iNUDGE: INtegration of liqUiD Biopsy Based Next Generation Gene sEquencing in Newly Diagnosed NSCLC - A Stepped Wedge Cluster Randomized Clinical Trial
Phase: NA
Conditions: Non Small Cell Lung Cancer Metastatic, Newly Diagnosed NSCLC, Non-Squamous Non-Small Cell Neoplasm of Lung
Sponsor: Charu Aggarwal

iNUDGE: Liquid biopsy based NGS in newly diagnosed NSCLC             
 
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iNUDGE: INtegration of liqUiD biopsy based next generation Gene sEquencing in newly diagnosed 
NSCLC – A stepped wedge cluster randomized clinical trial  
Principal Investigator  
Co-investigators 
Funding Agency: 
IRB Protocol Number: 
UPCC Protocol Number: 
Version Date:  
Charu Aggarwal, MD, MPH 
10-137, South Pavilion 
3400 Civic Center Boulevard 
Philadelphia, PA 19104 
Tel: 215-662-6318 | Fax: 215-349-5326 
Penn Medicine 
Christopher D’ Avella, MD 
E. Paul Wileyto, PhD 
Justin Bekelman, MD 
Katharine Rendle, PhD, MPH 
Melina Marmarelis, MD, MSCE 
Ramy Sedhom, MD 
Samuel Kerr, MD  
Shayma Kazmi, MD 
Loxo Oncology 
852795 
27522 
03/24/2023 
 
 
 

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Table of Contents 
1. 
BACKGROUND AND RATIONALE .............................................................................. 3 
2. 
OBJECTIVES .................................................................................................................... 4 
2.1 
Primary Objectives ...................................................................................................... 4 
2.2 
Primary Outcomes ...................................................................................................... 4 
2.3 
Secondary Outcomes ................................................................................................. 4 
3. STUDY POPULATION ........................................................................................................... 4 
3.1 
Target Population ........................................................................................................ 4 
3.2 
Inclusion Criteria ......................................................................................................... 4 
3.3 Exclusion Criteria ............................................................................................................ 5 
3.4 Vulnerable Populations ................................................................................................. 5 
4. 
STUDY DESIGN................................................................................................................ 5 
4.1 
Preliminary Studies .................................................................................................... 5 
4.2 
Overview ........................................................................................................................ 5 
4.3 
Study Duration and Timeline .................................................................................... 9 
4.4 
Study Setting ................................................................................................................ 9 
5. 
STUDY PROCEDURES ................................................................................................. 10 
5.1 
Recruitment and Retention ..................................................................................... 10 
5.2 
Informed Consent ...................................................................................................... 10 
5.3 
Measures and Outcomes ........................................................................................ 11 
5.4 
Sources of Materials ................................................................................................. 11 
6. 
STATISTICAL DESIGN AND POWER ....................................................................... 12 
6.1 
Sample Size ................................................................................................................ 12 
6.2 
Analysis Plan .............................................................................................................. 12 
7. 
RESOURCES NECESSARY FOR HUMAN RESEARCH PROTECTION ............ 15 
8. 
STUDY TEAM.................................................................................................................. 15 
9. 
REFERENCES ................................................................................................................ 16 
10. 
APPENDICES.................................................................................................................. 18 
 
 

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1. BACKGROUND AND RATIONALE 
The development of targeted therapies has changed the treatment paradigm for non–small cell 
lung cancer (NSCLC). With the growing number of FDA approved targeted therapies, current 
NCCN guidelines recommend comprehensive molecular genotyping, defined as detection of 
mutations in seven genes (EGFR, ALK, BRAF, ROS1, MET, RET, and NTRK) prior to first line 
(1L) therapy for all newly diagnosed patients with metastatic non-squamous (mNSq) NSCLC to 
enable the delivery of personalized therapy.1, 2 Furthermore, the emergence of immune-
checkpoint inhibitors has amplified the importance of molecular genotyping in the care of these 
patients because patients with actionable genomic alterations rarely respond to immunotherapy, 
even in the presence of high PD-L1 expression and should be preferentially treated with 
targeted therapy.3 In addition, there is a growing body of evidence that introduction of targeted 
tyrosine kinase inhibitors after immunotherapy may be associated with higher rates of immune 
related adverse events, even after discontinuation of immunotherapy.4 Additionally, in previous 
studies, amongst patients with a mutation in a NCCN-listed gene, exposure to targeted therapy 
has been shown to be associated with improved overall survival.5 Given these considerations, 
upfront tumor genotyping is now considered an essential step in guiding treatment decisions for 
all patients with mNSq NSCLC, prior to 1L therapy.  
Despite the critical importance of molecular testing in patients with advanced NSCLC, numerous 
barriers impede timely completion of testing prior to initiation of 1L systemic therapy.6-8 Common 
issues include insufficient tissue for testing, lack of infrastructure for obtaining and sending 
biopsy samples for testing, and unacceptably long turnaround times for results.9 These issues 
have created a critical need for additional convenient, and minimally invasive options for tumor 
genotyping.10, 11 We and others have previously demonstrated that the incorporation of 
concurrent plasma based next-generation gene sequencing (NGS), ordered at the same time as 
tissue NGS, improves detection of clinically actionable mutations in patients with advanced 
NSCLC.11  
At our institution, we piloted a behavioral economics (BE) informed “nudge” strategy to guide 
physicians’ clinical practice to include concurrent use of plasma and tissue-based NGS at initial 
diagnosis. This real-world cohort study was conducted at the Abramson Cancer Center and 2 
community sites within UPHS. Across the 3 practice sites, a provider team-focused Electronic 
Health Record (EHR) -based “nudge intervention” was designed to order plasma-based NGS at 
the time of new patient consultation. Eligible patients for the nudge were identified using an 
EHR based checklist, that included 3 criteria: i. new diagnosis, ii. treatment naïve, iii. mNSq 
NSCLC. Results from the intervention period (4/2021-12/2021) were compared to baseline data 
from similar patients treated at our institution between 01/2019 and 03/2021. Of the 526 patients 
with mNSq NSCLC that were included in the analysis: 381 were included in the pre-intervention 
cohort and 145 in the post-intervention cohort. After implementation of the EHR-based nudge, 
we observed that a higher proportion of patients underwent concurrent tissue + plasma testing 
in the post intervention cohort compared to pre-intervention 90.3% (131/145) vs. 68.8% 
(262/381), p<0.00001. Additionally, by virtue of having robust tissue + plasma testing 
performed, there were improved rates of comprehensive molecular genotyping in the post-
intervention cohort compared to pre-intervention, 98.6% (143/145) vs. 87.1% (332/381), 
p=0.00007. A greater proportion of patients had comprehensive genotyping available prior to 
1st-line therapy in the post-intervention vs. pre-intervention cohort (86.2% vs. 76.3%, p=0.013).  
These findings demonstrated that behavioral, EHR-based nudges are feasible and can promote 
guideline concordant diagnostic testing at both community and academic sites. The overarching 
goal of this current trial is to expand the application of this BE informed nudge approach, which 
has been operationalized within Epic, the EHR used at UPHS, to six satellite hospitals. Our 

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central hypothesis is that this approach will dramatically increase adoption of comprehensive 
molecular testing and enhance the delivery of molecularly informed first-line therapy in patients 
with newly diagnosed metastatic non-squamous NSCLC. Molecular testing will be defined as i) 
comprehensive: EGFR, ALK, BRAF, ROS1, MET, RET, and NTRK testing, ii) incomplete: <6 
genes tested, and iii) no testing performed. Clinically actionable mutations will be defined as an 
alteration in one of the seven genes on the comprehensive gene list with an FDA approved 
targeted therapy in the 1L setting, plus KRAS G12C, EGFR exon 20 insertion, and ErbB2 
mutations. Molecularly informed first line therapy will be defined as one that is informed by 
results of NGS, obtained by plasma, tissue or both. 
 
2. OBJECTIVES 
2.1 Primary Objectives 
Objective 1: In a stepped wedge cluster randomized trial of patients with newly diagnosed 
metastatic NSCLC, test the effectiveness of a BE informed EHR nudge intervention to increase 
timely receipt of comprehensive molecular test results before 1L therapy by incorporating 
concurrent tissue and plasma-based molecular testing into the workup of newly diagnosed 
patients. 
Objective 2: Evaluate contextual mechanisms contributing to the adoption, reach, and 
effectiveness of EHR nudge interventions with a lens for health equity. 
2.2 Primary Outcomes 
Objective 1: Availability of comprehensive molecular test results (as defined above) prior to first 
line therapy for patients with mNSq NSCLC. 
 
2.3 Secondary Outcomes 
Objective 1: 1) successful EHR based nudge delivery, 2) turnaround time of delivery of provider 
focused alerts after receipt of plasma genotyping results, 3) completion of comprehensive 
molecular testing (tissue and/or plasma testing), 4) reasons for failure to complete 
comprehensive molecular testing (QNS or other), 5) time to molecularly-informed treatment 
initiation, 6) type of therapy received (targeted therapy, chemo-immunotherapy, immunotherapy, 
clinical trial or none) and 7) overall survival. 
Objective 2: Individual and contextual factors shaping adoption (provider-level ordering of 
molecular testing), reach (patient-level completion of molecular testing), and effectiveness 
(receipt of molecular testing), of interventions, guided by RE-AIM with Equity Extension 
Framework12. 
 
3. STUDY POPULATION 
3.1 Target Population 
This stepped wedge cluster randomized trial will be conducted across newly diagnosed patients 
with mNSq NSCLC treated at Penn Medicine that comprise 3 clusters (sites): 1) Lancaster 
General Hospital (LGH), 2) Penn – New Jersey (Princeton Medical Center (PMC), Penn 
Medicine at Cherry Hill (PMCH), Penn Medicine at Washington Township (PMWT), and Penn 
Medicine Voorhees (PMV)), and 3) Penn Presbyterian Medical Center (PPMC). 
 
3.2 Inclusion Criteria 
a) Patients with histological, or cytological diagnosis of mNSq NSCLC who have not yet 
received systemic treatment for metastatic disease.  
b) Patients must be seen at LGH, PMC, PPMC, PMCH, PMWT, or PMV for mNSq NSCLC.  
 

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3.3 Exclusion Criteria 
a) Incomplete staging information. 
 
3.4 Vulnerable Populations 
a) Children, pregnant women, fetuses, neonates, or prisoners are not included in this 
research study. 
 
4. STUDY DESIGN 
4.1 Preliminary Studies 
We have conducted two prior studies that inform the design of this trial. In the first, we evaluated 
the impact of plasma-based molecular testing in addition to tissue testing on the detection of 
actionable mutations in patients with metastatic NSCLC. In 229 patients who underwent 
concurrent plasma and tissue molecular testing, tissue alone detected targetable mutations in 
21% of patients, whereas addition of plasma testing increased targetable mutation detection to 
36%.11 Thus, plasma-based testing increased the rate of detection of therapeutically targetable 
alterations in metastatic NSCLC when used concurrently with tissue testing. In a second study, 
initiation of plasma-based testing, based on a BE informed EHR based nudge at time of new 
patient evaluation increased the proportion of patients undergoing concurrent tissue + plasma 
NGS testing to 90.3% (131/145) vs. 68.8% (262/381), p<0.00001(Aggarwal C et al, ASCO 
Quality Care Symposium, 2022). Thus, the proposed stepped wedge cluster randomized trial 
will examine effectiveness of this approach in a larger proportion of patients, reduce disparities 
in molecular testing, and target testing more optimally. 
 
4.2 Overview 
Objective 1: In a stepped wedge cluster randomized trial of patients with newly diagnosed 
mNSq NSCLC, test the effectiveness of BE informed EHR nudge intervention to increase timely 
receipt of comprehensive molecular test results before 1L therapy by integration of concurrent 
tissue and plasma molecular testing. 
 
The design of this trial will include 3 clusters, representing the 6 community hospitals. There will 
be an initial period in which no clusters are exposed to the intervention. Subsequently, at regular 
intervals (the “steps”) one cluster (or a group of clusters) will be randomized to cross from the 
control to the intervention under evaluation. This process will continue until all clusters have 
crossed over to be exposed to the intervention. At the end of the study there will be a period 
when all clusters are exposed. Data collection will continue throughout the study, so that each 
cluster will contribute observations under both control and intervention observation periods. 
 
Intervention: 
An EHR-based nudge intervention that allows for default placement of a plasma based 
molecular genotyping order at time of the first new patient visit will be implemented (Figure 1). 
Subsequently, results detected on the default plasma NGS order will be conveyed to providers 
in the form of an electronic clinical decision support notification (Figure 3). The intervention will 
be tailored to the organizational needs of each cluster.  
 

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Figure 1. EHR based nudge intervention workflow. 
1. The EHR-based nudge intervention will fire at the time of the first telephone encounter 
with a new patient coordinator (NPC) based on a set of pre-populated molecular 
questions (Supplemental eFigure 1) for all patients with a new diagnosis of mNSq 
NSCLC.  
2. The EHR-based nudge intervention will appear when the visit is opened within the 
electronic medical record by a provider and will allow default placement of a plasma-
based NGS order. Thoracic oncology providers can opt out of this order if they feel it is 
not appropriate for the patient or because other molecular testing has already been 
initiated/completed. Large gene panel (>50 genes) based plasma will be used, and be 
based on site preference.  
Standardization of the process will include availability of plasma kits at each of the sites (LGH, 
PMC, PPMC, PMCH, PMWT, and PMV) and clinical labs, and communication of the ordering 
process will be conveyed with the respective medical support teams (APP, RN) (Figure 2). 
Sites will be encouraged to choose one plasma-based assay to be used at their site in order to 
streamline order design.  
Figure 2. Current and proposed future workflow for plasma-NGS ordering. 
 
 
As part of the downstream EHR-based nudge intervention workflow, an electronic clinical 
decision support (e-CDS) system for alterations detected on plasma genotyping will be created 
and implemented into the EHR as a “Research (non-chargeable) Encounter” to alert the 
provider team caring for the patient (Supplemental eFigure 2). This support program will be 
created to notify clinicians of targetable mutations, as well as absence of mutations detected on 
plasma testing as a means of improving the timely delivery of molecularly informed therapy. 
 

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Figure 3: Screening of plasma NGS reports and creation of e-CDS.  
 
1. The study team will review plasma NGS reports for therapeutically targetable alterations 
(based on NCCN recommended biomarkers including EGFR, ALK, ROS1, RET, MET, 
BRAF, KRAS, ErbB2 and NTRK) (Table 1). When identified, the project manager (PM) 
will alert the internal review team (Drs. Aggarwal and Marmarelis).  
2. If deemed appropriate the PM will assemble and send an EHR-based reflex alert with 
the information included in Figure 3 to the patient’s oncologist and associated APP. This 
alert will be created by the internal review team and will include information about the 
possible therapeutic options for this alteration including available clinical trials. 
3. To ensure that providers are reaching the most appropriate molecularly informed 
treatment decision, e-CDS alerts will be sent to providers even for mutations deemed not 
“therapeutically targetable” (i.e., STK11, TP53, etc.). 
4. Provider response to the e-CDS program, plan to prescribe targeted therapy as well as 
prescription of targeted therapy in response to a molecular alteration will be monitored to 
determine provider engagement. Reasons for not prescribing targeted therapy will also 
be recorded.  
Table 1. 
Gene 
Mutations 
Targeted Therapies 
EGFR 
Exon 19 del/ Exon 21 L858R 
Osimertinib 
EGFR 
Exon 18del/ins, E709A, G719A, G719C, 
G719R, G719S, Exon19del, Exon20Ins, 
T790M, S768I, C797S, L858R, L861Q 
Afatinib, Erlotinib, Gefitinib, Osimertinib, 
Dacomitinib, Amivantamab 
ALK 
EML-ALK fusion, F1174L, G1123S, G1202R, 
I1171S, I1171T, L1196Q 
Alectinib, Brigatinib, Ceritinib, Crizotinib, 
Lorlatinib 
ROS1 
Fusions 
Brigatinib, Ceritinib, Crizotinib, Entrectinib, 
Lorlatinib 
RET 
RET-KIF5B Fusion 
Pralsetinib, Selpercatinib  

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RET fusions with CCDC6, NCOA, TRIM33, 
CUX1, KIAA1217, FRMD4A, KIAA1468  
MET 
Exon 14 skipping mutation 
Crizotinib, Capmatinib, Cabozantinib 
BRAF 
V600E, V600 
Dabrafenib, Dabrafenib/Trametinib, 
Vemurafenib 
KRAS 
G12C 
Clinical Trials 
ErbB2 
Exon20Ins 
Trastuzumab-deruxtecan, Ado-trastuzumab, 
emtansine, Afatinib, Lapatinib, Neratinib 
NTRK 
NTRK 1,2,3 fusions 
Larotrectinib, Entrectinib 
 
Objective 2: Evaluate contextual mechanisms contributing to the adoption, reach, and 
effectiveness of EHR nudge interventions, with a lens for health equity. 
Using rigorous approaches proven successful in our prior work13, we will recruit 10-15 patient 
and clinician participants from each site (estimated 40-60 participants total) to complete semi-
structured interviews (Supplemental eFigure 3) following the active trial period. The goal of this 
objective is to understand contextual mechanisms (e.g., patient, clinician, clinic, structural 
factors) shaping adoption, reach, and effectiveness of each intervention and identify how 
response may differ by race and ethnicity, socioeconomic status, and other key social 
determinants of health. These data will be analyzed using qualitative comparative analysis, a 
mixed method approach well suited to identify mechanisms in pragmatic trials with smaller 
sample sizes. 
 
Patients: We will oversample (at least 50% of the sample at each site) for Black patients and 
patients living in impoverished neighborhoods to understand effectiveness by race/ethnicity and 
socioeconomic status and stratify by reach (completion of comprehensive testing) and site to 
understand factors contributing to both success and failure (e.g., primary outcome). Trial 
participants will be invited in randomly selected batches following active trial engagement (to 
enhance capture over time) until we reach our target sample of approximately 30-40 patients. 
Patients will be invited to participate in interviews via email and/or letter within 6 weeks of their 
return oncology visit. 
 
Clinicians: Recruitment for clinicians will be similar to the procedure for patients, except we will 
wait until the completion of the full trial to conduct interviews to avoid potential contamination. 
Clinicians will be purposively sampled by adoption (low or high levels of ordering for molecular 
testing) and clinical role (e.g., oncologists, nurses, clinical leads) to enhance variation and 
invited via email. Recruitment will continue until we reach our target sample of 10-20 clinicians. 
 
 
 
 
 
 

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4.3 Study Duration and Timeline 
The study duration will be approximately 36 months.  
 
Objective 1: Following an observation period, in month 6, we will initiate the stepped wedge 
cluster randomized trial. Each cluster will include a 5-month observation period, and a 1-month 
washout. The active enrollment period will be 24 months, with a 6-month lookback for baseline 
period observation. Longitudinal data capture to collect secondary outcome information for 
patients will be completed up to 90 days post-initial oncology visit Thus, collection and 
verification of all study endpoints will be completed by the end of year 3.  
 
 
Objective 2: For patient interviews, we will use rolling enrollment to capture variation in 
processes and effectiveness overtime. Patient interview recruitment and data collection will 
begin during the first month of intervention observation (i.e., second month of after 
implementation to account for washout period and continue until saturation if reached. Clinician 
recruitment and data collection will start after active trial observation is completed at the specific 
site. Mixed methods analysis (including transcription and coding) will run throughout Years 2-3. 
 
4.4 Study Setting 
This study will occur within the University of Pennsylvania Health System. 
 
Trial Timeline 
Project Timeline 
Year 1 
Year 2 
Year 3 
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 
Protocol Development, IRB  
• 
 
 
 
 
Objective1: Deliver 
intervention 
• 
• 
• 
• 
• 
• 
• 
• 
• 
 
 
Objective 1: Endpoint 
ascertainment 
 
• 
• 
• 
• 
• 
• 
• 
• 
• 
 
 
Objective 1: Endpoint analysis 
• 
• 
• 
 
Objective 2: Recruit & conduct 
patient interviews 
• 
• 
• 
• 
• 
• 
• 
• 
• 
 
 
Objective 2: Recruit & conduct 
clinician interviews 
• 
• 
• 
• 
• 
• 
• 
• 
Objective 2: Mixed methods 
coding & analysis 
• 
• 
• 
• 
• 
• 
• 
• 
Baseline Analysis and 
disseminate results 
 
 
• 
 
• 
 
• 
 
• 
 
 
 
Submit manuscripts & 
disseminate overall results 
• 
• 
• 
• 

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5. STUDY PROCEDURES 
5.1 Recruitment and Retention 
Objective 1: A waiver of informed consent is requested for the stepped wedge cluster 
randomized trial. The study evaluates molecular testing rates before and after intervention at 
each site. Molecular testing at the initial diagnosis of mNSq NSCLC is standard, therefore, 
physicians and their patients will not be consented as this is the standard of practice.  
Objective 2: This portion of the study will utilize prospective consent with waiver of written 
documentation. An estimated 30-40 patients and 10-20 clinicians will be interviewed (sample 
size dependent upon when data saturation is reached). Interview participants will also complete 
a structured questionnaire at the time of the interview. The final interview guide and 
questionnaire will be submitted for review prior to commencement of Objective 2.  
Patients: A sample of patients will be invited to participate in the semi-structured interview and 
survey via email and/or letter within 6-weeks of in-person visit. Participants will be invited in 
randomly selected batches (estimate approximately 5 per month to enhance capture over time) 
until we reach our target sample of approximately 30 patients. We will oversample for Black 
patients and patients living in low-income neighborhoods (at least 50% of the sample at each 
site) to understand effectiveness by key social determinants of health and by reach (patient 
completion of testing) and site to understand factors contributing to both success and failure the 
interventions (5-10 patients at each site). 
 
Clinicians: Recruitment of clinicians will be similar to that of patients, except we will wait until 
end of active observation at each site to avoid potential contamination. Clinicians will be 
purposively sampled by adoption (low or high levels of ordering for molecular testing) and 
clinical role (e.g., oncologists, nurses, clinical leads) to enhance variation and invited via email. 
Recruitment will continue until we reach our target sample of 5-10 clinician (per site) or until 
data saturation is reached.  
 
Data Collection: Email and/or letter invitations will be followed by a phone call from research 
staff to assess interest and schedule interviews with all interested participants. Interviews will be 
conducted by the qualitative data analyst or trained staff at Penn’s Mixed Methods Research 
Lab (MMRL), a service center that provides expertise and support in qualitative and mixed 
methods data collection, data management, and data analysis. Interviews will be conducted in-
person, by phone or using a HIPAA-compliant video platform, depending on participant 
preference. Structured questionnaire data will be collected via REDCap, a HIPAA compliant 
survey platform, or verbally administered if the participant cannot access this platform. 
 
5.2 Informed Consent 
Objective 1: This study will employ a waiver of consent mechanism. 
 
Objective 2: Potential interview participants will be initially contacted by study team members by 
patient portal, email, and/or letter and given the option to decline further contact from the team. 
If the participant has not opted out within two weeks, they will be contacted by phone to assess 
interest in participating in the study. If the participant agrees to participate, they will be 
scheduled to have an interview via telephone or in person (based on preference and study 
procedures at the time). For this portion of the study, a waiver of documentation of written 
informed consent will be used because the risk to the individual is minimal, a signed informed 
consent form (ICF) could identify that an individual participated in the study and obtaining a 
signed paper form would significantly decrease the likelihood of proceeding towards an 
interview. Prior to the start of the interview, research staff will review study purpose, procedures, 

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and the rights of the participant. They will also provide an information statement to participants 
via email prior to the scheduled interview. Research staff will state that participation is voluntary 
and ask the participant’s permission to record their interview. They will describe the transcription 
and de-identification process, and they will ask permission to proceed with the interview. All 
participants will be free to withdraw participation at any time, and study enrollment will not 
impact employment or care at Penn Medicine. Prior to commencement of interviews, we will 
submit all relevant recruitment materials (e.g., telephone script and recruitment letter), the 
interview guide, and the information statement for IRB review and approval. Objective 2 
(interviews) activities will not commence prior to approval of these documents. 
5.3 Measures and Outcomes 
Objective 1: The primary endpoint is receipt of comprehensive molecular test results prior to 1L 
therapy for patients with mNSq NSCLC. This outcome encompasses successful completion of 
concurrent tissue and plasma based molecular testing and the ability of the patient and 
oncology care team to have all necessary information to collaboratively arrive at the optimal 
treatment approach. We anticipate that approximately 80% of patients in the interventional arm 
will have molecular test results available prior to initiation of first line therapy. The primary 
outcome will be assessed by review of clinician documentation (e.g., progress notes) within the 
electronic medical record (EHR). Baseline data will be collected from all 4 sites. Molecular 
testing rates will be assessed, proportion of patients that undergo complete molecular 
genotyping prior to start of 1L therapy for mNSq NSCLC will be tabulated (Comprehensive 
testing will be defined as testing of all NCCN recommended biomarkers). Proportion of patients 
receiving targeted therapies when therapeutically targetable alterations are detected (Table 1) 
will be tabulated on a quarterly basis.  
Secondary outcomes include: 1) successful EHR based nudge delivery, 2) turnaround time of 
delivery of provider focused alerts after receipt of plasma genotyping results, 3) completion of 
comprehensive molecular testing (tissue and/or plasma testing), 4) reasons for failure to 
complete comprehensive molecular testing (QNS or other), 5) time to molecularly-informed 
treatment initiation, 6) type of therapy received (targeted therapy, chemo-immunotherapy, 
immunotherapy, clinical trial or none) and 7) overall survival at 1 year, and 2 years. 
 
Objective 2: We will use structured and validated measures and develop a semi-structured 
interview guide. The interview guide will be developed using RE-AIM with Equity Extension 
Framework, a widely used implementation science framework that measures key 
implementation outcomes (reach, effectiveness, adoption, implementation, and maintenance) 
and monitors how these outcomes vary by key determinants of health (equity). We will also 
draw upon the Consolidated Framework for Implementation Research to assess multilevel 
determinants that may shape implementation success. For patients, we will also assess factors 
that we hypothesize will impact completion of patient testing (e.g., patient knowledge of 
molecular testing, perceived importance of molecular testing, barriers to testing) and 
sociodemographics (e.g., self-reported race/ethnicity, health literacy, insurance, medical 
mistrust) using structured and validated items when available. A full interview guide and 
questionnaire will be submitted for IRB review prior to commencement of interviews. 
 
5.4 Sources of Materials 
Objective 1: Electronic health record (EHR) data will be used to collect the primary endpoint as 
well as covariates required for statistical analysis.  
 
Objective 2: Semi-structured interviews and structured questionnaire data will be collected and 
analyzed in conjunction with quantitative measures conducted. 

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6. STATISTICAL DESIGN AND POWER 
6.1 Sample Size 
Objective 1: We have calculated sample size based on estimates of completion of 
comprehensive molecular testing prior to initiation of first line therapy. Based on our prior 
studies, we anticipate that the baseline rate of comprehensive molecular testing prior to first line 
therapy is 60%. In this stepped wedge cluster randomized trial, we wish to detect an absolute 
increase of 20% in our primary outcome for patients in the intervention arm.  
 
A sample of 3 clusters in a complete stepped-wedge cluster-randomized design with 4 time periods 
(including the baseline), 3 steps, 1 cluster(s) switching from control to treatment at each step, and an 
average of 120 subjects per cluster with an average of 30 subjects per cluster per time period (for a total 
sample size of 360 subjects) achieves 80% power to detect a difference between proportions of 0.21701. 
The treatment proportion is assumed to be 0.81701 under the alternative hypothesis. The control 
proportion is 0.6. The test statistic used is the two-sided Wald Z-Test. The ICC is 0, and the significance 
level of the test is 0.05. 
 
  
Objective 2: Proposed sample size is based on the estimated number of interviews needed to 
reach data saturation within each group and by intervention outcome to support mixed methods 
evaluation; however, interviews will continue until saturation is achieved. 
 
6.2 Analysis Plan 
 
Objective 1 Primary Analyses Outcome Measures: Relative and absolute change in 
availability of molecular testing prior to IL therapy. The change will be calculated from baseline 
pre-intervention period to intervention periods in all the intervention arms. Comprehensive 
molecular testing will be defined as comprehensive if results for EGFR, ALK, BRAF, ROS1, 
MET, RET, and NTRK testing are available from plasma, tissue, or both.  
 
Primary Analyses Statistical Plan 
The primary outcome is binary and will be analyzed using logistic regression, fitted using 
Generalized Estimating Equations (GEE). The model will include a time varying covariate to 
represent pre-treatment, washout, and treatment, within each randomized cluster, and an 
ordinal categorical variable to represent time. The GEE model will adjust variances for 
correlation within institution (cluster). The primary hypothesis will be tested using the z-score 
corresponding to the difference between treatment and pretreatment proportions (after 
adjustment for time effects).    
Objective 1 Secondary Analyses Outcome Measures: 
1. Successful EHR based nudge delivery:  
a. Amongst eligible patients (see eligibility above), calculate the proportion of 
patients for whom the EHR nudge fired successfully (yes/no). Applicable for the 
patients enrolled in the time periods following randomization. 
 
2. Turnaround time of delivery of provider focused alerts: 
a. Reported as number of days, median. Applicable for the patients enrolled in the 
time periods following randomization. 
 
3. Completion of comprehensive molecular testing: 

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a. Amongst eligible patients, relative and absolute change in completion of 
comprehensive molecular testing will be tabulated, regardless of timing of 1L 
therapy.  
b. Relative and absolute change in completion of comprehensive testing by tissue 
and plasma, plasma alone, or tissue alone will be tabulated. 
 
4. Reasons for failure to complete comprehensive molecular testing: 
a. Summarize reasons for failure of completion of testing 
i. Tissue related (QNS) 
ii. Patient related factors (unable to biopsy, patient declined biopsy etc) 
iii. Assay related factors (plasma assay does not detect mutations) 
iv. Other 
5.  Time to molecularly informed treatment initiation: 
a. Amongst eligible patients, relative and absolute change in time to start 1L 
therapy.  
i. Calculated as time to therapy from the date of diagnosis of Stage IV 
disease (date of biopsy) 
ii. Calculated as time to therapy from the date of first new patient visit with 
medical oncology 
 
6.  Type of therapy received: 
a. Targeted therapy 
b. Chemo-immunotherapy 
c. Immunotherapy 
d. Clinical trial or n 
e. None 
 
7. Overall Survival: 
a. Time from initial diagnosis to date of death or last follow up. 
b. 1 year and 2-year overall survival rates will be calculated for the intervention 
group, and compared to baseline. 
 
Secondary Analyses Statistical Plan 
Secondary outcomes will be summarized by time and treatment condition, as proportions, 
means, medians, as appropriate with two-sided 95% CIs. Successful nudge delivery, completion 
of testing, reasons for failure to complete testing, and type of therapy received will be tabulated 
and summarized as proportions. Turnaround time of delivery for provider focused alerts will be 
treated as time to event and summarized as median time with 95% CI. Time to treatment 
initiation and Overall Survival will be summarized as time to event using Kaplan Meier methods, 
with the effect of intervention estimated as the hazard ratio (with two-sided 95% CI). 
For a complete list of clinical and interventional related variables that will be collected 
throughout the course of this study please refer to Supplemental eTable 1 below.  

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Objective 2 Analyses Plan 
We will descriptively assess survey data 
and thematically code interview data. 
These data will be used to conduct 
qualitative comparative analysis (QCA) to 
identify how contextual factors shaped both 
timelines and quality of care and the 
effectiveness of nudge intervention. QCA is 
a multistep analytic method that combines 
qualitative and quantitative coding and 
calibration to identify conditions shaping 
the effectiveness of EHR based nudge 
intervention strategies. The four-step 
process will use survey and interview data 
as inputs or “conditions.”  
 
Code Contextual Data (Step 1-2). We will 
use convergent mixed methods analysis to code contextual conditions (inner setting, outer 
setting, and individual characteristics) and implementation conditions (characteristics of specific 
EHR strategy and process).  
 
Qualitative Data. The constant comparative method, guided by modified grounded theory,14 will 
be used to iteratively identify a priori domains of interest (guided by RE-AIM and CFIR) and to 
inductively explore emergent themes. Two trained coders will first independently read through 
each transcript to identify themes within each domain. We then will use this list to develop a 
coding dictionary and apply it to subset of the data. We will measure inter-rater reliability to 
document and improve coding consistency. Once high reliability is achieved (kappa >0.8), we 
will apply the full coding dictionary to the interview data using qualitative software, and produce 
thematic reports summarizing our findings. We will then use qualitative data to expand upon and 
triangulate quantitative patterns identified in trial and surveys.15 
 
Survey Data will be analyzed descriptively and coded dichotomously or categorically as 
appropriate. Contextual data will serve as QCA inputs to identify necessary and sufficient 
conditions for telehealth success.  
 
Qualitative Comparative Analysis (QCA) (Steps 3-4). For QCA, our primary endpoint will be 
completion of comprehensive molecular testing prior to initiation of first line therapy, and 
success or failure will be determined at the patient-level. Each case will be calibrated as having 
or not having the primary outcome or condition (described above).16 Thresholds for coding 
primary outcomes and the presence or absence of each condition (e.g., low quality 
communication) will be determined based on existing literature or stakeholder consensus.17,18 
Most outcomes will be dichotomous, but continuous values and fuzzy set QCA will be used as 
appropriate.19,20 Data (“truth”) tables will be created for analysis, which list all possible 
configurations of conditions, the number of cases that fall into each configuration, and the 
consistency of the cases—or the proportion of cases in the specific configuration that have the 
desired outcome.20, 21 We will conduct QCA analyses using R package QCApro.22–24 Raw and 
unique coverage will be calculated and consistency will be set at 80% for sufficient and 90% for 
necessary conditions. This iterative analytic process will identify what conditions—alone or in 
combination with others—are necessary or sufficient to yield the primary endpoint. 

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7. RESOURCES NECESSARY FOR HUMAN RESEARCH PROTECTION 
Adequate facilities are available within Penn Medicine’s Clinical Practice Network. Members of 
the research team, listed in HSERA, will be overseen by the PI and include appropriate 
personnel to successfully implement this pilot project. All personnel will complete required 
training before being granted access to any identifying information. Training includes information 
on confidentiality through the Collaborative IRB Training Initiative (CITI) courses. All personnel 
will also be trained in procedures for reporting unintentional breaches in confidentiality to the PI. 
All personnel will be aware that violations of participant’s confidentiality, either unintentional or 
deliberate, may result in termination of hire.  
 
Protection of Human Subjects 
Computer-based files will only be made available to personnel involved in the study through the 
use of access privileges and passwords. Wherever feasible, identifiers will be removed from 
study-related information. Precautions are already in place to ensure the data are secure by 
using passwords and HIPAA-compliant encryption. Data on physicians and patients will be 
obtained from EHR and Penn Data Store. Any information that is obtained will be used only for 
research purposes and to inform the interventions described above. Information on individual 
patients will only be disclosed within the study team. All study staff will be reminded of the 
confidential nature of the data collected and contained in these databases. Data will be stored, 
managed, and analyzed on a secure, encrypted server behind the University of Pennsylvania 
Health System (UPHS) firewall. Data access will be password protected. Whenever possible, 
data will be de-identified for analysis. 
 
 
8. STUDY TEAM 
Our interdisciplinary team includes investigators with world known experts in thoracic oncology, 
and clinical implementation of molecular testing, implementation science, behavioral economics, 
EHR-based strategies, and mixed-methods research. At the University of Pennsylvania, the 
work will be led by Charu Aggarwal, MD, MPH, Leslye M. Heisler Associate Professor of Lung 
Cancer Excellence and Associate Director, Penn Center for Precision Medicine and Justin 
Bekelman, MD, Professor of Radiation Oncology, Medicine, and Health Policy and Director of 
PC3I and an international leader in cancer care innovation. Other key investigators include 
Melina E. Marmarelis, MD, a medical oncologist with clinical and research expertise in lung 
cancer. Other co-investigators include E. Paul Wilyeto, PhD (biostatistician), and Katharine 
Rendle, PhD, MSW, MPH who bring statistical and implementation science expertise to the 
team. Local team leaders will be Chris D. Avella, MD at Penn Presbyterian Medical Center, 
Ramy Sedhom, MD at Penn-Princeton, Shayma Kazmi, MD at Penn-Cherry Hill and Penn-
Washington Township, and Samuel Kerr, MD at Penn-LGH.  The study team will include Peter 
Gabriel, MD, Chief Oncology Informatics Officer at the Abramson Cancer Center, Meagan 
Hume, MDP, Innovation Manager at PC3I, Anthony Martella, BA, Clinical Research 
Coordinator B, PC3I, and Dylan Scholes, BS, Clinical Research Coordinator B, Clinical 
Research Unit.  
 
 
 

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9. REFERENCES 
1. Aggarwal C, Rolfo CD, Oxnard GR, Gray JE, Sholl LM, Gandara DR. Strategies for the 
successful implementation of plasma-based NSCLC genotyping in clinical practice. Nat Rev Clin 
Oncol 2020. 
2. Rolfo C, Mack P, Scagliotti GV, et al. Liquid Biopsy for Advanced NSCLC: A Consensus 
Statement From the International Association for the Study of Lung Cancer. J Thorac Oncol 
2021;16(10): 1647-62. 
3. Lisberg A, Cummings A, Goldman JW, et al. A Phase II Study of Pembrolizumab in EGFR-
Mutant, PD-L1+, Tyrosine Kinase Inhibitor Naive Patients With Advanced NSCLC. J Thorac 
Oncol 2018;13(8): 1138-45. 
4. Schoenfeld AJ, Arbour KC, Rizvi H, et al. Severe immune-related adverse events are 
common with sequential PD-(L)1 blockade and osimertinib. Ann Oncol 2019;30(5): 839-44. 
5. Singal G, Miller PG, Agarwala V, et al. Association of Patient Characteristics and Tumor 
Genomics With Clinical Outcomes Among Patients With Non-Small Cell Lung Cancer Using a 
Clinicogenomic Database. JAMA 2019;321(14): 1391-99. 
6. Robert NJ, Espirito JL, Chen L, et al. Biomarker testing and tissue journey among patients 
with metastatic non-small cell lung cancer receiving first-line therapy in The US Oncology 
Network. Lung Cancer 2022;166: 197-204. 
7. Bruno DS, et al. Racial disparities in biomarker testing and clinical trial enrollment in non-
small cell lung cancer (NSCLC). . Journal of Clinical Oncology 2021;39(no. 15_suppl (May 20, 
2021) ): 9005-05. 
8. Presley CJ, Tang D, Soulos PR, et al. Association of Broad-Based Genomic Sequencing With 
Survival Among Patients With Advanced Non-Small Cell Lung Cancer in the Community 
Oncology Setting. JAMA 2018;320(5): 469-77. 
9. Thompson JC, Yee SS, Troxel AB, et al. Detection of Therapeutically Targetable Driver and 
Resistance Mutations in Lung Cancer Patients by Next-Generation Sequencing of Cell-Free 
Circulating Tumor DNA. Clin Cancer Res 2016;22(23): 5772-82. 
10. Leighl NB, Page RD, Raymond VM, et al. Clinical Utility of Comprehensive Cell-free DNA 
Analysis to Identify Genomic Biomarkers in Patients with Newly Diagnosed Metastatic Non-
small Cell Lung Cancer. Clin Cancer Res 2019;25(15): 4691-700. 
11. Aggarwal C, Thompson JC, Black TA, et al. Clinical Implications of Plasma-Based 
Genotyping With the Delivery of Personalized Therapy in Metastatic Non-Small Cell Lung 
Cancer. JAMA Oncol 2019;5(2): 173-80. 

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12. Shelton RC, Chambers DA, Glasgow RE. An Extension of RE-AIM to Enhance 
Sustainability: Addressing Dynamic Context and Promoting Health Equity Over Time. Front 
Public Health 2020;8: 134. 
13. Rendle KA, Abramson CM, Garrett SB, Halley MC, Dohan D. Beyond exploratory: a tailored 
framework for designing and assessing qualitative health research. BMJ Open 2019;9(8): 
e030123. 
14. Glaser BG, Strauss AL. The Discovery of Grounded Theory: Strategies for Qualitative 
Research. Aldine Pub Co; 1967. 
15. Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. 3rd ed. 
SAGE Publications, Inc; 2018. 
16. Rihoux B, Ragin C. Configurational Comparative Methods. SAGE Publications; 2008. 
17. Kane H, Lewis MA, Williams PA, Kahwati LC. Using qualitative comparative analysis to 
understand and quantify translation and implementation. Transl Behav Med. 2014;4(2):201-208. 
doi:10.1007/s13142-014-0251-6 
18. Weiner BJ, Jacobs SR, Minasian LM, Good MJ. Organizational designs for achieving high 
treatment trial enrollment: a fuzzy-set analysis of the community clinical oncology program. J 
Oncol Pract. 2012;8(5):287-291. doi:10.1200/JOP.2011.000507 
19. Baumgartner M, Ambühl M. Causal modeling with multi-value and fuzzy-set Coincidence 
Analysis. Polit Sci Res Methods. 2020;8(3). doi:10.1017/psrm.2018.45 
20. Ragin CC. Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago; 
2008. 
21. Whitaker RG, Sperber N, Baumgartner M, et al. Coincidence analysis: a new method for 
causal inference in implementation science. Implement Sci. 2020;15(1):108-108. 
doi:10.1186/s13012-020-01070-3 
22. Thiem A. Using Qualitative Comparative Analysis for Identifying Causal Chains in 
Configurational Data: A Methodological Commentary on Baumgartner and Epple (2014). Sociol 
Methods Res. 2015;44(4):723-736. doi:10.1177/0049124115589032 
23. Baumgartner M, Thiem A. Often Trusted but Never (Properly) Tested: Evaluating Qualitative 
Comparative Analysis. Sociol Methods Res. 2020;49(2). doi:10.1177/0049124117701487 
24. Baumgartner M, Ambühl M. Cna: An R Package for Configurational Causal Inference and 
Modeling. 
 
 

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10. APPENDICES 
Supplemental eTable 1: Clinical variables and tumor characteristics 
Variable 
Description 
Demographics  
Study Patient ID 
Unique identifier generated by study team 
Name 
First Last 
MRN 
Medical record number 
DOB 
date of birth, MM/DD/YYYY 
DOD  
date of death, MM/DD/YYYY 
 
Vital status 
Alive, deceased 
 
ECOG date (@ time of 1st MedOnc visit) 
MM/DD/YYYY 
 
ECOG value 
0-1, >2 
 
Days difference (ECOG date – Dx date) 
# (days) 
 
Race 
American Indian or Alaskan Native, Asian, Black or 
African American, Native Hawaiian or Other Pacific 
Islander, White, Other, Declined, Unknown 
Ethnic group 
Hispanic or Latino, Non-Hispanic or Latino, Declined 
 
Sex 
Female, male, other (if available)  
Date of Diagnosis 
date of clinical or pathologic diagnosis, MM/DD/YYYY 
Age at Diagnosis 
# (year) 
 
Histology 
i.e. adenocarcinoma, poorly differentiated etc. 
TNM Stage 
Stage IV (T4, N0, M0 or any T, N1, M0 or any T, any 
N, M1) 
 
Primary site of treatment 
PPMC, PMC, LGH, PMCH, PMWT, OHCI 
Medical Oncologist 
MD name (First Last)  
Date of 1st MedOnc encounter 
MM/DD/YYYY 
 
Smoking status 
Current, former, never 
 
Molecular testing 
Date of Tissue Testing 
MM/DD/YYYY 
Type of Tissue Testing 
Spot, NGS, FISH, IHC, etc. 
Tissue testing successful? 
yes/no (QNS – quality/quantity not sufficient, 
cancelled, etc.)  
 
Tissue Testing Platform 
CPD NGS/FTP, CARIS, GenPath OnkoSight, etc.  
 
Gene panel size  
>50 vs. <50 genes 
 
Tissue NCCN markers* 
EGFR, ALK, BRAF, ROS1, MET, RET, ErbB2, 
NTRK. 
 
Tissue additional markers 
KRAS, ErbB2, BRCA1 (yes/no and specific 
alteration)  
Date of Plasma Testing 
MM/DD/YYYY 
Plasma Testing Platform 
Guardant360®, Foundation etc.  
Type of Plasma Testing 
Spot, NGS, FISH, IHC, etc. 

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Plasma testing successful?  
yes/no (QNS – quality/quantity not sufficient, 
cancelled, etc.) 
 
Plasma NCCN markers* 
EGFR, ALK, BRAF, ROS1, MET, RET, ErbB2, 
NTRK.  
 
Plasma additional markers  
KRAS, ErbB2, BRCA1 (yes/no and specific 
alteration)  
Gene panel size 
>50 vs. <50 genes 
 
Testing modality  
T, P, or T+P 
 
Treatment 
First line start date 
MM/DD/YYYY 
First line treatment regimen 
systemic therapy regimen (IO, chemo-IO, TKI, etc.) 
First line last treatment date 
MM/DD/YYYY 
Second line treatment start date 
MM/DD/YYYY 
Second line treatment regimen 
systemic therapy regimen (IO, chemo-IO, TKI, etc.)  
Second line last treatment date 
MM/DD/YYYY 
Radiation prior to first line? 
yes/no 
Type of radiation prior to first line 
curative, palliative etc. 
Date of completion of radiation 
MM/DD/YYYY 
Assessment 
NCCN markers tested prior to 1L start? 
yes/no 
Targeted Tx prescribed if targetable mutation 
detected? (If no, will record reasons why) 
yes/no  
Line of targeted TX prescribed? 
1L, 2L, subsequent lines 
 
Response to Reflex Alert Notification 
Alteration detected 
yes/no 
Alteration gene 
EGFR L858R, KRAS G12C, KIF5B-RET Fusion, etc.  
 
Date of plasma report 
MM/DD/YYYY 
Date reflex alert sent 
MM/DD/YYYY 
Turnaround time (TAT) of alert 
# (days) 
 
Name of MD receiving alert 
First Last 
Tx naïve at time of alert? 
yes/no 
 
Tx pre-reflex alert 
systemic therapy regimen (IO, chemo-IO, TKI, etc.) 
 
Tx post-reflex alert 
systemic therapy regimen (IO, chemo-IO, TKI, etc.) 
 
Change in Tx pre-vs. post reflex alert? 
yes/no 
 
Plan to prescribe a targeted Tx? 
yes/no 
Receipt of targeted Tx? 
yes/no 
Clinical team review requested 
yes/no 

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Supplemental eFigure 1: Pre-populated molecular questions 
 
 

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Supplemental eFigure 2: e-CDS alert template 

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Supplemental eFigure 3: Patient Interview Semi-Structured Questions 
iNUDGE Patient Interview Guide (Version Date: 11/8/2022): Semi-Structured Questions 
 
As part of cancer care, some patients are recommended to undergo molecular testing to identify 
which treatments might be best for your specific cancer. Sometimes this is done even before 
you know you have cancer for sure. It often involves giving your blood or another type of sample 
at a lab or in the clinic when you seek your care team.  
1. At any point in your care, did you complete molecular testing to help guide your 
treatment decisions or options for lung cancer? 
2. [IF YES]: Can you tell me a bit more about when and where you completed molecular 
testing? Did you complete the testing before your first visit with your oncologist? 
3. [IF NO]: Did you care team ever recommend that you complete molecular testing? If so, 
can you tell me a bit more about why you did not complete molecular testing? 
4. How, if at all, did your cancer care team discuss why you might need molecular testing? 
5. How, if at all, did your care team discuss how molecular testing might change what 
treatment options might be best for you? 
6. How comfortable did you feel asking questions about molecular testing with your care 
team? 
7. What things made it easier for you to complete molecular testing?  
Probe: patient, clinic, societal 
8. What things made it harder for you to complete molecular testing?  
Probe: patient, clinic, societal 
9. How important, if at all, do you think molecular testing is for your cancer care? 
10. How much, if at all, do you understand what molecular testing is looking for and why it 
might change your treatment options? 
11. How, if at all, did your oncologist discuss molecular testing during your first visit? 
PROBE: Can you describe the conversation a bit more for me? 
12. How comfortable did you feel asking questions about molecular testing during your first 
visit? 
13. [If TESTED]: How, if at all, did your oncologist discuss molecular testing results? 
14. [If TESTED]: How, if at all, did your molecular testing results impact your treatment 
options? Your treatment decisions? 
15. [If TESTED]: How comfortable did you feel asking questions about molecular testing 
results with your cancer care team? 
16. How satisfied are you with your decision to complete (or not) molecular testing?  
a. Probe: Do you have any regrets with your decision? 
17. Prior to meeting with your oncologist for the first time in person, did you complete an in-
person or telehealth visit with the lung cancer nurse navigator? [IF yes, was it in in-
person or using telehealth]? 
a. [IF NO]: Have you ever completed a telehealth or in-person visit with a lung 
cancer nurse navigator? 
b. [IF COMPLETED BEFORE]: How comfortable did you feel discussing necessary 
testing or other items related to lung cancer prior to meeting with your 
oncologist?  

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c. [IF COMPLETED EVER]: Did you discuss molecular testing with the lung cancer 
nurse navigator? Please describe the conversation a bit for me if yes. 
d. [IF COMPLETED BEFORE]: How, if at all, did your appointment with the lung 
cancer nurse navigator make it easier or harder for you to prepare for your initial 
in-person oncology visit? Why? 
e. [IF COMPLETED EVER]: How, if at all, did your appointment with the lung 
cancer nurse navigator make it easier or harder for you to prepare for treatment? 
Why? 
PROBE: Molecular testing in particular 
f. [IF COMPLETED EVER]: How, if at all, did your appointment with the lung 
cancer nurse navigator make it easier or harder for you to communicate with your 
cancer care team? Why? 
g. [ALL]: Do you think it was (or would be) appropriate to meet with a lung cancer 
nurse navigator before meeting with your oncologist? Why or why not? Would 
you prefer in-person or telehealth? 
18. Do you have any additional thoughts on any of the items we discussed?