||5R01CA222090-03 Interpret this number
||Sloan-Kettering Inst Can Research
||Using a Mixed Methods Approach to Understand Shared Decision-Making in Lung Cancer Screening
Efforts to enhance patient-centered care have resulted in increasing advocacy for shared decision-making
(SDM). Yet, the active ingredients of SDM that positively impact decision quality and patient outcomes remain
elusive. Lung cancer screening discussions and decisions are challenging for both patients and clinicians where
benefits are present, but risk and uncertainty exist and vary by individual. There is a critical need to determine
how patients and clinicians are approaching SDM in lung cancer screening and the impact on decision quality
and important behavioral outcomes. Without this knowledge, effective interventions that support both the patient
and clinician cannot be developed. Our long term goal is to develop the next generation of decision support tools
and alternative communication strategies to support clinicians and patients as they face increasingly complex
cancer screening decisions. Our overall objective in this application is to identify the components of patient-
clinician discussions that contribute to high quality lung cancer screening decisions and subsequent important
behavioral outcomes. Our central hypothesis is that patients who perceive lung cancer screening discussions
with their clinician as shared will have positive decisional and behavioral outcomes. Guided by strong preliminary
data, this hypothesis will be tested by pursuing three specific aims: 1) determine the key components of the SDM
process that predict patient-perceived lung cancer screening decision quality, screening completion among
patients who decide to screen, and stage of readiness for smoking cessation among current smokers; 2) identify
clinician factors that predict patient-perceived lung cancer screening decision quality, screening completion
among patients deciding to screen, and stage of readiness for smoking cessation among current smokers; and
3) identify patient factors that predict patient-perceived lung cancer screening decision quality, screening
completion among patients deciding to screen, and stage of readiness for smoking cessation among current
smokers. We will use a convergent mixed methods design to examine clinician (N=75) and patient (N=550)
factors simultaneously by linking patient and clinician survey data with electronic health record data. We will
examine further the decision process through content analysis of audio-recordings of preventive care visits in
which lung cancer screening is discussed (N=30). This proposal is innovative in that results will exponentially
advance our ability to create the next generation of decision support tools, including alternative communication
strategies, to support patients and clinicians in lung cancer screening. This contribution is significant because
identifying the “active ingredients” in the SDM process will open new research horizons, particularly in health
communication, between clinicians and patients who are navigating complex health-related decisions such as
lung cancer screening. Because of this work, we will also open new paths toward improving health
communication among high-risk, long-term smokers and their clinicians about their health.
Pathways to breast cancer screening artificial intelligence algorithm validation.
, Houssami N.
, Elmore J.G.
, Buist D.S.M.
Breast (Edinburgh, Scotland), 2020 Aug; 52, p. 146-149.