Evaluation of a Scalable Decision Support and Shared Decision Making Tool for Lung Cancer Screening
Phase: NA
Conditions: Early Detection of Cancer, Lung Neoplasms
Sponsor: University of Utah

 
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Design and Implementation of Scalable Decision Support 
and Shared Decision Making for Lung Cancer Screening 
 
Protocol Summary 
 
 
 
IRB Approval 
Date of 
Current 
Version: 
2/16/2024 
University of 
Utah IRB #: 
IRB_00125797 
Sponsor: DHHS AGNCY FOR HEALTHCARE RESH & QUALITY 
  
Principal 
Investigator: 
Kensaku Kawamoto 
 
This document was created using the ERICA Online System at the University of Utah. The document is created 
from study information approved by the IRB on the date listed above.  Any alteration to the original content of this 
document may not be considered to represent the study as approved by the IRB.  
 
 

 
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Background and Introduction 
 
Lung cancer is prevalent and deadly. Lung cancer is the second most commonly diagnosed 
cancer in the United States (US), with an estimated 234,030 new diagnoses in 2018, and it is 
the leading cause of cancer-related deaths among both men and women, with an estimated 
150,050 deaths in 2018.1 Lung cancer accounts for approximately 1 in 4 cancer-related 
deaths.1 The primary risk factor for lung cancer is cigarette smoking.1 
  
Low-dose computed tomography (LDCT) screening is effective for reducing lung cancer 
mortality. The National Lung Screening Trial (NLST)2 enrolled 53,454 participants aged 55 to 
74 years who had a history of cigarette smoking of at least 30 pack-years and, if former 
smokers, had quit within the past 15 years. This study randomized participants to screening 
with either LDCT or chest radiography. Compared to the radiography group, LDCT resulted in 
a 20% relative reduction in lung cancer mortality (p = 0.004).2 This 20% relative reduction in 
lung cancer deaths with screening is larger than the reduction in breast cancer deaths with 
mammograms. Based on this finding,2 the US Preventive Services Task Force (USPSTF) 
recommended in 2013 that annual LDCT be performed for lung cancer screening in patients 
meeting NLST enrollment criteria.3,4 The USPSTF recommendations expand the eligible age 
range from 55 to 80 years inclusive. The US Preventive Services Task Force (USPSTF) 
provided a grade B recommendation in 2013 that annual LDCT lung cancer screening be 
offered to patients 55 to 80 years old with a 30+ pack-year smoking history who are current 
smokers or quit in the last 15 years.3,4 In March 2021, the USPSTF updated its 
recommendation to lower the eligibility age to 50 and the smoking requirement to 20+ pack-
years.5 The revised criteria should reduce lung cancer mortality by an additional 33% while 
increasing the number of screening-eligible patients by 67%.6 
  
LDCT can result in significant harm, with the balance of benefits and harms dependent on the 
patient’s risk profile. The NLST found substantial harms associated with LDCT screening, 
including high false positive rates (96.4%) which can lead to unnecessary and potentially 
harmful invasive procedures.2 Moreover, additional studies found that the balance of benefits 
and harms is highly dependent on the patient’s risk profile.7–9 These studies developed 
validated models for predicting the likelihood of lung cancer, false positive LDCT tests, and 
adverse events based on patient-specific risk factors including age, gender, smoking history, 
and asbestos exposure.7–9 Among LDCT-eligible patients, the ratio of false positive results to 
LDCT-prevented lung cancer death range from 1,648 among those in the lowest quintile of 
risk to 65 for those in the highest quintile of risk.8 
  
Shared decision making (SDM) ensures that patients and providers make informed, patient-
centered decisions regarding LDCT screening. Because the net benefit of LDCT screening can 

 
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vary dramatically across eligible patients, the USPSTF strongly advocates for SDM, asserting 
that “the decision to begin screening should be the result of a thorough discussion of the 
possible benefits, limitations, and known and uncertain harms.”3 In a 2015 decision memo, the 
Centers for Medicare and Medicaid Services (CMS) required a SDM consultation involving 
use of a decision aid for payment of initial LDCT screening.10 SDM is the process wherein the 
provider shares medical information about options and patients share their values and 
preferences. Following the sharing of information by both parties, the patient and provider 
collaborates to decide on the course of action that reflects both the best medical evidence and 
the patient’s preferences and values.11,12 
  
Current LDCT screening rates among eligible patients is <5% nationally. Despite the USPSTF 
recommending LDCT in 2013, the percentage of eligible smokers who received LDCT 
screening in the past 12 months remained low and constant, from 3.3% in 2010 to 3.9% in 
2015,13  and a more recent analysis found no improvements.14 At University of Utah Health, 
6.4% of eligible patients received LDCT screening in year preceding August 2018. 
  
The benefits of LDCT screening outweigh even highly negative patient views ~50% of the 
time. In a recent study led by co-I Caverly, a state-transition microsimulation model was 
developed to evaluate the impact of both patient risk and patient preferences.15 This study 
found that for ~50% of the study population at the highest risk, the benefits of LDCT 
screening overcame even highly negative views about screening and its downsides. 
  
If eligible patients in the top 60% of risk were screened, ~10,000 lung cancer deaths could be 
averted each year in the US. This represents 88% of the possible lung cancer mortality benefits 
from screening.8 Since screening of all USPSTF-eligible patients could avert 12,250 lung 
cancer deaths per year in the US,16 and given the existing ~5% screening rate, appropriate 
SDM and LDCT could avert up to ~10,000 lung cancer deaths per year if patients in the top 
60% of risk were screened (12,500 if all screened * 0.88 benefit from screening top 60% * 
0.95 not yet screened = 10,450). As noted earlier, the benefits of LDCT screening should 
outweigh any negative patient preferences for ~50% of patients. Also, of 4,246 LDCT-eligible 
patients who were offered screening and paper-based SDM at eight medical centers in the 
Veterans Health Administration (VHA), 58% agreed to undergo screening and 50% underwent 
screening during the study timeframe.17 Thus, we believe a screening rate of 40-60% is a 
reasonable expectation if all eligible patients underwent effective SDM for LDCT. Even if 
only the patients at the top 40% of risk were screened, 73% of the lung cancer mortality 
benefits from screening would be achieved, translating to ~8,700 lung cancer deaths averted 
annually in the US.8 

 
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The level of evidence underlying these recommendations is substantial. Through its systematic 
evidence review, the USPSTF concluded that LDCT screening should be offered to eligible 
patients due to moderate certainty of substantial net benefit (evidence grade B).3 Moreover, the 
mandate by CMS and the USPSTF that SDM be performed prior to LDCT screening reflects 
the strong evidence that SDM improves outcomes. Research has found that patients who share 
in medical decision making tend to be more satisfied with their care,18 report a better quality 
of life,19 and are more adherent to recommendations than those who do not.20 
  
Stand-alone, Web-based CDS tools are available for LDCT SDM, including a tool developed 
by the project team known as Decision Precision that uses a continuous risk model. In 
recognition of the need to support SDM for LDCT screening, several groups, including 
AHRQ, have developed stand-alone, Web-based CDS tools in this area.21–24 An important 
limitation of these other tools is that they use trial averages rather than provide personalized 
guidance based on a continuous model. Our project leverages a provider-facing, Web-based 
CDS and SDM tool known as “Decision Precision” that uses a continuous model for providing 
patient-specific guidance. Decision Precision supports the USPSTF guidelines for LDCT 
screening5 while providing patient-specific information on the expected benefits and harms of 
screening.7–9,25 This Web tool provides (1) personalized quantitative risk assessment of the 
trade-offs; (2) patient-friendly language; (3) graphics that have been empirically demonstrated 
to help patients understand their personalized benefits and harms; and (4) quick and easy 
documentation of personalized SDM after using the tool. The system also supports printing a 
patient handout that includes patient-specific benefit and harm information, whether he/she is 
eligible for LDCT screening per USPSTF guidelines, guidance on smoking cessation, 
information on LDCT, and what happens next if the screening test is positive. Decision 
Precision also generates a summary of the SDM session that can be copied into the EHR for 
documentation. 
  
Decision Precision has been iteratively enhanced through usability studies and pilot testing. To 
determine the best risk communication method for presenting the benefits and harms of lung 
cancer screening, we first conducted a randomized survey experiment with 1,612 adult 
smokers. We compared comprehension and perception of lung cancer screening benefits and 
harms when information was presented using four different evidence-based formats. 
Participants who viewed a pictograph had better knowledge about the magnitude of the benefit 
and how this benefit compared with important harms, leading to our use of pictographs in the 
tool. In addition, we iteratively designed four versions of the Web tool based on usability 
testing with decision aid researchers, primary care providers (PCPs), and patients. Decision 
Precision has been further refined through feedback from providers and patients who have 
helped further develop the tool at 4 VHA medical centers: Ann Arbor, Durham, Portland, and 
Charleston. The tool has been used to help guide discussions about lung cancer screening with 

 
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>1,100 patients. Table 1 outlines key lessons learned and how they will be applied in this 
project. 
  
Table 1. Key Lessons Learned from Clinical Use of Decision Precision and Incorporation into 
Project 
  
Key Lesson 
Incorporation into Project 
Clinicians are used to binary decisions 
based on yes/no cut-offs rather than SDM 
weighing continuous risks and benefits. 
Decision Precision now specifies when screening 
is of high benefit and  likely to overcome even 
highly negative patient preferences.15 
Incorporating time-consuming SDM can be 
challenging in the context of busy and 
time-constrained clinic visits. 
Explore leveraging non-physician care team 
members for SDM. Enable patients to review 
Decision Precision+ results at home between 
visits. 
User training is important, but must be 
time-efficient. One site declined to 
participate, citing workload and time 
demands. 
User training for Decision Precision+ has been 
made as efficient as possible, so that it can be 
deployed in contexts without external funding. 
Local workflows can vary greatly. A 
scalable CDS solution must not only 
account for variations in health IT 
platforms across health systems but also 
variations in workflow. 
Leverage extensive workflow assessments, 
including a PCORI-funded assessment of 
smoking-related clinical workflows being 
conducted at over 30 community primary care 
practices across the state of Utah. 
Response rates to paper-based patient 
surveys are decent (27%) but could be 
improved. 
Patient surveys will both be mailed and 
administered electronically using the patient 
questionnaire feature of the personal health record 
(PHR). 
  
Stand-alone CDS tools are limited by a lack of workflow integration and duplicate data entry. 
Stand-alone, Web-based CDS tools such as Decision Precision are easy to deploy. However, 
they have significant limitations: (1) they are not integrated into EHRs and routine workflows; 
(2) they require manual, often duplicative data entry; and (3) patients and providers are often 
unaware of their existence. In the context of busy clinical practices, stand-alone, Web-based 
CDS tools have a significant risk of limited impact due to suboptimal use.26,27 
  
Integration of CDS with the EHR can overcome limitations of stand-alone tools, but widely 
scaling such CDS tools is difficult. Integrated CDS tools, as opposed to stand-alone CDS tools, 
(1) integrate the CDS with usual clinical workflows and (2) remove the need for duplicate data 

 
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entry. Wide dissemination of EHR-integrated CDS has been difficult, however, with a major 
challenge being the lack of standards-based interoperability for CDS deployment across health 
systems and EHR platforms.28–30 
  
Recent efforts have advanced the vision of standards-based CDS interoperability across EHR 
platforms,31–40 and the project team has been at the forefront of these efforts. Table 2 on the 
next page summarizes these efforts and our role. As detailed in Aim 1, we will enable CDS 
interoperability leveraging the standards developed in these efforts, including the Clinical 
Quality Framework (CQF) standards for CDS and electronic clinical quality measure (eCQM) 
interoperability developed with PI Kawamoto’s leadership and through the sponsorship of 
CMS and the US Office of the National Coordinator for Health IT (ONC). A summary of CDS 
interoperability standards is also available in an AHRQ-sponsored Webinar on the topic given 
by PI Kawamoto.41 
  
This project will enable widespread CDS to optimize lung cancer LDCT screening and provide 
a model for widely disseminating other evidence-based CDS. We will adapt the stand-alone 
Decision Precision tool into a standards-based CDS tool that is fully integrated with the EHR 
(Aim 1), integrate the tool with multiple EHR systems and widely disseminate the tool (Aim 
2), and evaluate the reach and impact of the tool (Aim 3). Because the approach will be 
scalable using standards-based approaches, and because use of the tool could be greatly 
facilitated by CMS payment rules requiring SDM when initiating LDCT lung cancer 
screening,10 the tool could ultimately enable more appropriate lung cancer LDCT screening 
for the estimated 8.6 million US adults who meet USPSTF LDCT screening guidelines.16 Such 
optimized LDCT screening could prevent as many as 10,000 lung cancer deaths annually 
while minimizing adverse events associated with screening.8,13,15,16 
  
The proposed SDM approach can be readily adapted to other conditions. As such, this project 
will provide a template for the widespread implementation of other evidence-based findings 
through CDS-enabled SDM. 
  
Table 2. Notable Initiatives Related to CDS Interoperability and Project Team Role 
  
Initiative 
Description 
Project Team Role 
AHRQ PCCDS 
(formerly PCOR 
Collaboration community for 
learning how best to translate 
evidence-based research findings to 
Steering Committee Member (PI 
Kawamoto) 

 
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CDS) Learning 
Network36 
improve patient outcomes via 
patient-centered CDS 
  
AHRQ CDS 
Connect37 
Project to develop repository and 
dissemination channels for 
standards-based CDS artifacts 
Work group member for CDS 
repository design and 
implementation (PI Kawamoto) 
Health Level 7 
International 
(HL7) 
Leading standards development 
organization for health IT 
internationally 
Board Member (PI Kawamoto) and 
Co-Chairs of CDS Work Group (PI 
Kawamoto, co-I Del Fiol) 
ONC/CMS CQF 
initiative31 
Effort to develop and validate 
harmonized HL7 interoperability 
standards for CDS and eCQM 
Co-Initiative Coordinator (PI 
Kawamoto) 
  
ONC Health 
eDecisions 
(HeD)38 
Predecessor effort to CQF that 
developed and validated HL7 CDS 
interoperability standards 
Initiative Coordinator (PI 
Kawamoto) 
US Health IT 
Advisory 
Committee 
(HITAC)42 
Committee established by 21st 
Century Cures Act to provide 
counsel to National Coordinator for 
Health IT 
Co-chair of Interoperability 
Standards Priorities Task Force (PI 
Kawamoto) 
National 
Academy of 
Medicine CDS 
Effort to develop action plan for 
interoperable, effective CDS at 
scale43 
Work Group Lead for Interoperable 
CDS Content (PI Kawamoto) 
SMART on 
FHIR39 and CDS 
Hooks44 
Efforts to embed interoperable apps 
and CDS services within EHRs via 
OAuth and FHIR standards 
Active implementers of standards-
based CDS solutions in production 
settings (PI Kawamoto) 
OpenCDS40 
Open-source, freely-available 
implementation of CDS and eCQM 
interoperability standards 
Founder and lead (PI Kawamoto) 
  
  
LDCT is currently the only recommended screening option for the lung cancer screening.45 
Chest Radiographs have been evaluated in a large randomized controlled trial and found to be 
ineffective.46  If a patient chooses to forego LDCT screening, the provider is expected to 
support this decision.  In such cases, the provider may recommend risk-mitigating strategies 
such as smoking cessation, avoiding second-hand smoke, avoiding asbestos exposure, and 
improving diet and exercise.  Respecting the patient’s autonomy is especially important when 
the balance of benefits and harms makes the screening preference-sensitive.  In such cases, the 
provider is expected to explain that both screening and not screening are reasonable.  While 
the decision to screen or not screen will ultimately be up to the patient, if a patient is not 
healthy enough to undergo curative lung resection or has limited life-expectancy due to other 

 
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comorbidities, that patient is unlikely to benefit from lung cancer screening, and the provider 
would be expected to recommend against screening.  The provider may also recommend 
against screening if the balance of harms versus benefits is unfavorable, or if the patient does 
not meet USPSTF criteria for lung cancer screening. In any case, the decision will ultimately 
be up to the patient. 
  
References 
  
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doi:10.1093/jnci/95.6.470 

 
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10.        Centers for Medicare and Medicaid Services. Decision memo for screening for 
lung cancer with low dose computed tomography. Published 2015. Accessed August 29, 
2019. https://www.cms.gov/medicare-coverage-database/details/nca-decision-
memo.aspx?NCAId=274. 
11.        O’Connor AM. Validation of a Decisional Conflict Scale. Med Decis Mak. 
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13.        Jemal A, Fedewa SA. Lung Cancer Screening With Low-Dose Computed 
Tomography in the United States—2010 to 2015. JAMA Oncol. 2017;3(9):1278. 
doi:10.1001/jamaoncol.2016.6416 
14.        Pham D, Bhandari S, Oechsli M, Pinkston CM, Kloecker GH. Lung cancer 
screening rates: Data from the lung cancer screening registry. J Clin Oncol. 
2018;36(15_suppl):6504. doi:10.1200/jco.2018.36.15_suppl.6504 
15.        Caverly TJ, Cao P, Hayward RA, Meza R. Identifying Patients for Whom Lung 
Cancer Screening Is Preference-Sensitive. Ann Intern Med. 2018;169(1):1. 
doi:10.7326/m17-2561 
16.        Ma J, Ward EM, Smith R, Jemal A. Annual number of lung cancer deaths 
potentially avertable by screening in the United States. Cancer. 2013;119(7):1381-1385. 
doi:10.1002/cncr.27813 
17.        Kinsinger LS, Anderson C, Kim J, et al. Implementation of Lung Cancer 
Screening in the Veterans Health Administration. JAMA Intern Med. 2017;177(3):399. 
doi:10.1001/jamainternmed.2016.9022 
18.        Moyer A, Salovey P. Patient participation in treatment decision making and the 
psychological consequences of breast cancer surgery. Womens Health. 1998;4(2):103-
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19.        Street RL, Voigt B. Patient Participation in Deciding Breast Cancer Treatment 
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20.        Brian Haynes R, Ann McKibbon K, Kanani R. Systematic review of 
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21.        Agency for Healthcare Research and Quality. Lung cancer screening: a 
clinician’s checklist. Published 2016. Accessed August 29, 2019. 

 
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https://effectivehealthcare.ahrq.gov/tools-and-resources/patient-decision-aids/lung-
cancer-screening/clinicians-checklist/ 
22.        Memorial Sloan Kettering Cancer Center. Lung cancer screening decision tool. 
Published 2016. Accessed August 29, 2019. 
http://nomograms.mskcc.org/Lung/Screening.aspx 
23.        American Lung Association. Lung cancer screening saves lives [online patient 
decision aid]. Published 2016. Accessed August 29, 2019. https://www.lung.org/our-
initiatives/saved-by-the-
scan/?utm_source=domain&utm_medium=redirect&utm_campaign=saved_by_the_sca
n 
24.        University of Michigan. Lung cancer CT screening: should I get screened? 
Published 2016. Accessed August 29, 2019. https://shouldiscreen.com/English/home 
25.        Caverly T. Selecting the Best Candidates for Lung Cancer Screening. JAMA 
Intern Med. 2015;175(6):898. doi:10.1001/jamainternmed.2015.1235 
26.        Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice 
using clinical decision support systems: a systematic review of trials to identify features 
critical to success. BMJ. 2005;330(7494):765. doi:10.1136/bmj.38398.500764.8f 
27.        Bates DW, Kuperman GJ, Wang S, et al. Ten Commandments for Effective 
Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality. 
J Am Med Informatics Assoc. 2003;10(6):523-530. doi:10.1197/jamia.m1370 
28.        Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A 
Roadmap for National Action on Clinical Decision Support. J Am Med Informatics 
Assoc. 2007;14(2):141-145. doi:10.1197/jamia.m2334 
29.        Sittig DF, Wright A, Osheroff JA, et al. Grand challenges in clinical decision 
support. J Biomed Inform. 2008;41(2):387-392. doi:10.1016/j.jbi.2007.09.003 
30.        Kawamoto K, Del Fiol G, Lobach DF, Jenders RA. Standards for Scalable 
Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal 
for Progress. Open Med Inform J. 2010;4(1):235-244. 
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31.        Clinical Quality Framework Initiative. Clinical Quality Framework homepage. 
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https://oncprojectracking.healthit.gov/wiki/display/TechLabSC/CQF+Home 
32.        Health Level 7. HL7 Guideline Appropriate Ordering FHIR Implementation 
Guide. Accessed August 29, 2019. https://www.hl7.org/fhir/2016Jan/gao/gao.html 
33.        Healthcare Services Platform Consortium. The Healthcare Innovation 
Ecosystem. Published 2016. Accessed August 29, 2019. 

 
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https://healthservices.atlassian.net/wiki/display/HSPC/Healthcare+Services+Platform+
Consortium 
34.        Middleton B. The clinical decision support consortium. Stud Health Technol 
Inform. 2009;150:26-30. Accessed August 30, 2019. 
http://www.ncbi.nlm.nih.gov/pubmed/19745260 
35.        Office of the National Coordinator for Health IT. Certification of Health IT. 
Published online 2018. https://www.healthit.gov/topic/certification-ehrs/certification-
health-it 
36.        AHRQ Patient-Centered CDS Learning Network. Patient-Centered CDS 
Learning Network Home Page. https://pccds-ln.org/ 
37.        Agency for Healthcare Research and Quality. CDS Connect. Accessed August 
30, 2019. https://cds.ahrq.gov/cdsconnect 
38.        Heatlh eDecsions Initiative. Health eDecisions homepage. Accessed August 30, 
2019. 
https://oncprojectracking.healthit.gov/wiki/display/TechLabSC/Health+eDecisions. 
39.        Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: 
A standards-based, interoperable apps platform for electronic health records. J Am Med 
Informatics Assoc. 2016;23(5):1-10. doi:10.1093/jamia/ocv189 
40.        OpenCDS Home. Open Clinical Decision Support Tools and Resources. 
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41.        AHRQ Patient-Centered CDS Learning Network. Patient-Centered CDS 
Learning Network Webinars. Published 2017. Accessed August 30, 2019. https://pccds-
ln.org/webinars 
42.        Office of the National Coordinator for Health IT. Health IT Advisory 
Committee. Published 2018. Accessed August 30, 2019. https://www.healthit.gov/hitac/ 
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46.        Oken MM, Hocking WG, Kvale PA, et al. Screening by chest radiograph and 
lung cancer mortality: The Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized 
trial. JAMA - J Am Med Assoc. 2011;306(17):1865-1873. doi:10.1001/jama.2011.1591 
  
 
 
 
Purpose and Objectives 
 
The purpose of this project is to increase appropriate low-dose computed tomography (LDCT) 
lung cancer screening through the development and wide dissemination of patient-centered 
clinical decision support (CDS) tools that (1) are integrated with the electronic health record 
(EHR) and clinical workflows, (2) prompt for shared decision making (SDM) when patients 
meet screening criteria, and (3) enable effective SDM using individually-tailored information 
on the potential benefits and harms of screening. The study will promote standard of care that 
is endorsed by the Centers for Medicare & Medicaid Services (CMS) and the US Preventive 
Services Task Force (USPSTF).The US Preventive Services Task Force (USPSTF) provided a 
grade B recommendation in 2013 that annual LDCT lung cancer screening be offered to 
patients 55 to 80 years old with a 30+ pack-year smoking history who are current smokers or 
quit in the last 15 years. In March 2021, the USPSTF updated its recommendation to lower the 
eligibility age to 50 and the smoking requirement to 20+ pack-years. 
  
This project is supported both operationally and by an Agency for Healthcare Research and 
Quality (AHRQ) R18 grant. 
  
The EHR-based CDS tools are being developed by the University of Utah ReImagine EHR 
team, directed by Dr. Kensaku Kawamoto. Within University of Utah Health, Dr. Kawamoto 
is also Associate Chief Medical Information Officer and Director of the health system’s 
Knowledge Management and Mobilization unit. Dr. Kawamoto’s team receives operational 
funding from University of Utah Health to develop EHR-based solutions to improve patient 
care and the provider experience. Grant funding such as the current AHRQ R18 grant enable 
this operational team to allocate more resources to the optimal design, development, and 
implementation of these software tools, as well as to undertake more rigorous intervention 
design and evaluation procedures that would not be possible in the context of operational 
quality improvement (QI). 
  
This project will leverage Decision Precision (https://share.lungdecisionprecision.com/), a 
validated Web-based tool for LDCT SDM developed at the Veterans Health Administration, as 
well as an initial version of Decision Precision+, an EHR-integrated version of the tool which 
can be accessed directly in the EHR and auto-populate relevant patient data in the tool instead 

 
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of requiring manual data entry. An initial version of Decision Precision+ was developed and 
made accessible within the Epic EHR shortly after the AHRQ grant was submitted for 
operational QI purposes. However, due to feedback from physician leaders that the system 
needed to be streamlined for optimal use in busy primary care settings, the tool’s availability 
was never advertised and no CDS prompts were introduced to encourage its use. 
Consequently, tool access has been minimal to date. 
  
This study seeks to build upon our existing work in CDS and SDM to promote appropriate 
LDCT lung cancer screening as recommended by the USPSTF and CMS. Listed below are the 
three study aims, as well as the human subjects research associated with each aim. 
  
Aim 1. System Design and Development 
Design and develop a standards-based CDS tool for lung cancer screening SDM that is 
integrated with the EHR and can be effectively used in busy primary care settings (Decision 
Precision+); design and develop CDS tools for optimally integrating the tool into clinical 
workflows; and advance underlying standards and their adoption. Information for user-
centered design and workflow assessments will be collected through cognitive work analysis 
interviews with clinicians at University of Utah Health. Human subjects research for this aim 
will consist of the following: 
  
- Pre-Implementation Provider and Staff Interviews  
  
Aim 2. Implementation Trial and Iterative System Refinement 
  
Conduct a pragmatic implementation trial of Decision Precision+ and associated CDS tools 
within University of Utah Health. Make Decision Precision+ available to other institutions 
through app stores, and also share associated CDS tools. There will be no randomization, and 
the actual clinical trial will be preceded by a pilot implementation to ensure that the tools are 
useable and compatible with clinical workflows. The study population for the clinical trial will 
be primary care patients at University of Utah Health who are eligible for or potentially 
eligible for LDCT screening and the associated SDM, and the initial intervention will include 
availability of Decision Precision+ combined with CDS prompts to promote its appropriate 
use. The intervention will be enhanced based on feedback, with anticipated enhancements 
including prompting medical assistants to collect required smoking history information from 
potentially eligible patients, as well as prompting patients to consider LDCT screening and 

 
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SDM via the personal health record. Human subjects research for this aim will consist of the 
following: 
  
- Provider and Staff Participation in Pilot Implementation 
- Patient Participation in Pilot Implementation 
- Provider and Staff Participation in Clinical Trial 
- Patient Participation in Clinical Trial 
- Post-implementation Provider and Staff Interviews 
  
Aim 3. Evaluation 
  
Evaluate the impact of the CDS tool, including for adoption, clinical impact, and financial 
impact. We will conduct an interrupted time series (ITS) study of the implementation trial; 
focus groups and surveys with patients, staff, and providers; and evaluation of the resource 
costs associated with implementation. Human subjects research for this aim will consist of the 
following: 
  
 - Patient Outcomes Assessment 
 - Provider and Staff App Usage Assessment 
 - Provider and Staff Surveys 
 - Patient Surveys 
 - Provider and Staff Focus Groups 
 - Patient Focus Groups 
 - Stakeholder Interviews 
 
 
 
Study Population 
 
Age of Participants: 18+ 
 

 
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Sample Size: 
At Utah: 
  
All Centers: 
up to 21,000 patients 
 
Inclusion Criteria: 
Aim 1. Design 
Pre-Implementation Provider and Staff Interviews: Up to 36 providers and staff members 
will be included in the interviews. Providers and staff will be eligible for the interviews if 
they provide care related to lung cancer screening for the targeted patient population at a 
University of Utah primary care clinic. 
  
Aim 2. Implementation 
  
Provider and Staff Participation in Pilot Implementation: Up to 200 providers and staff will 
serve as pilot users of the intervention. Providers and staff will be eligible if they volunteer 
to serve as pilot users or provide care related to lung cancer screening for the targeted 
patient population at a pilot clinic. 
  
Patient Participation in Pilot Implementation: We anticipate up to 2,000 patients may be a 
part of the pilot implementation, with the intervention available to support their care. This 
population will consist of patients eligible for, or potentially eligible for, LDCT lung cancer 
screening according to USPSTF guidelines, who are seen by a pilot user of the intervention. 
The inclusion criteria for pilot implementation are (i) >= 55 years and <= 80 years old at the 
time of the visit; (ii) does not already have lung cancer; and (iii) meets 2013 USPSTF 
smoking criteria for LDCT screening (30+ pack-year smoking history and current smoker or 
quit in the past 15 years) or may meet the criteria if a complete smoking history were taken. 
  
Provider and Staff Participation in Clinical Trial: Up to 1,000 providers and staff in 
primary care will be included in the clinical trial. Providers and staff will be eligible if they 
provide care related to lung cancer screening for the targeted patient population at a 
University of Utah primary care clinic. 
  
Patient Participation in Clinical Trial: We anticipate up to 19,000 primary care patients will 
be enrolled, with the intervention available to support their care. Patients will be eligible for 
inclusion in the trial if they receive care at University of Utah primary care clinics. The 
study population will consist of patients eligible for, or potentially eligible for, LDCT lung 
cancer screening according to USPSTF guidelines, who are seen at one of these clinics. The 

 
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inclusion criteria for phase 1 are (i) >= 55 years and <= 80 years old at the time of the visit; 
(ii) does not already have lung cancer; and (iii) meets 2013 USPSTF smoking criteria for 
LDCT screening (30+ pack-year smoking history and current smoker or quit in the past 15 
years) or may meet the criteria if a complete smoking history were taken.  The inclusion 
criteria for phase 2 are (i) >= 50 years and <= 80 years old at the time of the visit; (ii) does 
not already have lung cancer; and (iii) meets 2021 USPSTF smoking criteria for LDCT 
screening (20+ pack-year smoking history and current smoker or quit in the past 15 years) or 
may meet the criteria if a complete smoking history were taken.  
  
Post-Implementation Provider and Staff Interviews: Up to 36 providers and staff who 
participated in the pilot implementation or clinical trial will be included in the interviews. 
  
Aim 3. Evaluation 
  
Patient Outcomes Assessment: up to 21,000 patients will be enrolled. Eligibility criteria are 
the same as for participation in the pilot implementation and clinical trial. 
  
Provider and Staff App Usage Assessment: up to 1,200 providers and staff will be enrolled. 
Eligibility criteria are the same as for participation in the pilot implementation and clinical 
trial. 
  
Provider and Staff Surveys: Up to 400 providers and staff from University of Utah primary 
care clinics will be surveyed regarding the intervention. Providers and staff will be eligible if 
they have interacted with the intervention through the pilot implementation or clinical trial. 
  
Patient Surveys: Up to 200 patients from University of Utah primary care clinics will be 
surveyed regarding the intervention. Patients will be eligible if the intervention was used in 
their care and they speak English. 
  
Provider and Staff Focus Groups: Up to 36 providers and staff will be enrolled in focus 
groups to evaluate the intervention as well as explore implementation and adoption issues 
with CDS, SDM, and guideline-focused care in general. Eligibility criteria will be the same 
as for the provider and staff surveys. 

 
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Patient Focus Groups: Up to 18 patients will be enrolled in focus groups to evaluate the 
intervention as well as explore implementation and adoption issues with CDS, SDM, and 
guideline-focused care in general. Eligibility criteria will be the same as for the patient 
surveys. 
  
Stakeholder Interviews: Up to 100 key stakeholders who implemented the intervention at the 
University of Utah or elsewhere will be interviewed with a goal of understanding barriers 
and facilitators to the scalable dissemination of evidence-based CDS, as well as to estimate 
the associated time and resource costs. Eligibility criteria will be involvement in the 
implementation of the intervention for clinical care purposes. 
 
 
Exclusion Criteria: 
None. 
 
 
 
Design  
 
Prospective Biomedical Intervention or Experiment 
 
 
 
 
 
Study Procedures 
 
Recruitment/Participant Identification Process: 
Aim 1. Design 
  
Pre-Implementation Provider and Staff Interviews: We will recruit participants in-person or 
via email in consultation with Dr. Michael Flynn, who oversees research and informatics for 
the University of Utah Community Physicians Group, and who is a co-investigator on the 
project. Recruitment may also take place during staff meetings or short educational 
presentations at clinic sites by Dr. Flynn, other research staff or other clinic directors. 
  
Aim 2. Implementation 
  
Provider and Staff Participation in Pilot Implementation and Clinical Trial: Because the 
intervention is promoting standard of care, we will base our approach on how University of 

 
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Utah Health operationally introduces other health IT interventions to promote standard of care. 
The PI is Associate Chief Medical Information Officer for the health system and routinely 
introduces similar interventions for operational purposes outside of a research context. We will 
follow this usual process, including seeking approval from relevant governance groups such as 
the Community Physical Group Medical Directors or the EHR Operations committee. We will 
seek a waiver of informed consent for individual providers and staff as described later. 
  
Patient Participation in Pilot Implementation and Clinical Trial: We will seek a waiver of 
informed consent for targeted patients as described later. 
  
Post-Implementation Provider and Staff Interviews: We will use the same recruitment 
strategies as for Aim 1. 
  
Aim 3. Evaluation 
  
Patient Outcomes Assessment: The recruitment approach will be the same as for the patient 
participation in the pilot implementation and clinical trial as described above. 
  
Provider and Staff App Usage Assessment: The recruitment approach will be the same as for 
the provider and staff participation in the pilot implementation and clinical trial as described 
above. 
  
Provider and Staff Surveys: Survey study participants will be recruited via email. To improve 
the response rate, we may send up to three reminders. Additionally, we may recruit through 
targeted in-person distribution of the surveys by research team members. 
  
Patient Surveys: Survey study participants will be recruited electronically or via postal mail. 
  
Provider and Staff Focus Groups: We will ask in the surveys above if participants are 
potentially interested in the focus groups. We will recruit from those respondents who answer 
positively to this question. If needed, additional participants will be recruited in-person or via 
email. 

 
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Patient Focus Groups: We will ask in the surveys above if participants are potentially 
interested in the focus groups. We will recruit from those respondents who answer positively 
to this question. 
  
Stakeholder Interviews: We will recruit participants via email. 
  
Data Quality Assurance: Because the intervention is promoting standard of care, we will base 
our approach on how University of Utah Health operationally introduces other health IT 
interventions to promote standard of care. As part of standard quality assurance procedures, 
we will assess smoking history and comorbidities data for all adults who had an outpatient 
visit in University of Utah Health primary care clinic. 
 
 
Informed Consent: 
Description of location(s) where consent will be obtained:  
For interviews and focus groups, verbal consent will be obtained where the interviews and 
focus groups are conducted, which are expected to be at the clinics. For surveys, proceeding 
with the survey will constitute consent. This is expected to take place at the clinics or at the 
respondents' homes.  
 
Description of the consent process(es), including the timing of consent:  
Aim 1. Design Pre-Implementation Provider and Staff Interviews: Oral informed consent will 
be obtained at the beginning of the interview process following participant review of a 
Consent Cover Letter. See item 8 for details. Aim 2. Implementation Provider and Staff 
Participation in Pilot Implementation: For the pilot implementation, a waiver of informed 
consent is sought following assent of the pilot users or the clinical leadership (see Waiver 
request). Patient Participation in Pilot Implementation: A waiver of informed consent is sought 
following assent of assent of the pilot users or the clinical leadership (see Waiver request). 
Provider and Staff Participation in Clinical Trial: A waiver of informed consent is sought 
following assent of the clinical leadership (see Waiver request). Patient Participation in 
Clinical Trial: A waiver of informed consent is sought following assent of the clinical 
leadership (see Waiver request). Post-Implementation Provider and Staff Interviews: For 
provider and staff interviews, verbal informed consent will be obtained at the beginning of the 
interview process. Aim 3. Evaluation Patient Outcomes Assessment: A waiver of informed 
consent is sought as for the Patient Participation in the Pilot Implementation and Clinical Trial. 
Provider and Staff App Usage Assessment: A waiver of informed consent is sought as for the 
Provider and Staff Participation in the Pilot Implementation and Clinical Trial. Provider and 
Staff Surveys: Electronic informed consent will be obtained at the beginning of the survey. 
See item 8 for details. Patient Surveys: Proceeding with the survey will constitute consent. See 
item 8 for details. Provider and Staff Focus Groups: Oral informed consent will be obtained at 
the beginning of the focus groups following participant review of a Consent Cover Letter. See 

 
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item 8 for details. Patient Focus Groups: Oral informed consent will be obtained at the 
beginning of the focus groups following participant review of a Consent Cover Letter. See 
item 8 for details. Stakeholder Interviews: Oral informed consent will be obtained at the 
beginning of the interviews following participant review of a Consent Cover Letter. See item 8 
for details.  
 
Requested Waivers/Alterations of Consent: 
Waiver of Informed 
Consent 
Request for waiver of consent for providers and staff to be 
exposed to EHR-based intervention facilitating lung cancer 
shared decision making for patients meeting USPSTF eligibility 
criteria for this clinical service 
Waiver of Informed 
Consent 
Request for waiver of consent for patient to have their providers 
and staff to have access to the IT intervention promoting 
standard of care, and to evaluate their data to assess 
intervention impact. 
 
Procedures: 
Listed below are the procedures for the human subjects research aspects of this project. 
Procedures for activities outside of humans subjects research are not listed, such as 
engagement with standards development organizations to advance relevant health IT 
standards; software development, implementation and monitoring undertaken to fulfill clinical 
operations and operational QI responsibilities rather than for research purposes; and making 
the software developed in this project available in EHR app stores. 
  
Aim 1. System Design and Development 
  
Pre-Implementation Provider and Staff Interviews: During each interview, the interviewer will 
ask the participant to recall a recent relevant patient and provide a 1) summary, 2) timeline, 
and 3) a deepened description. After each interview, we will display the intervention or a 
mock-up (if not yet fully implemented) and conduct usability assessments. The interviews will 
be coded and modeled through consensus by members of the research team. The results of the 
interviews will be used to guide refinement of the intervention. Interviews are expected to take 
approximately 45 minutes to 1 hour, taking place outside of normal work hours. Audio and 
screen recordings will be made of the interviews where appropriate with participant consent. 
Screen recording tools may include eye tracking. We may record the screen, eye movements, 
and audio to analyze the app use by the participant. Audio-recordings may be transcribed. 
  
  
Aim 2. Implementation 
  

 
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Provider and Staff Participation in Pilot Implementation: To account for real-world 
conditions, the interventions will be pilot tested by providers and staff who volunteer to serve 
as pilot users or provide care related to lung cancer screening for the targeted patient 
population at a pilot clinic. 
  
Patient Participation in Pilot Implementation: Patients will be included in the pilot 
implementation if they meet the pilot implementation inclusion criteria as described earlier. 
  
Provider and Staff Participation in Clinical Trial: The project team will work with leaders in 
primary care at the University of Utah to identify how best to incorporate the intervention into 
clinical workflows. Based on these discussions and following assent by the clinical leaders, we 
anticipate that we will visit each clinic to describe the intervention, conduct training, and 
answer questions. It is anticipated that usual clinical staff meetings will be leveraged for this 
purpose. Providers and designated staff will have access to the intervention in the EHR. 
Providers and staff will be free to follow or disregard the intervention's suggestions as they see 
fit. 
  
There will not be randomization in this study. The intervention will be rolled out systemwide. 
  
The intervention will consist of the following core items: 
  
- An EHR-integrated shared decision making (SDM) tool for providing information on the 
risks and benefits of lung cancer screening through low-dose computed tomography (LDCT) 
testing. An initial version of this tool (Decision Precision+) was previously approved for 
operational clinical use outside of this research study. A current version of the standalone 
version of the SDM tool is available at https://share.lungdecisionprecision.com/. 
  
- Reminders in the EHR to offer SDM for LDCT for patients eligible for lung cancer screening 
according to USPSTF guidelines. Epic "Health Maintenance" reminders for this purpose were 
also previously approved for operational clinical care through usual clinical governance 
channels. Additional reminders may be provided using other EHR-based mechanisms, 
including through an Epic-integrated disease management and health maintenance system that 
is also already available in the Epic EHR system at the University of Utah. 
  

 
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Based on feedback received from users, it is anticipated that at least one cycle of improvement 
will be released into the intervention during the course of the pragmatic implementation trial. 
For study power analysis purposes, we anticipate one improvement release approximately 9 
months into the trial, with the trial anticipated to last 18 months. We anticipate the intervention 
will remain as a part of routine clinical care following the trial. In addition to these major 
enhancements, less significant updates or bug fixes, if any, will be released as clinically 
required. All major enhancements will be introduced with the support and approval of the 
appropriate governance group, such as the Community Physicians Group Medical Directors or 
the EHR Operations committee. While the nature of the major intervention enhancements will 
be dependent on user feedback, we currently anticipate that one or more of the following will 
be introduced as the enhancements: 
  
- Providing eligible patients with the recommendation to undergo shared decision making for 
lung cancer screening. This is already done for many USPSTF recommended preventive care 
reminders. We anticipate that we will provide these care reminders through the Epic MyChart 
personal health record portal. 
  
- Introducing Epic Health Maintenance prompts for medical assistants to collect detailed 
smoking history for patients who may be eligible for screening (age 55 to 80 with a history of 
smoking and no lung cancer diagnosis) but who lack the detailed smoking history data 
required to make a complete determination (e.g., pack-years of smoking and years since quit 
smoking). 
  
- Facilitating patient education outside of the clinic visit, e.g., through printing out the shared 
decision making materials during the visit, for patient review prior to a follow-up discussion 
with the provider at a subsequent visit. 
  
Following the roll-out, clinic leaders may receive periodic feedback on use of the intervention 
and LDCT screening rates, including comparisons to other clinics. Clinic leaders may be given 
user-level data so that outlier performances can be addressed if desired. Activities for 
facilitating implementation may include regular email, phone, or in-person communication 
with clinic leaders to discuss CDS adoption and address barriers to adoption; identify 
“implementation pearls”; and share these “pearls” with other clinics. We and others have 
shown that such facilitation activities improve the adoption and impact of practice change 
interventions. Contact frequency will be determined by workflow adoption, with more 
intensive follow-up directed to clinics with lower adoption rates. We will maintain a log of 
interactions to identify common issues, and issues will be prioritized and addressed. 
  

 
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Patient Participation in Clinical Trial: Patients will be included in the clinical trial if they 
meet the clinical trial inclusion criteria described earlier. 
  
Post-Implementation Provider and Staff Interviews: Early formative interviews will be 
conducted with care team members at the pilot clinic, and potentially other intervention 
clinics, as the users respond to the intervention. The purpose is to examine impact on 
workflow, uncover any usability problems, refine implementation procedures, and identify any 
training needs. Interviews are expected to take approximately 45 minutes to 1 hour. Audio and 
screen recordings will be made of the interviews where appropriate with participant consent. 
Audio-recordings may be transcribed. Screen recording tools may include eye tracking. Some 
screen captures may include actual patient data. We may record the screen, eye movements, 
and audio to analyze intervention use by the participant for later analysis or for participants to 
comment on their thought process as they review their own recordings. 
  
  
  
  
  
  
Aim 3. Evaluation 
  
Patient Outcomes Assessment: Data for analysis will be extracted from the University of Utah 
data warehouse and system logs. Outcome measures are described under section 9 below. A 
PhD-level statistician co-investigator will oversee the statistical analyses. Patients for whom 
the intervention was used prior to the clinical trial will be excluded from the main clinical trial 
analyses. 
  
Provider and Staff App Usage Assessment: The same approach as for the Patient Outcomes 
Assessment will be used. 
  
Provider and Staff Surveys: Providers and staff will be surveyed regarding their satisfaction 
with the intervention using the System Usability Scale (SUS), with supplemental questions on 
barriers and facilitators to usage as well as questions on the impact on workflow and 

 
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workload. We will also ask whether respondents are interested in the focus groups described 
next. Providers and staff will be surveyed electronically, with invitations sent via email. 
  
Patient Surveys: Patients will be surveyed electronically and/or on paper with invitations, 
surveys, and pre-paid return envelopes sent by mail. Patients will be surveyed regarding their 
screening preferences, knowledge, preference for SDM, decisional conflict, and satisfaction 
with provider communication. We will also ask whether respondents are interested in the 
focus groups described next. 
  
Provider and Staff Focus Groups: The focus groups will evaluate the intervention specifically, 
as well as explore the implementation and adoption issues with CDS, SDM, and guideline-
focused care in general. Methods suggested by Krueger and associates will be utilized, 
including a prepared script (Introduction, Overview, Ground Rules, and Questions), use of 
non-threatening group processes, and homogenous groups. We will focus on facilitators and 
barriers to CDS adoption. Each focus group is expected to take 1-1.5 hours. Discussions will 
be recorded and may be transcribed. 
  
Patient Focus Groups: The same methods as for the Provider and Staff Focus Groups will be 
used. 
  
Stakeholder Interviews: We will interview key stakeholders at each of the implementation 
sites, including dissemination sites in other healthcare systems, with a goal of understanding 
barriers and facilitators to the scalable dissemination of evidence-based CDS, as well as to 
estimate the associated time and resource costs. To facilitate cost estimation, stakeholders will 
be asked to keep track of significant resource expenditures. 
  
Data Quality Assurance: Because the intervention is promoting standard of care, we will base 
our approach on how University of Utah Health operationally introduces other health IT 
interventions to promote standard of care. As part of standard quality assurance procedures, 
we will assess all adults who had an outpatient visit in University of Utah Health primary care. 
Data for smokers would be analyzed to find inaccuracies and new estimates for lung cancer 
risk and eligibility for lung cancer will be produced. 
  
Our grant application matches the ERICA application in the following areas: study design, 
study population, study objectives and goals, and study test interventions and procedures. 
Surveys, interview guides, and focus group guides have been uploaded in the Document and 

 
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Attachment section.  In case of any edits, all of these materials will be submitted to the IRB 
via amendment for review and approval before they are used with participants. 
 
 
Procedures performed for research purposes only:  
 
 
 
Statistical Methods, Data Analysis and Interpretation 
 
Aim 1. Design 
  
Pre-Intervention Provider and Staff Interviews: Records will be analyzed using qualitative 
analysis software (such as ATLAS or NVivo), which can integrate transcripts, pictures, memos 
and other materials. We will use procedures recommended by Patton and others that focus on 
developing coding protocols to highlight issues, problems, and potential recommendations. 
  
Aim 2. Implementation 
  
Pilot Implementation and Clinical Trial: see below for analyses for the pilot implementation 
and clinical trial. 
  
Post-Implementation Provider and Staff Interviews: The purpose is to examine impact on 
workflow, uncover any usability problems, refine implementation procedures, and identify any 
training needs. We will use qualitative analyses procedures similar to Aim 1. 
  
  
Aim 3. Evaluation 
  
Patient Outcomes Assessment: The primary outcome is the proportion of patients eligible for 
LDCT screening per USPSTF guidelines receiving LDCT screening. Planned secondary 
outcomes are the expected number of lung cancer deaths prevented given the risk profiles of 
individuals screened with LDCT, the expected number of major complications given the risk 
profiles of individuals screened with LDCT, and, if approved by the University of Utah Value 
Driven Outcomes research committee, the operating margin attributable to study patients 
during study period (total revenues minus total cost). Data for the operating margin analysis 

 
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will leverage the Value Driven Outcomes value analysis tool co-developed by PI Kawamoto to 
determine the true costs of care. 
  
We will report the number of patients and patient characteristics for each phase of the 
interrupted time series (ITS) study. We anticipate a 12-month baseline phase, a 9-month 
intervention phase, and a 9-month enhanced intervention phase which incorporates 
improvements made to the CDS intervention based on initial experience. We will conduct a 
segmented regression analysis to fit the monthly series and assess the extent to which the 
intervention was associated with changes in outcome measures. The study phases constitute 
three segments of regression models with monthly time points. We will use the segmented 
least squares approach with parameters for intercept, baseline trend, and changes in the level 
and trend after the intervention. We will start with assuming a linear trend line within each 
segment. Nonlinearity trends will be explored if the linear assumption is violated. We will use 
the Durbin Watson statistic as a measure of autocorrelation and test up to six-order 
autocorrelation. Potential confounding is limited to covariates associated with the outcome that 
change at the time of the intervention. We will test for confounding due to changes in study 
population characteristics such as age, gender and race. Statistical significance will be defined 
at alpha = 0.05. 
  
To mitigate the impact of pandemic COVID19, we will make the following two modifications 
in the statistical analysis plan. First, we will redefine the baseline phase so that it has no 
overlap with the lockdown period, because all the non-essential medical visits have been 
cancelled during this period. Secondly, we anticipate the patient characteristics and 
composition during the intervention phase and even enhanced intervention phase might be 
different from the baseline phase which is observed before the pandemic. We will compare the 
distributions of patient characteristics across the three phases of the ITS study. If significant 
discrepancies are observed in the patient characteristics and composition, we will conduct 
propensity score modeling and use a matching or weighting approach to create a comparable 
study cohort in each phase before running the originally proposed segmented regression 
analysis. If any lock-down periods coincide with the intervention phases, we may remove 
visits occurring during these periods from the analysis. 
  
  
To estimate statistical power, we followed Rozario, Moore and McWilliams’ approach, and 
used the statistical software SAS. We assumed the proportion of eligible patients receiving 
LDCT screening, the primary outcome in the study, will increase from the current 6.4% to at 
least 12.8% during the intervention phase and 20% during the enhanced intervention phase. 
Empirical EHR data indicate at least 900 patients per month will meet USPSTF LDCT 
eligibility criteria throughout the study period. With this conservative monthly sample 
estimation and the length of time at each phase, we would have more than 95% power to detect 

 
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the hypothesized differences in the primary outcomes at different phases. All the estimated 
statistical powers were calculated based on two-sided tests with a significance level of alpha = 
0.05. 
  
  
  
Provider and Staff App Usage Assessment: Utilization metrics will include the number of 
clinics, providers, and other empowered staff utilizing the intervention. 
  
Provider and Staff Surveys: Descriptive data analysis will be conducted and aggregated data 
will be compared across groups. 
  
Patient Surveys: The same methods will be used as for the provider and staff surveys. 
  
Provider and Staff Focus Groups: Records will be analyzed using qualitative analysis software 
(such as ATLAS or NVivo) which can integrate transcripts, pictures, memos and other 
materials. We will use procedures recommended by Patton and others that focus on developing 
coding protocols to highlight issues, problems, and potential recommendations. 
  
Patient Focus Groups: The same methods will be used as for the provider and staff focus 
groups. 
  
Stakeholder Interviews: The same methods will be used as for the provider and staff focus 
groups. 
  
Data Quality Assurance: Data for smokers would be analyzed to find inaccuracies and new 
estimates for lung cancer risk and eligibility for lung cancer will be produced. We will use 
smoking history data entered in the EHR in the designated structured fields. Detailed smoking 
history could be recorded in 5 fields: smoking status, packs per day, years smoked, smoking 
start date and smoking quit date. In our system, which uses the default EHR configuration for 
smoking history documentation, smoking start date is usually missing as it can only be entered 
by patients through the patient portal. More granular smoking status data will be classified into 
current and former smoker categories. We will aim to develop an algorithm which could be 

 
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used in the future to more accurately predict eligibility for lung cancer screening. Intermediary 
evaluation measures for this algorithm will include whether patients have sufficient data to 
calculate eligibility, pack-years and years since quit on 2021/01/01.