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

Abstract

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

Lung Cancer Screening Decision Aid Designed for a Primary Care Setting

“…The tool guides clinicians to tailor an LCS SDM discussion based on individual lung cancer risk profiles. Neither implementation strategy increased the use of the tool, with clinician time constraints and competing priorities emerging as barriers …”

“…Patient decision aids (PDAs) for LCS in both veteran and general populations have improved decision quality in before-and-after single-group study designs . A number of SDM interventions including PDAs have also been evaluated in randomized clinical trials (RCTs) .…”

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

“…20 DP provides individualized information based on risk prediction and is designed to support personalized SDM for LCS to facilitate the delivery of more equitable and patient-centered screening for USPSTF-eligible patients based on age and smoking history. 21 One of the implementation methods used in this QI study was academic detailing, an educational outreach approach focusing on one-on-one clinical education and behavior change. 22 Detailing numerous primary care clinicians across multiple medical facilities allowed us to explore in-depth clinician views on routinely implementing personalized, prediction-based SDM for cancer screening in the primary care setting.…”

Investigation on the incidence and risk factors of lung cancer among Chinese hospital employees

“…The current evidence that low‐dose helical computed tomography (LDCT) reduces lung cancer mortality comes primarily from two randomized controlled trials 8,12–14 . From 2002 to 2004, the National Lung Screening Trial (NLST) was carried out by the American Lung Cancer Screening Research Group 10,15–17 . A total of 53 454 people at high risk of lung cancer were included, and they were randomly divided into LDCT group and chest X‐ray group.…”

“…8,[12][13][14] From 2002 to 2004, the National Lung Screening Trial (NLST) was carried out by the American Lung Cancer Screening Research Group. 10,[15][16][17] A total of 53 454 people at high risk of lung cancer were included, and they were randomly divided into LDCT group and chest X-ray group. Compared with chest X-ray, LDCT can reduce lung cancer mortality by 20%.…”