Integrating tobacco treatment into lung cancer screening practices: Study protocol for the Screen ASSIST randomized clinical trial

Budget impact analysis for implementation decision making, planning, and financing

Shelley et al. (in Accelerating integration of tobacco use treatment in the context of lung cancer screening: relevance and application of implementation science to achieving policy and practice. Transl Behav Med 2022;12:1076-1083) laid out how implementation science frameworks and methods can advance the delivery of tobacco use treatment services during lung cancer screening services, which until recently was mandated by the Centers for Medicare and Medicaid Services. Their discussion provides an important overview of the full process of implementation and highlights the vast number of decisions that must be made when planning for implementation of an evidence-based practice such as tobacco use treatment: what specific tobacco use treatment services to deliver, when to deliver those services within the lung cancer screening process, and what implementation strategies to use. The costs of implementation play a major role in decision making and are a key implementation determinant discussed in major implementation frameworks. When making decisions about what and how to implement, budget impact analyses (BIAs) can play an important role in informing decision making by helping practitioners understand the overall affordability of a given implementation effort. BIAs can also inform the development of financing strategies to support the ongoing sustainment of tobacco use treatment service provision. More attention is needed by the research community to produce high-quality, user-friendly, and flexible BIAs to inform implementation decision making in health system and community settings. The application of BIA can help ensure that the considerable time and effort spent to develop and evaluate evidence-based programs has the best chance to inform implementation practice.

Enhancing an enterprise data warehouse for research with data extracted using natural language processing

Objective

This study aims to develop a generalizable architecture for enhancing an enterprise data warehouse for research (EDW4R) with results from a natural language processing (NLP) model, which allows discrete data derived from clinical notes to be made broadly available for research use without need for NLP expertise. The study also quantifies the additional value that information extracted from clinical narratives brings to EDW4R.

Materials and methods

Clinical notes written during one month at an academic health center were used to evaluate the performance of an existing NLP model and to quantify its value added to the structured data. Manual review was utilized for performance analysis. The architecture for enhancing the EDW4R is described in detail to enable reproducibility.

Results

Two weeks were needed to enhance EDW4R with data from 250 million clinical notes. NLP generated 16 and 39% increase in data availability for two variables.

Discussion

Our architecture is highly generalizable to a new NLP model. The positive predictive value obtained by an independent team showed only slightly lower NLP performance than the values reported by the NLP developers. The NLP showed significant value added to data already available in structured format.

Conclusion

Given the value added by data extracted using NLP, it is important to enhance EDW4R with these data to enable research teams without NLP expertise to benefit from value added by NLP models.

Practical Approaches to Advancing Health Equity in Radiology, From the AJR Special Series on DEI

Despite significant advances in healthcare, many patients from medically underserved populations are impacted by existing healthcare disparities. Radiologists are uniquely positioned to decrease health disparities and advance health equity efforts in their practices. However, literature on practical tools for advancing radiology health equity efforts applicable to a wide variety of patient populations and care settings is lacking. Therefore, this article seeks to equip radiologists with an evidence-based and practical knowledge toolkit of health equity strategies, presented in terms of four pillars of research, clinical care, education, and innovation. For each pillar, equity efforts across diverse patient populations and radiology practice settings are examined through the lens of existing barriers, current best practices, and future directions, incorporating practical examples relevant to a spectrum of patient populations. Health equity efforts provide an opportune window to transform radiology through personalized care delivery that is responsive to diverse patient needs. Guided by compassion and empathy as core principles of health equity, leveraging the four pillars provides a helpful framework to advance health equity efforts as a step towards social justice in health.

Leveraging the Clinical Timepoints in Lung Cancer Screening to Engage Individuals in Tobacco Treatment

The US Preventive Services Task Force recommends lung cancer screening (LCS) to promote early lung cancer detection, and tobacco cessation services are strongly recommended in adjunct. Screen ASSIST (NCT03611881) is a randomized factorial trial to ascertain the best tobacco treatment intervention for smokers undergoing LCS; trial outreach is conducted during 3 recruitment points (RPs): when LCS is ordered (RP1), at screening (RP2), and following results (RP3). Among 177 enrollees enrolled from April 2019 to March 2020, 31.6% enrolled at RP1, 13.0% at RP2, and 55.4% at RP3. The average number of enrollees (per 1000 recruitment days) was 2.26 in RP1, 3.37 in RP2, and 1.04 in RP3. LCS provides an opportunity to offer tobacco treatment at multiple clinical timepoints. Repeated and proactive outreach throughout the LCS experience was beneficial to enrolling patients in tobacco cessation services.

Engaging Patients in Smoking Cessation Treatment within the Lung Cancer Screening Setting: Lessons Learned from an NCI SCALE Trial

Offering smoking cessation treatment at lung cancer screening (LCS) will maximize mortality reduction associated with screening, but predictors of treatment engagement are not well understood. We examined participant characteristics of engagement in an NCI SCALE cessation trial. Eligible LCS patients (N = 818) were randomized to the Intensive arm (8 phone counseling sessions +8 weeks of nicotine replacement therapy (NRT)) vs. Minimal arm (3 sessions + 2 weeks of NRT). Engagement was measured by number of sessions completed (none, some, or all) and NRT mailed (none vs. any) in each arm. In the Intensive arm, those with ≥some college (OR = 2.1, 95% CI = 1.1, 4.0) and undergoing an annual scan (OR = 2.1, 95% CI = 1.1, 4.2) engaged in some counseling vs. none. Individuals with higher nicotine dependence were more likely (OR = 2.8, 95% CI = 1.3, 6.2) to request NRT. In the Minimal arm, those with higher education (OR = 2.1, 95% CI = 1.1, 3.9) and undergoing an annual scan (OR = 2.0, 95% CI = 1.04, 3.8) completed some sessions vs. none. Requesting NRT was associated with more pack-years (OR = 1.9, 95% CI = 1.1, 3.5). Regardless of treatment intensity, additional strategies are needed to engage those with lower education, less intensive smoking histories, and undergoing a first scan. These efforts will be important given the broader 2021 LCS guidelines.