What Goes into Patient Selection for Lung Cancer Screening? Factors Associated with Clinician Judgments of Suitability for Screening.

Rationale: Achieving the net benefit of lung cancer screening (LCS) depends on optimizing patient selection. Objective: To identify factors associated with clinician assessments that a patient was unlikely to benefit from LCS ("LCS-inappropriate") because of comorbidities or limited life expectancy. Methods: Retrospective analysis of patients assessed for LCS at 30 Veterans Health Administration facilities from January 1, 2015 to February 1, 2021. We conducted hierarchical mixed-effects logistic regression analyses to determine factors associated with clinicians' designations of LCS inappropriateness (primary outcome), accounting for 3-year predicted probability (i.e., competing risk) of non-lung cancer death. Measurements and Main Results: Among 38,487 LCS-eligible patients, 1,671 (4.3%) were deemed LCS-inappropriate by clinicians, whereas 4,383 (11.4%) had an estimated 3-year competing risk of non-lung cancer death greater than 20%. Patients with higher competing risks of non-lung cancer death were more likely to be deemed LCS-inappropriate (odds ratio [OR], 2.66; 95% confidence interval [CI], 2.32-3.05). Older patients (ages 75-80; OR, 1.45; 95% CI, 1.18-1.78) and those with interstitial lung disease (OR, 1.98; 95% CI, 1.51-2.59) were more likely to be deemed LCS-inappropriate than would be explained by competing risk of non-lung cancer death, whereas patients currently smoking (OR, 0.65; 95% CI, 0.58-0.73) were less likely to be deemed LCS-inappropriate, suggesting that clinicians over- or underweighted these factors. The probability of being deemed LCS-inappropriate varied from 0.4% to 74%, depending on the clinician making the assessment (median OR, 3.07; 95% CI, 2.89-3.25). Conclusion: Concerningly, the likelihood that a patient is deemed LCS-inappropriate is more strongly associated with the clinician making the assessment than with patient characteristics. Patient selection may be optimized by providing decision support to help clinicians assess net LCS benefit.

Tackling Guideline Non-concordance: Primary Care Barriers to Incorporating Life Expectancy into Lung Cancer Screening Decision-Making-A Qualitative Study.

BACKGROUND: Primary care providers (PCPs) are often the first point of contact for discussing lung cancer screening (LCS) with patients. While guidelines recommend against screening people with limited life expectancy (LLE) who are less likely to benefit, these patients are regularly referred for LCS. OBJECTIVE: We sought to understand barriers PCPs face to incorporating life expectancy into LCS decision-making for patients who otherwise meet eligibility criteria, and how a hypothetical point-of-care tool could support patient selection. DESIGN: Qualitative study based on semi-structured telephone interviews. PARTICIPANTS: Thirty-one PCPs who refer patients for LCS, from six Veterans Health Administration facilities. APPROACH: We thematically analyzed interviews to understand how PCPs incorporated life expectancy into LCS decision-making and PCPs' receptivity to a point-of-care tool to support patient selection. Final themes were organized according to the Cabana et al. framework Why Don't Physicians Follow Clinical Practice Guidelines, capturing the influence of clinician knowledge, attitudes, and behavior on LCS appropriateness determinations. KEY RESULTS: PCP referrals to LCS for patients with LLE were influenced by limited knowledge of the life expectancy threshold at which patients are less likely to benefit from LCS, discomfort estimating life expectancy, fear of missing cancer at the point of early detection, and prioritization of factors such as quality of life, patient values, clinician-patient relationship, and family support. PCPs were receptive to a decision support tool to inform and communicate LCS appropriateness decisions if easy to use and integrated into clinical workflows. CONCLUSIONS: Our study suggests knowledge gaps and attitudes may drive decisions to offer screening despite LLE, a behavior counter to guideline recommendations. Integrating a LCS decision support tool that incorporates life expectancy within the electronic medical record and existing clinical workflows may be one acceptable solution to improve guideline concordance and increase confidence in selecting high benefit patients for LCS.

"It Can't Hurt!": Why Many Patients With Limited Life Expectancy Decide to Accept Lung Cancer Screening.

PURPOSE: Lung cancer screening (LCS) has less benefit and greater potential for iatrogenic harm among people with multiple comorbidities and limited life expectancy. Yet, such individuals are more likely to undergo screening than healthier LCS-eligible people. We sought to understand how patients with marginal LCS benefit conceptualize their health and make decisions regarding LCS. METHODS: We interviewed 40 people with multimorbidity and limited life expectancy, as determined by high Care Assessment Need scores, which predict 1-year risk of hospitalization or death. Patients were recruited from 6 Veterans Health Administration facilities after discussing LCS with their clinician. We conducted a thematic analysis using constant comparison to explore factors that influence LCS decision making. RESULTS: Patients commonly held positive beliefs about screening and perceived LCS to be noninvasive. When posed with hypothetical scenarios of limited benefit, patients emphasized the nonlongevity benefits of LCS (eg, peace of mind, planning for the future) and generally did not consider their health status or life expectancy when making decisions regarding LCS. Most patients were unaware of possible additional evaluations or treatment of screen-detected findings, but when probed further, many expressed concerns about the potential need for multiple evaluations, referrals, or invasive procedures. CONCLUSIONS: Patients in this study with multimorbidity and limited life expectancy were unaware of their greater risk of potential harm when accepting LCS. Given patient trust in clinician recommendations, it is important that clinicians engage patients with marginal LCS benefit in shared decision making, ensuring that their values of desiring more information about their health are weighed against potential harms from further evaluations.

Engaging Operational Partners Is Critical for Successful Implementation of Research Products: a Coincidence Analysis of Access-Related Projects in the Veterans Affairs Healthcare System.

BACKGROUND/OBJECTIVE: The Veterans Health Administration (VHA) has prioritized timely access to care and has invested substantially in research aimed at optimizing veteran access. However, implementing research into practice remains challenging. Here, we assessed the implementation status of recent VHA access-related research projects and explored factors associated with successful implementation. DESIGN: We conducted a portfolio review of recent VHA-funded or supported projects (1/2015-7/2020) focused on healthcare access ("Access Portfolio"). We then identified projects with implementable research deliverables by excluding those that (1) were non-research/operational projects; (2) were only recently completed (i.e., completed on or after 1/1/2020, meaning that they were unlikely to have had time to be implemented); and (3) did not propose an implementable deliverable. An electronic survey assessed each project's implementation status and elicited barriers/facilitators to implementing deliverables. Results were analyzed using novel Coincidence Analysis (CNA) methods. PARTICIPANTS/KEY RESULTS: Among 286 Access Portfolio projects, 36 projects led by 32 investigators across 20 VHA facilities were included. Twenty-nine respondents completed the survey for 32 projects (response rate = 88.9%). Twenty-eight percent of projects reported fully implementing project deliverables, 34% reported partially implementing deliverables, and 37% reported not implementing any deliverables (i.e., resulting tool/intervention not implemented into practice). Of 14 possible barriers/facilitators assessed in the survey, two were identified through CNA as "difference-makers" to partial or full implementation of project deliverables: (1) engagement with national VHA operational leadership; (2) support and commitment from local site operational leadership. CONCLUSIONS: These findings empirically highlight the importance of operational leadership engagement for successful implementation of research deliverables. Efforts to strengthen communication and engagement between the research community and VHA local/national operational leaders should be expanded to ensure VHA's investment in research leads to meaningful improvements in veterans' care. The Veterans Health Administration (VHA) has prioritized timely access to care and has invested substantially in research aimed at optimizing veteran access. However, implementing research findings into clinical practice remains challenging, both within and outside VHA. Here, we assessed the implementation status of recent VHA access-related research projects and explored factors associated with successful implementation. Only two factors were identified as "difference-makers" to adoption of project findings into practice: (1) engagement with national VHA leadership or (2) support and commitment from local site leadership. These findings highlight the importance of leadership engagement for successful implementation of research findings. Efforts to strengthen communication and engagement between the research community and VHA local/national leaders should be expanded to ensure VHA's investment in research leads to meaningful improvements in veterans' care.

Stakeholder Research Priorities to Promote Implementation of Shared Decision-Making for Lung Cancer Screening: An American Thoracic Society and Veterans Affairs Health Services Research and Development Statement.

Rationale: Shared decision-making (SDM) for lung cancer screening (LCS) is recommended in guidelines and required by Medicare, yet it is seldom achieved in practice. The best approach for implementing SDM for LCS remains unknown, and the 2021 U.S. Preventive Services Task Force calls for implementation research to increase uptake of SDM for LCS. Objectives: To develop a stakeholder-prioritized research agenda and recommended outcomes to advance implementation of SDM for LCS. Methods: The American Thoracic Society and VA Health Services Research and Development Service convened a multistakeholder committee with expertise in SDM, LCS, patient-centered care, and implementation science. During a virtual State of the Art conference, we reviewed evidence and identified research questions to address barriers to implementing SDM for LCS, as well as outcome constructs, which were refined by writing group members. Our committee (n = 34) then ranked research questions and SDM effectiveness outcomes by perceived importance in an online survey. Results: We present our committee's consensus on three topics important to implementing SDM for LCS: 1) foundational principles for the best practice of SDM for LCS; 2) stakeholder rankings of 22 implementation research questions; and 3) recommended outcomes, including Proctor's implementation outcomes and stakeholder rankings of SDM effectiveness outcomes for hybrid implementation-effectiveness studies. Our committee ranked questions that apply innovative implementation approaches to relieve primary care providers of the sole responsibility of SDM for LCS as highest priority. We rated effectiveness constructs that capture the patient experience of SDM as most important. Conclusions: This statement offers a stakeholder-prioritized research agenda and outcomes to advance implementation of SDM for LCS.

Applying User-Centered Design to Develop Practical Strategies that Address Overuse in Primary Care.

INTRODUCTION: Engaging patients and frontline clinicians in re-designing clinical care is essential for improving care delivery in a complex clinical environment. This study sought to assess an innovative user-centered design approach to improving clinical care quality, focusing on the use cases of de-intensifying non-beneficial care within the following areas: (1) de-intensifying diabetes treatment in high-risk patients; (2) stopping screening for carotid artery stenosis in asymptomatic patients; and (3) stopping colorectal cancer screening in average-risk, older adults. METHODS: The user-centered design approach, consisting of patient and patient-clinician charrettes (defined as intensive workshops where key stakeholders collaborate to develop creative solutions to a specific problem) and participant surveys, has been described previously. Following the charrettes, we used inductive coding to identify and categorize themes emerging from the de-intensification ideas prioritized by participants as well as facilitator notes and audio recordings from the charrettes. RESULTS: Thirty-five patients participated in the patient design charrettes, generating 134 unique de-intensification ideas and prioritizing 32, which were then distilled into six patient-generated principles of de-intensification by the study team. These principles provided a starting point for a subsequent patient-clinician charrette. In this follow-up charrette, 9 patients who had participated in an earlier patient design charrette collaborated with 7 clinicians to generate 63 potential de-intensification solutions. Six of these potential solutions were developed into multi-faceted, fully operationalized de-intensification strategies. DISCUSSION: The de-intensification strategies that patients and clinicians prioritized and operationalized during the co-design charrette process were detailed and multi-faceted. Each component of a strategy had a rationale based on feasibility, practical considerations, and ways of overcoming barriers. The charrette-based process may be a useful way to engage clinicians and patients in developing the complex and multi-faceted strategies needed to improve care delivery.

A survey of internists' recommendations for aspirin in older adults and barriers to evidence-based use.

Recent trials suggest that aspirin for primary prevention may do more harm than good for some, including adults over 70 years of age. We sought to assess how primary care providers (PCPs) use aspirin for the primary prevention in older patients and to identify barriers to use according to recent guidelines, which recommend against routine use in patients over age 70. We surveyed PCPs about whether they would recommend aspirin in clinical vignettes of a 75-year-old patient with a 10-year atherosclerotic cardiovascular disease risk of 25%. We also queried perceived difficulty following guideline recommendations, as well as perceived barriers and facilitators. We obtained responses from 372 PCPs (47.9% response). In the patient vignette, 45.4% of clinicians recommended aspirin use, which did not vary by whether the patient was using aspirin initially (p = 0.21); 41.7% believed aspirin was beneficial. Perceived barriers to guideline-based aspirin use included concern about patients being upset (41.6%), possible malpractice claims (25.0%), and not having a strategy for discussing aspirin use (24.5%). The estimated adjusted probability of rating the guideline as "hard to follow" was higher in clinicians who believed aspirin was beneficial (29.4% vs. 8.0%; p < 0.001) and who worried the patient would be upset if told to stop aspirin (26.7% vs. 12.5%; p = 0.001). Internists vary considerably in their recommendations for aspirin use for primary prevention in older patients. A high proportion of PCPs continue to believe aspirin is beneficial in this setting. These results can inform de-implementation efforts to optimize evidence-based aspirin use.

Planning and Reporting Effective Web-Based RAND/UCLA Appropriateness Method Panels: Literature Review and Preliminary Recommendations.

BACKGROUND: The RAND/UCLA Appropriateness Method (RAM), a variant of the Delphi Method, was developed to synthesize existing evidence and elicit the clinical judgement of medical experts on the appropriate treatment of specific clinical presentations. Technological advances now allow researchers to conduct expert panels on the internet, offering a cost-effective and convenient alternative to the traditional RAM. For example, the Department of Veterans Affairs recently used a web-based RAM to validate clinical recommendations for de-intensifying routine primary care services. A substantial literature describes and tests various aspects of the traditional RAM in health research; yet we know comparatively less about how researchers implement web-based expert panels. OBJECTIVE: The objectives of this study are twofold: (1) to understand how the web-based RAM process is currently used and reported in health research and (2) to provide preliminary reporting guidance for researchers to improve the transparency and reproducibility of reporting practices. METHODS: The PubMed database was searched to identify studies published between 2009 and 2019 that used a web-based RAM to measure the appropriateness of medical care. Methodological data from each article were abstracted. The following categories were assessed: composition and characteristics of the web-based expert panels, characteristics of panel procedures, results, and panel satisfaction and engagement. RESULTS: Of the 12 studies meeting the eligibility criteria and reviewed, only 42% (5/12) implemented the full RAM process with the remaining studies opting for a partial approach. Among those studies reporting, the median number of participants at first rating was 42. While 92% (11/12) of studies involved clinicians, 50% (6/12) involved multiple stakeholder types. Our review revealed that the studies failed to report on critical aspects of the RAM process. For example, no studies reported response rates with the denominator of previous rounds, 42% (5/12) did not provide panelists with feedback between rating periods, 50% (6/12) either did not have or did not report on the panel discussion period, and 25% (3/12) did not report on quality measures to assess aspects of the panel process (eg, satisfaction with the process). CONCLUSIONS: Conducting web-based RAM panels will continue to be an appealing option for researchers seeking a safe, efficient, and democratic process of expert agreement. Our literature review uncovered inconsistent reporting frameworks and insufficient detail to evaluate study outcomes. We provide preliminary recommendations for reporting that are both timely and important for producing replicable, high-quality findings. The need for reporting standards is especially critical given that more people may prefer to participate in web-based rather than in-person panels due to the ongoing COVID-19 pandemic.

Dealing with the Lack of Time for Detailed Shared Decision-making in Primary Care: Everyday Shared Decision-making.

Policymakers and researchers are strongly encouraging clinicians to support patient autonomy through shared decision-making (SDM). In setting policies for clinical care, decision-makers need to understand that current models of SDM have tended to focus on major decisions (e.g., surgeries and chemotherapy) and focused less on everyday primary care decisions. Most decisions in primary care are substantive everyday decisions: intermediate-stakes decisions that occur dozens of times every day, yet are non-trivial for patients, such as whether routine mammography should start at age 40, 45, or 50. Expectations that busy clinicians use current models of SDM (here referred to as "detailed" SDM) for these decisions can feel overwhelming to clinicians. Evidence indicates that detailed SDM is simply not realistic for most of these decisions and without a feasible alternative, clinicians usually default to a decision-making approach with little to no personalization. We propose, for discussion and refinement, a compromise approach to personalizing these decisions (everyday SDM). Everyday SDM is based on a feasible process for supporting patient autonomy that also allows clinicians to continue being respectful health advocates for their patients. We propose that alternatives to detailed SDM are needed to make progress toward more patient-centered care.

Incorporating Coexisting Chronic Illness into Decisions about Patient Selection for Lung Cancer Screening. An Official American Thoracic Society Research Statement.

BACKGROUND: Lung cancer screening (LCS) has the potential to reduce the risk of lung cancer death in healthy individuals, but the impact of coexisting chronic illnesses on LCS outcomes has not been well defined. Consideration of the complex relationship between baseline risk of lung cancer, treatment-related harms, and risk of death from competing causes is crucial in determining the balance of benefits and harms of LCS. OBJECTIVES: To summarize evidence, identify knowledge and research gaps, prioritize topics, and propose methods for future research on how best to incorporate comorbidities in making decisions regarding LCS. METHODS: A multidisciplinary group of international clinicians and researchers reviewed available data on the effects of comorbidities on LCS outcomes, focusing on the juxtaposition of lung cancer risk and competing risks of death, consideration of benefits and risks in patients with chronic obstructive pulmonary disease, communication of risk, and treatment of screen-detected lung cancer. RESULTS: This statement identifies gaps in knowledge regarding how comorbidities and competing causes of death impact outcomes in LCS, and we have developed questions to help guide future research efforts to better inform patient selection, education, and implementation of LCS. CONCLUSIONS: There is an urgent need for further research that can help guide clinical decision-making with patients who may not benefit from LCS owing to coexisting chronic illness. This statement establishes a research framework to address essential questions regarding how to incorporate and communicate risks of comorbidities into patient selection and decisions regarding LCS.

Identifying Patients for Whom Lung Cancer Screening Is Preference-Sensitive: A Microsimulation Study.

Background: Many health systems are exploring how to implement low-dose computed tomography (LDCT) screening programs that are effective and patient-centered. Objective: To examine factors that influence when LDCT screening is preference-sensitive. Design: State-transition microsimulation model. Data Sources: Two large randomized trials, published decision analyses, and the SEER (Surveillance, Epidemiology, and End Results) cancer registry. Target Population: U.S.-representative sample of simulated patients meeting current U.S. Preventive Services Task Force criteria for screening eligibility. Time Horizon: Lifetime. Perspective: Individual. Intervention: LDCT screening annually for 3 years. Outcome Measures: Lifetime quality-adjusted life-year gains and reduction in lung cancer mortality. To examine the effect of preferences on net benefit, disutilities (the "degree of dislike") quantifying the burden of screening and follow-up were varied across a likely range. The effect of varying the rate of false-positive screening results and overdiagnosis associated with screening was also examined. Results of Base-Case Analysis: Moderate differences in preferences about the downsides of LDCT screening influenced whether screening was appropriate for eligible persons with annual lung cancer risk less than 0.3% or life expectancy less than 10.5 years. For higher-risk eligible persons with longer life expectancy (roughly 50% of the study population), the benefits of LDCT screening overcame even highly negative views about screening and its downsides. Results of Sensitivity Analysis: Rates of false-positive findings and overdiagnosed lung cancer were not highly influential. Limitation: The quantitative thresholds that were identified may vary depending on the structure of the microsimulation model. Conclusion: Identifying circumstances in which LDCT screening is more versus less preference-sensitive may help clinicians personalize their screening discussions, tailoring to both preferences and clinical benefit. Primary Funding Source: None.

Prediction-Augmented Shared Decision-Making and Lung Cancer Screening Uptake.

Importance: Addressing poor uptake of low-dose computed tomography lung cancer screening (LCS) is critical, especially for those having the most to gain-high-benefit persons with high lung cancer risk and life expectancy more than 10 years. Objective: To assess the association between LCS uptake and implementing a prediction-augmented shared decision-making (SDM) tool, which enables clinicians to identify persons predicted to be at high benefit and encourage LCS more strongly for these persons. Design, Setting, and Participants: Quality improvement interrupted time series study at 6 Veterans Affairs sites that used a standard set of clinical reminders to prompt primary care clinicians and screening coordinators to engage in SDM for LCS-eligible persons. Participants were persons without a history of LCS who met LCS eligibility criteria at the time (aged 55-80 years, smoked ≥30 pack-years, and current smoking or quit <15 years ago) and were not documented to be an inappropriate candidate for LCS by a clinician during October 2017 through September 2019. Data were analyzed from September to November 2023. Exposure: Decision support tool augmented by a prediction model that helps clinicians personalize SDM for LCS, tailoring the strength of screening encouragement according to predicted benefit. Main outcome and measure: LCS uptake. Results: In a cohort of 9904 individuals, the median (IQR) age was 64 (57-69) years; 9277 (94%) were male, 1537 (16%) were Black, 8159 (82%) were White, 5153 (52%) were predicted to be at intermediate (preference-sensitive) benefit and 4751 (48%) at high benefit, and 1084 (11%) received screening during the study period. Following implementation of the tool, higher rates of LCS uptake were observed overall along with an increase in benefit-based LCS uptake (higher screening uptake among persons anticipated to be at high benefit compared with those at intermediate benefit; primary analysis). Mean (SD) predicted probability of getting screened for a high-benefit person was 24.8% (15.5%) vs 15.8% (11.8%) for a person at intermediate benefit (mean absolute difference 9.0 percentage points; 95% CI, 1.6%-16.5%). Conclusions and Relevance: Implementing a robust approach to personalized LCS, which integrates SDM, and a decision support tool augmented by a prediction model, are associated with improved uptake of LCS and may be particularly important for those most likely to benefit. These findings are timely given the ongoing poor rates of LCS uptake.

Clinician Perceptions on Using Decision Tools to Support Prediction-Based Shared Decision Making for Lung Cancer Screening.

Background: Considering a patient's full risk factor profile can promote personalized shared decision making (SDM). One way to accomplish this is through encounter tools that incorporate prediction models, but little is known about clinicians' perceptions of the feasibility of using these tools in practice. We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS). Design: We conducted a qualitative study based on field notes from academic detailing visits during a multisite quality improvement program. The detailer engaged one-on-one with 96 primary care clinicians across multiple Veterans Affairs sites (7 medical centers and 6 outlying clinics) to get feedback on 1) the rationale for prediction-based LCS and 2) how to use the DecisionPrecision (DP) encounter tool with eligible patients to personalize LCS discussions. Results: Thematic content analysis from detailing visit data identified 6 categories of clinician willingness to use the DP tool to personalize SDM for LCS (adoption potential), varying from "Enthusiastic Potential Adopter" (n = 18) to "Definite Non-Adopter" (n = 16). Many clinicians (n = 52) articulated how they found the concept of prediction-based SDM highly appealing. However, to varying degrees, nearly all clinicians identified challenges to incorporating such an approach in routine practice. Limitations: The results are based on the clinician's initial reactions rather than longitudinal experience. Conclusions: While many primary care clinicians saw real value in using prediction to personalize LCS decisions, more support is needed to overcome barriers to using encounter tools in practice. Based on these findings, we propose several strategies that may facilitate the adoption of prediction-based SDM in contexts such as LCS. Highlights: Encounter tools that incorporate prediction models promote personalized shared decision making (SDM), but little is known about clinicians' perceptions of the feasibility of using these tools in practice.We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS).While many clinicians found the concept of prediction-based SDM highly appealing, nearly all clinicians identified challenges to incorporating such an approach in routine practice.We propose several strategies to overcome adoption barriers and facilitate the use of prediction-based SDM in contexts such as LCS.

Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention: A Nonrandomized Controlled Trial.

Importance: Lung cancer is the deadliest cancer in the US. Early-stage lung cancer detection with lung cancer screening (LCS) through low-dose computed tomography (LDCT) improves outcomes. Objective: To assess the association of a multifaceted clinical decision support intervention with rates of identification and completion of recommended LCS-related services. Design, Setting, and Participants: This nonrandomized controlled trial used an interrupted time series design, including 3 study periods from August 24, 2019, to April 27, 2022: baseline (12 months), period 1 (11 months), and period 2 (9 months). Outcome changes were reported as shifts in the outcome level at the beginning of each period and changes in monthly trend (ie, slope). The study was conducted at primary care and pulmonary clinics at a health care system headquartered in Salt Lake City, Utah, among patients aged 55 to 80 years who had smoked 30 pack-years or more and were current smokers or had quit smoking in the past 15 years. Data were analyzed from September 2023 through February 2024. Interventions: Interventions in period 1 included clinician-facing preventive care reminders, an electronic health record-integrated shared decision-making tool, and narrative LCS guidance provided in the LDCT ordering screen. Interventions in period 2 included the same clinician-facing interventions and patient-facing reminders for LCS discussion and LCS. Main Outcome and Measure: The primary outcome was LCS care gap closure, defined as the identification and completion of recommended care services. LCS care gap closure could be achieved through LDCT completion, other chest CT completion, or LCS shared decision-making. Results: The study included 1865 patients (median [IQR] age, 64 [60-70] years; 759 female [40.7%]). The clinician-facing intervention (period 1) was not associated with changes in level but was associated with an increase in slope of 2.6 percentage points (95% CI, 2.4-2.7 percentage points) per month in care gap closure through any means and 1.6 percentage points (95% CI, 1.4-1.8 percentage points) per month in closure through LDCT. In period 2, introduction of patient-facing reminders was associated with an immediate increase in care gap closure (2.3 percentage points; 95% CI, 1.0-3.6 percentage points) and closure through LDCT (2.4 percentage points; 95% CI, 0.9-3.9 percentage points) but was not associated with an increase in slope. The overall care gap closure rate was 175 of 1104 patients (15.9%) at the end of the baseline period vs 588 of 1255 patients (46.9%) at the end of period 2. Conclusions and Relevance: In this study, a multifaceted intervention was associated with an improvement in LCS care gap closure. Trial Registration: ClinicalTrials.gov Identifier: NCT04498052.

Methods for Using Race and Ethnicity in Prediction Models for Lung Cancer Screening Eligibility.

Importance: Using race and ethnicity in clinical prediction models can reduce or inadvertently increase racial and ethnic disparities in medical decisions. Objective: To compare eligibility for lung cancer screening in a contemporary representative US population by refitting the life-years gained from screening-computed tomography (LYFS-CT) model to exclude race and ethnicity vs a counterfactual eligibility approach that recalculates life expectancy for racial and ethnic minority individuals using the same covariates but substitutes White race and uses the higher predicted life expectancy, ensuring that historically underserved groups are not penalized. Design, Setting, and Participants: The 2 submodels composing LYFS-CT NoRace were refit and externally validated without race and ethnicity: the lung cancer death submodel in participants of a large clinical trial (recruited 1993-2001; followed up until December 31, 2009) who ever smoked (n = 39 180) and the all-cause mortality submodel in the National Health Interview Survey (NHIS) 1997-2001 participants aged 40 to 80 years who ever smoked (n = 74 842, followed up until December 31, 2006). Screening eligibility was examined in NHIS 2015-2018 participants aged 50 to 80 years who ever smoked. Data were analyzed from June 2021 to September 2022. Exposure: Including and removing race and ethnicity (African American, Asian American, Hispanic American, White) in each LYFS-CT submodel. Main Outcomes and Measures: By race and ethnicity: calibration of the LYFS-CT NoRace model and the counterfactual approach (ratio of expected to observed [E/O] outcomes), US individuals eligible for screening, predicted days of life gained from screening by LYFS-CT. Results: The NHIS 2015-2018 included 25 601 individuals aged 50 to 80 years who ever smoked (2769 African American, 649 Asian American, 1855 Hispanic American, and 20 328 White individuals). Removing race and ethnicity from the submodels underestimated lung cancer death risk (expected/observed [E/O], 0.72; 95% CI, 0.52-1.00) and all-cause mortality (E/O, 0.90; 95% CI, 0.86-0.94) in African American individuals. It also overestimated mortality in Hispanic American (E/O, 1.08, 95% CI, 1.00-1.16) and Asian American individuals (E/O, 1.14, 95% CI, 1.01-1.30). Consequently, the LYFS-CT NoRace model increased Hispanic American and Asian American eligibility by 108% and 73%, respectively, while reducing African American eligibility by 39%. Using LYFS-CT with the counterfactual all-cause mortality model better maintained calibration across groups and increased African American eligibility by 13% without reducing eligibility for Hispanic American and Asian American individuals. Conclusions and Relevance: In this study, removing race and ethnicity miscalibrated LYFS-CT submodels and substantially reduced African American eligibility for lung cancer screening. Under counterfactual eligibility, no one became ineligible, and African American eligibility increased, demonstrating the potential for maintaining model accuracy while reducing disparities.

Implementation of Lung Cancer Screening in Primary Care and Pulmonary Clinics: Pragmatic Clinical Trial of Electronic Health Record-Integrated Everyday Shared Decision-Making Tool and Clinician-Facing Prompts.

BACKGROUND: Although low-dose CT (LDCT) scan imaging lung cancer screening (LCS) can reduce lung cancer mortality, it remains underused. Shared decision-making (SDM) is recommended to assess the balance of benefits and harms for each patient. RESEARCH QUESTION: Do clinician-facing electronic health record (EHR) prompts and an EHR-integrated everyday SDM tool designed to support routine incorporation of SDM into primary care improve LDCT scan imaging ordering and completion? STUDY DESIGN AND METHODS: A preintervention and postintervention analysis was conducted in 30 primary care and four pulmonary clinics for visits with patients who met United States Preventive Services Task Force criteria for LCS. Propensity scores were used to adjust for covariates. Subgroup analyses were conducted based on the expected benefit from screening (high benefit vs intermediate benefit), pulmonologist involvement (ie, whether the patient was seen in a pulmonary clinic in addition to a primary care clinic), sex, and race and ethnicity. RESULTS: In the 12-month preintervention phase among 1,090 eligible patients, 77 patients (7.1%) had LDCT scan imaging orders and 48 patients (4.4%) completed screenings. In the 9-month intervention phase among 1,026 eligible patients, 280 patients (27.3%) had LDCT scan imaging orders and 182 patients (17.7%) completed screenings. Adjusted ORs were 4.9 (95% CI, 3.4-6.9; P < .001) and 4.7 (95% CI, 3.1-7.1; P < .001) for LDCT imaging ordering and completion, respectively. Subgroup analyses showed increases in ordering and completion for all patient subgroups. In the intervention phase, the SDM tool was used by 23 of 102 ordering providers (22.5%) and for 69 of 274 patients (25.2%) for whom LDCT scan imaging was ordered and who needed SDM at the time of ordering. INTERPRETATION: Clinician-facing EHR prompts and an EHR-integrated everyday SDM tool are promising approaches to improving LCS in the primary care setting. However, room for improvement remains. As such, further research is warranted. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT04498052; URL: www. CLINICALTRIALS: gov.

Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation.

BACKGROUND: Lung cancer risk and life expectancy vary substantially across patients eligible for low-dose computed tomography lung cancer screening (LCS), which has important consequences for optimizing LCS decisions for different patients. To account for this heterogeneity during decision-making, web-based decision support tools are needed to enable quick calculations and streamline the process of obtaining individualized information that more accurately informs patient-clinician LCS discussions. We created DecisionPrecision, a clinician-facing web-based decision support tool, to help tailor the LCS discussion to a patient's individualized lung cancer risk and estimated net benefit. OBJECTIVE: The objective of our study is to test two strategies for implementing DecisionPrecision in primary care at eight Veterans Affairs medical centers: a quality improvement (QI) training approach and academic detailing (AD). METHODS: Phase 1 comprised a multisite, cluster randomized trial comparing the effectiveness of standard implementation (adding a link to DecisionPrecision in the electronic health record vs standard implementation plus the Learn, Engage, Act, and Process [LEAP] QI training program). The primary outcome measure was the use of DecisionPrecision at each site before versus after LEAP QI training. The second phase of the study examined the potential effectiveness of AD as an implementation strategy for DecisionPrecision at all 8 medical centers. Outcomes were assessed by comparing absolute tool use before and after AD visits and conducting semistructured interviews with a subset of primary care physicians (PCPs) following the AD visits. RESULTS: Phase 1 findings showed that sites that participated in the LEAP QI training program used DecisionPrecision significantly more often than the standard implementation sites (tool used 190.3, SD 174.8 times on average over 6 months at LEAP sites vs 3.5 SD 3.7 at standard sites; P<.001). However, this finding was confounded by the lack of screening coordinators at standard implementation sites. In phase 2, there was no difference in the 6-month tool use between before and after AD (95% CI -5.06 to 6.40; P=.82). Follow-up interviews with PCPs indicated that the AD strategy increased provider awareness and appreciation for the benefits of the tool. However, other priorities and limited time prevented PCPs from using them during routine clinical visits. CONCLUSIONS: The phase 1 findings did not provide conclusive evidence of the benefit of a QI training approach for implementing a decision support tool for LCS among PCPs. In addition, phase 2 findings showed that our light-touch, single-visit AD strategy did not increase tool use. To enable tool use by PCPs, prediction-based tools must be fully automated and integrated into electronic health records, thereby helping providers personalize LCS discussions among their many competing demands. PCPs also need more time to engage in shared decision-making discussions with their patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT02765412; https://clinicaltrials.gov/ct2/show/NCT02765412.