intervention(s)  
n/a 
Study Population 
Adults aged 55-80 years who currently 
smoke or quit within the past 15 years with 
a 30 pack-year tobacco smoking history 
Sample Size 
552 
Study Duration for individual participants 
6 months 
 
1 – Introduction    
Our long-term goal is to develop the next generation of decision support tools, 
including alternative communication strategies, for clinicians and patients to use for 
lung cancer screening decisions.  
 
This project specifically seeks to identify the components of patient-clinician 
discussions about lung cancer screening that contribute to high quality decisions and 
subsequent important behavioral outcomes (screening completion among patients 
who decide to screen; stage-of-readiness advancement for smoking cessation 
among current smokers).  
 
Our central hypothesis was that patients who perceive lung cancer screening 
discussions with their clinician as shared/mutual will have positive decisional and 
behavioral outcomes. The behavioral outcomes we are evaluating are consistent 

 
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with recent findings linking lung cancer screening with improved smoking cessation 
rates. 
We are examining clinician and patient factors simultaneously by collecting patient 
and clinician survey data and integrating it with patient EHR data. 
 
2 – Background 
2.1. Background/literature review (make sure you provide references) 
a. Scientific Background 
Provide the scientific or scholarly background for, rationale for, and significance of the 
research based on the existing literature and how will it add to existing knowledge. A 
short list of references or bibliography must be included as part of this document, 
uploaded separately, or provide the relevant page number(s) of the grant where 
references or bibliography can be found.  
 
Lung cancer kills more people in the U.S. than any other cancer, primarily because 
most people are diagnosed at an advanced stage where treatment options are 
limited, and mortality is high. Lung cancer screening is a complex issue because 
there are associated risks and potential harms including false-positive results, over-
diagnosis, and incidental findings that can lead to a cascade of follow-up tests and 
treatments. The decision to screen for lung cancer is not straightforward and is the 
epitome of a preference-sensitive cancer screening decision best made in the 
context of SDM. Multiple professional organizations advocate for SDM in lung cancer 
screening,1,2 and in an unprecedented move, Medicare mandated SDM 
documentation for screening reimbursement. SDM has been studied in other types of 
cancer screening but has focused primarily on screening behavior as opposed to the 
decision-making process. 
 
SDM is viewed as the pinnacle of patient-centered care and resonates with the 
ethical imperative of respect for patient autonomy and engagement.3 Epstein and 
Street’s summary of the evidence on patient-centered communication posits clinical 
encounters between a patient and clinician should be based upon collaboration and 
deliberation.4 Research advancing the science of SDM has been thwarted primarily 
because the process of SDM has become somewhat synonymous with patient 
decision aids. The prevailing belief that information exchange via patient decision 
aids would enhance the patient-clinician collaborative process has resulted in studies 
that narrowly focused on the effects of decision aids rather than understanding the 
causal mechanisms between SDM and patient decisional and behavioral outcomes.5 
This project focuses on the critical components of SDM and factors associated with 
positive patient decisional and behavioral outcomes essential to advancing the state 
of the science in lung cancer screening decisions.  
 
1. Wender R, Fontham ET, Barrera Jr, E, et al. American Cancer Society lung 
cancer screening guidelines. CA Cancer J Clin. 2013;63(2):107-117. PMCID: 
PMC3632634 
 

 
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2. Bach PB, Mirkin JN, Oliver TK, et al. Benefits and harms of CT screening for lung 
cancer: a systematic review. JAMA 2012;307(22):2418–2429. PMCID: 
PMC3709596 
 
3. Crossing the quality chasm: A new health system for the 21st century. Institute of 
Medicine (US) Committee on Quality of Health Care in America. Washington 
(DC): National Academy Press (US); 2001. PMID:25057539 
 
4. Epstein RM, Street RL. Patient-Centered Communication in Cancer Care: 
Promoting Healing and Reducing Suffering. NIH Publication No. 07-6225. 
Bethesda, MD: National Cancer Institute. 2007, Retrieved from: 
https://healthcaredelivery.cancer.gov/pcc/pcc_monograph.pdf 
 
5. Shay LA, Elston Lafata J. Where is the evidence? A systematic review of shared 
decision making and patient outcomes. Med Decis Making. 2015;35(1):114-131. 
PMCID: PMC4270851 
 
3 – Rationale, Objectives and Hypothesis  
3.1. Study Rationale/Problem Statement/Research question or Study significance 
This project specifically seeks to identify the components of patient-clinician discussions 
about lung cancer screening that contribute to high quality decisions and subsequent 
important behavioral outcomes (screening completion among patients who decide to screen; 
stage-of-readiness advancement for smoking cessation among current smokers)  
3.2. Hypothesis (if applicable) 
Our central hypothesis was that patients who perceive lung cancer screening discussions 
with their clinician as shared/mutual will have positive decisional and behavioral outcomes. 
The behavioral outcomes we are evaluating are consistent with recent findings linking lung 
cancer screening with improved smoking cessation rates. 
3.3. Primary Objective 
Aim 1. Use survey and EHR data to identify clinician factors that predict: a) patient-
perceived lung cancer screening decision quality; b) screening completion among 
patients deciding to screen; and c) stage of readiness for smoking cessation among 
current smokers. Hypothesis 1: Increased clinician knowledge, positive attitude toward 
lung cancer screening, positive attitude toward shared decision making (SDM), decreased 
clinician perceived barriers, and high lung cancer screening referral propensity predict 
higher decision quality, screening completion, and advances in stage of readiness for 
cessation. 
 
Aim 2. Use survey and EHR data to identify patient factors that predict: a) patient-
perceived lung cancer screening decision quality; b) screening completion among 
patients deciding to screen; and c) stage of readiness for smoking cessation among 
current smokers. Hypothesis 2: Patient-perceived stigma and medical mistrust predict 
decreased decision quality, screening completion, and advances in stage of readiness for 
cessation. 

 
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3.4. Primary Outcome Variable(s) 
a) patient-perceived lung cancer screening decision quality; b) screening completion among 
patients deciding to screen; and c) stage of readiness for smoking cessation among current 
smokers 
 
4 - Study Design 
Please note the data for this study has already been 
collected at my prior institution and the only activity 
occurring at present is data analysis. The following 
is offered for context: 
4.1 General Design 
DECIDE is a descriptive, observational study that includes two populations: 
 
1. KPWA primary care providers (PCP - MDs, DOs, PAs and ARNPs), who have 
documented, in EPIC, at least one lung cancer screening SDM conversation with an 
eligible KPWA patient. 
2. KPWA patients who have recently engaged in a documented LCS SDM conversation 
with a KPWA PCP. 
 
Both populations were invited to participate in a survey (two different surveys for the two 
different populations), regarding their opinions about and experiences with the LCS SDM 
process. Prospective LCS-related clinical data about the patient participants were 
collected for 12 subsequent months after they completed the study survey to determine 
whether they screen, the outcome of their screening and any use of smoking cessation 
services. 
 
Data from the surveys and electronic medical records will be used to determine where 
improvements can be made in the SDM for LCS space and to help work toward the goal 
of developing revised or new tools for this process. 
  
 
4.1.1 Study Duration (if applicable) 
Total study duration = 5 years (2018-2023). Participants were enrolled in the study and had 
one follow-up time point in the study at 6 months post shared decision making discussion 
with their primary care clinician. 
4.1.2 Number of Study Sites 1 
Kaiser Permanente Washington Health Research Institute 
1730 Minor Avenue 

 
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Seattle, WA 98101 
Site PI: Dr. Karen Wernli (Karen.Wernli@kp.org) 
 
4.2 Study Population 
This was a descriptive, observational study that included two populations: 
1. KPWA primary care providers (PCP - MDs, DOs, PAs and ARNPs), who have 
documented, in EPIC, at least one lung cancer screening SDM conversation with an 
eligible KPWA patient. 
2. KPWA patients who have recently engaged in a documented LCS SDM conversation 
with a KPWA PCP. 
4.2.2. Eligibility Criteria 
a. Inclusion criteria  
Provider Inclusion Criteria: 
• 
KPWA primary care provider (Family Practice or Internal Medicine) 
• 
MD, DO, ARNP or PA 
• 
Has documented at least one SDM for LCS in a patient EMR since KP (GH) 
program rollout in January 2015. 
Patient Inclusion Criteria 
• 
KP-enrolled 
• 
Meet LCS eligibility criteria (age 55-80, 30+ pack years cigarette smoking, 
current smoker or quit in past 15 years) 
• 
Had EMR-documented LCS SDM conversation with a KPWA PCP in the past 
month 
b. Exclusion criteria  
• 
Anyone who is on KPWHRI’s Do Not Contact list 
• 
Patients who engaged an interpreter during the  visit at which the LCS for SDM 
conversation took place 
4.2.3. Vulnerable populations (if applicable). Vulnerable populations include children, 
prisoners, cognitively impaired individuals, economically or educationally 
disadvantaged individuals, employees, students. When vulnerable populations are 
included, indicate what safeguards are in place to minimize coercion or undue 
influence to participate. 
n/a 
4.2.4. Withdrawal criteria (as applicable) 
n/a 
4.3. Study procedures  
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 
4.3.a. Subject Identification 
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 
4.3.b. Data for Identification 
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 

 
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 4.3.c. Screening and Recruitment Process 
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 
4.3.1. Study discontinuation (if applicable) n/a 
4.3.2. Concomitant medication (if applicable) n/a 
4.4. Risks and Benefits  
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 
5 – Methods  
Sample Size Justification and Power Analysis. We enrolled 125 primary care providers 
and 553 patients. Power calculations were performed via simulation studies with a two-sided 
significance level set to 0.05. These power simulations accounted for the anticipated 
subsamples for the patient behavioral outcomes (for example, stage of readiness for 
smoking cessation and the fact that only current smokers will contribute data for this 
outcome). Based on this sample size, we are appropriately powered for fully testing the 
associations in Aims 1 and 2 with at least 80% power. Specifically, for the continuous 
outcomes, we are appropriately powered for testing the effect of an unbalanced binary 
variable with prevalence 25% or 75% and an adjusted standardized coefficient of at least 
0.2, based on a linear mixed-effects model. Additionally, we are sufficiently powered in 
terms of the binary outcomes of the aims, for testing the effect of an unbalanced binary 
independent variable with a prevalence of 25% or 75% and an (adjusted) odds ratio (OR) at 
least 2.5, using a mixed-effects logistic model. The tests for the continuous independent 
variables are expected to be more powerful for a given effect size in both continuous and 
binary outcomes. 
 
Quantitative Data Analysis and Interpretation. Our analyses were being defined a priori 
to address the study aims. We are using descriptive statistics such as means, standard 
deviations, and frequency distributions to examine data quality, identify patterns of missing 
and out-of-range values, and evaluate the assumptions of statistical tests. Remediation of 
normal distribution assumption violations will be accomplished using methods such as Box-
Cox family transformations, or other methods as appropriate. Assessment of internal 
consistency reliability of all scales will be carried out using the Cronbach’s alpha coefficient. 
Continuous outcomes (Aims 1 and 2) will be analyzed based on linear mixed effects models, 
with a random intercept according to the clinician, to account for the association between 
patients within the same clinician. Secondary analyses of item level components, consisting 
of Likert scale data, will be analyzed using Generalized Estimating Equations, which allow 
for the use of distributions in the Exponential Family other than the Normal Distribution. This 
will allow for the testing and use of the appropriate probability distribution to be used, while 
also accounting for the association between patients within the same clinician. Model 
selection for analyses focused on correlates of decision quality and screening completion 
will be based on Akaike’s Information Criterion (AIC), instead of the usual statistical 
significance level (to avoid type I error inflation); this approach has been shown to lead to 
optimized models. We will only consider the statistical significance of each independent 
variable in the final models if the corresponding overall model test is significant at an 
alpha=0.05. 
Patient-Provider Data Analyses. We are conducting a planned secondary analysis for 
physicians who have patients who respond to the survey (an estimated 40% of the total 
number of patients). We will perform a multilevel analysis using a model that contains 

 
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variables from providers and patients nested within those providers. This comparison may 
be on a 1:1 or 1:many level, depending on how many SDM patients of a given enrolled 
provider also enroll in the study. These multi-level models will be used to test physician level 
and patient level variables for their potential significant association with the outcomes. For 
continuous outcomes a linear link and normal error distribution will be used, and for binary 
outcomes a logit link and binomial error distribution will be used. The results will be 
interpreted in terms of the model’s coefficients which represent average effects over the 
sample; therefore, analyses involving linking of patients and providers will protect the 
providers and patients from being identified.  
  
Data Quality Control. Much of the data collected has been as part of previously validated 
instruments, yielding high quality data. 
5.1. Screening 
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 
5.2. Recruitment, enrollment and retention (including screen failures as applicable) 
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 
5.3. Study intervention (including schedule of events and study visits) 
n/a 
5.4. Assignment / randomization (if applicable) 
n/a 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.5. Section of instruments (to include for all studies with a social behavioral 
intervention) - data has already been collected at prior institution; currently analyzing de-
identified data only 

 
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5.6. Data management (data collection, source and storage) 
The study data is stored on secure KPWHRI servers behind a firewall. The SRP and the 
study programmer will have access to the identifiers/linking files. The identifiers/linking 
files will be destroyed no more than five years after the completion of study funding 
(8/23) – August 31, 2028. Since we plan to characterize the eligible study populations of 
providers and patients before obtaining consent from those who choose to participant, 
we will assign different study IDs for the analysis we plan to do of the eligible 
participants as a whole from the IDs we assign to consented participants. Only the study 
programmer will have access to a linking file between the two. 
 
We will use KPRHWI’s Secure File Transfer site to transfer limited data sets to our 
collaborating partners at Hackensack Meridian Health and Indiana University. We will 

 
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also put a DUA in place with these partners to ensure that transferred data, which we 
expected to be a limited data set, have requisite privacy protections. 
 
5.7. Follow-up and end-of study (if applicable) n/a 
6 - Trial Administration 
n/a (data collection completed at prior institution; currently analyzing de-identified data only) 
 
7- Resources Available 
7.1. Describe the resources available to conduct the research:  
The research has been conducted – all data has been collected. At this point, the study 
team is analyzing the data for dissemination.