The Development and Performance of Alternative Criteria for Lung Cancer Screening.

BACKGROUND: The recommendation for lung cancer screening (LCS) developed by the U.S. Preventive Services Task Force (USPSTF) may exclude some high-benefit people. OBJECTIVE: To determine whether alternative criteria can identify these high-benefit people. DESIGN: Model-based projections. SETTING: United States. PARTICIPANTS: People from the 1997-2014 National Health Interview Survey (NHIS) to develop alternative criteria using fast-and-frugal tree algorithms and from the 2014-2018 NHIS and the 2022 Behavioral Risk Factor Surveillance System for comparisons of USPSTF criteria versus alternative criteria. MEASUREMENTS: Life-years gained from LCS were estimated using the life-years gained from screening computed tomography (LYFS-CT) model. "High-benefit" was defined as gaining an average of at least 16.2 days of life from 3 annual screenings, which reflects high lung cancer risk and substantial life gains if lung cancer is detected by screening. RESULTS: The final alternative criteria were 1) people who smoked any amount each year for at least 40 years, or 2) people aged 60 to 80 years with at least 40 pack-years of smoking. The USPSTF and alternative criteria selected similar numbers of people for LCS. Compared with the USPSTF criteria, the alternative criteria had higher sensitivity (91% vs. 78%; P < 0.001) and specificity (86% vs. 84%; P < 0.001) for identifying high-benefit people. For racial and ethnic minorities, the alternative criteria provided greater gains in sensitivity than the USPSTF criteria (Black: 83% vs. 56% [P < 0.001]; Hispanic: 95% vs. 73% [P = 0.086]; Asian: 94% vs. 68% [P = 0.171]) at similar specificity. The alternative criteria identify high-risk, high-benefit groups excluded by the USPSTF criteria (those with a smoking duration of ≥40 years but <20 pack-years and a quit history of >15 years), many of whom are members of racial and ethnic minorities. LIMITATION: The results were based on model projections. CONCLUSION: These results suggest that simple alternative LCS criteria can identify substantially more high-benefit people, especially in some racial and ethnic groups. PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs Lung Precision Oncology Program.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Factors Associated With Declining Lung Cancer Screening After Discussion With a Physician in a Cohort of US Veterans.

Importance: Lung cancer screening (LCS) is underused in the US, particularly in underserved populations, and little is known about factors associated with declining LCS. Guidelines call for shared decision-making when LCS is offered to ensure informed, patient-centered decisions. Objective: To assess how frequently veterans decline LCS and examine factors associated with declining LCS. Design, Setting, and Participants: This retrospective cohort study included LCS-eligible US veterans who were offered LCS between January 1, 2013, and February 1, 2021, by a physician at 1 of 30 Veterans Health Administration (VHA) facilities that routinely used electronic health record clinical reminders documenting LCS eligibility and veterans' decisions to accept or decline LCS. Data were obtained from the Veterans Affairs (VA) Corporate Data Warehouse or Medicare claims files from the VA Information Resource Center. Main Outcomes and Measures: The main outcome was documentation, in clinical reminders, that veterans declined LCS after a discussion with a physician. Logistic regression analyses with physicians and facilities as random effects were used to assess factors associated with declining LCS compared with agreeing to LCS. Results: Of 43 257 LCS-eligible veterans who were offered LCS (mean [SD] age, 64.7 [5.8] years), 95.9% were male, 84.2% were White, and 37.1% lived in a rural zip code; 32.0% declined screening. Veterans were less likely to decline LCS if they were younger (age 55-59 years: odds ratio [OR], 0.69; 95% CI, 0.64-0.74; age 60-64 years: OR, 0.80; 95% CI, 0.75-0.85), were Black (OR, 0.80; 95% CI, 0.73-0.87), were Hispanic (OR, 0.62; 95% CI, 0.49-0.78), did not have to make co-payments (OR, 0.92; 95% CI, 0.85-0.99), or had more frequent VHA health care utilization (outpatient: OR, 0.70; 95% CI, 0.67-0.72; emergency department: OR, 0.86; 95% CI, 0.80-0.92). Veterans were more likely to decline LCS if they were older (age 70-74 years: OR, 1.27; 95% CI, 1.19-1.37; age 75-80 years: OR, 1.93; 95% CI, 1.73-2.17), lived farther from a VHA screening facility (OR, 1.06; 95% CI, 1.03-1.08), had spent more days in long-term care (OR, 1.13; 95% CI, 1.07-1.19), had a higher Elixhauser Comorbidity Index score (OR, 1.04; 95% CI, 1.03-1.05), or had specific cardiovascular or mental health conditions (congestive heart failure: OR, 1.25; 95% CI, 1.12-1.39; stroke: OR, 1.14; 95% CI, 1.01-1.28; schizophrenia: OR, 1.87; 95% CI, 1.60-2.19). The physician and facility offering LCS accounted for 19% and 36% of the variation in declining LCS, respectively. Conclusions and Relevance: In this cohort study, older veterans with serious comorbidities were more likely to decline LCS and Black and Hispanic veterans were more likely to accept it. Variation in LCS decisions was accounted for more by the facility and physician offering LCS than by patient factors. These findings suggest that shared decision-making conversations in which patients play a central role in guiding care may enhance patient-centered care and address disparities in LCS.

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.

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.

Invasive Procedures and Associated Complications After Initial Lung Cancer Screening in a National Cohort of Veterans.

BACKGROUND: Little is known about rates of invasive procedures and associated complications after lung cancer screening (LCS) in nontrial settings. RESEARCH QUESTION: What are the frequency of invasive procedures, complication rates, and factors associated with complications in a national sample of veterans screened for lung cancer? STUDY DESIGN AND METHODS: We conducted a retrospective cohort analysis of veterans who underwent LCS in any Veterans Health Administration (VA) facility between 2013 and 2019 and identified veterans who underwent invasive procedures within 10 months of initial LCS. The primary outcome was presence of a complication within 10 days after an invasive procedure. We conducted hierarchical mixed-effects logistic regression analyses to determine patient- and facility-level factors associated with complications resulting from an invasive procedure. RESULTS: Our cohort of 82,641 veterans who underwent LCS was older, more racially diverse, and had more comorbidities than National Lung Screening Trial (NLST) participants. Overall, 1,741 veterans (2.1%) underwent an invasive procedure after initial screening, including 856 (42.3%) bronchoscopies, 490 (24.2%) transthoracic needle biopsies, and 423 (20.9%) thoracic surgeries. Among veterans who underwent procedures, 151 (8.7%) experienced a major complication (eg, respiratory failure, prolonged hospitalization) and an additional 203 (11.7%) experienced an intermediate complication (eg, pneumothorax, pleural effusion). Veterans who underwent thoracic surgery (OR, 7.70; 95% CI, 5.48-10.81), underwent multiple nonsurgical procedures (OR, 1.49; 95% CI, 1.15-1.92), or carried a dementia diagnosis (OR, 3.91; 95% CI, 1.79-8.52) were more likely to experience complications. Invasive procedures were performed less often than in the NLST (2.1% vs 4.2%), but veterans were more likely to experience complications after each type of procedure. INTERPRETATION: These findings may reflect a higher threshold to perform procedures in veteran populations with multiple comorbidities and higher risks of complications. Future work should focus on optimizing the identification of patients whose chance of benefit likely outweighs the complication risks.

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.

Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility.

OBJECTIVE: The US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data. MATERIALS AND METHODS: In this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50-80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects. RESULTS: Over 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001). DISCUSSION: Missing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening. CONCLUSION: The use of longitudinal EHR smoking data could improve lung cancer screening.

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.

The Need for Brevity During Shared Decision Making (SDM) for Cancer Screening: Veterans' Perspectives on an "Everyday SDM" Compromise.

Introduction. Detailed or "full" shared decision making (SDM) about cancer screening is difficult in the primary care setting. Time spent discussing cancer screening is time not spent on other important issues. Given time constraints, brief SDM that is incomplete but addresses key elements may be feasible and acceptable. However, little is known about how patients feel about abbreviated SDM. This study assessed patient perspectives on a compromise solution ("everyday SDM"): 1) primary care provided makes a tailored recommendation, 2) briefly presents qualitative information on key tradeoffs, and 3) conveys full support for decisional autonomy and desires for more information. Methods. We recruited a stratified random sample of Veterans from an academic Veterans Affairs medical center who were eligible for lung cancer screening, oversampling women and minority patients, to attend a 6-hour deliberative focus group. Experts informed participants about cancer screening, factors that influence screening benefits, and the role of patient preferences. Then, facilitator-led small groups elicited patient questions and informed opinions about the everyday SDM proposal, its acceptability, and their recommendations for improvement. Results. Thirty-six Veterans with a heavy smoking history participated (50% male, 83% white). There was a strong consensus that everyday SDM was acceptable if patients were the final deciders and could get more information on request. Participants broadly recommended that clinicians only mention downsides directly related to screening and avoid discussion of potential downstream harms (such as biopsies). Discussion. Although further testing in more diverse populations and different conditions is needed, these patients found the everyday SDM approach to be acceptable for routine lung cancer screening discussions, despite its use of an explicit recommendation and presentation of only qualitative information.

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

OBJECTIVE: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. MATERIALS AND METHODS: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. RESULTS: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. DISCUSSION: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. CONCLUSION: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.