Implementing Guidelines for Shared Decision Making in Lung Cancer Screening (Preprint)

BACKGROUND Lung cancer risk and life-expectancy vary substantially across patients eligible for low-dose computed tomography lung cancer screening (LCS), and this 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 (screenLC.com), 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 was to test two strategies for implementing DecisionPrecision in primary care at eight VA medical centers: (1) a quality improvement (QI) training approach, and (2) academic detailing. METHODS Phase 1 consisted of a multi-site, cluster randomized trial comparing the effectiveness of standard implementation (adding a link to DecisionPrecision in the electronic health record or EHR) versus standard implementation plus the LEAP (Learn. Engage. Act. Process.) QI training program. The primary outcome measure was use of DecisionPrecision at each site pre- vs post-LEAP QI training. The second phase of the study examined the feasibility and utility of adding academic detailing (AD) as an implementation strategy for DecisionPrecision at all eight medical centers. Outcomes were assessed by (1) comparing tool use pre- and post-AD visits, and (2) conducting semi-structured interviews with a subset of primary care physicians and practitioners (PCPs) following the AD visits. RESULTS Phase 1 findings showed that sites who participated in the LEAP QI training program used DecisionPrecision significantly more often than the standard implementation sites (tool used 190.3 times on average over 6 months at LEAP sites vs. 3.5 at standard sites; P<.001). However, this finding was confounded with the lack of screening coordinators at standard implementation sites. In Phase 2, there was no difference in tool use between pre- and post-academic detailing (95% CI, 5.06 fewer tool uses post-AD to 6.40 more tool uses post-AD; P=0.82). Follow-up interviews with PCPs indicated that the AD strategy did increase provider awareness and appreciation of the benefits of the tool. However, other priorities and limited time prevented PCPs from using it during routine clinic 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 academic detailing strategy did not increase tool use. To enable adoption by PCPs, prediction-based tools need to be fully automated and integrated into the electronic health records (EHR), thereby helping providers personalize LCS discussions among their many other competing demands.