Behavioral Interventions to Improve Breast Cancer Screening Outreach: Two Randomized Clinical Trials.

Importance: Despite public health efforts, breast cancer screening rates remain below national goals. Objective: To evaluate whether bulk ordering, text messaging, and clinician endorsement increase breast cancer screening rates. Design, Setting, and Participants: Two concurrent, pragmatic, randomized clinical trials, each with a 2-by-2 factorial design, were conducted between October 25, 2021, and April 25, 2022, in 2 primary care regions of an academic health system. The trials included women aged 40 to 74 years with at least 1 primary care visit in the past 2 years who were eligible for breast cancer screening. Interventions: Patients in trial A were randomized in a 1:1 ratio to receive a signed bulk order for mammogram or no order; in a factorial design, patients were concurrently randomized in a 1:1 ratio to receive or not receive text message reminders. Patients in trial B were randomized in a 1:1 ratio to receive a message signed by their primary care clinician (clinician endorsement) or from the organization (standard messaging); in a factorial design, patients were concurrently randomized in a 1:1 ratio to receive or not receive text message reminders. Main Outcomes and Measures: The primary outcome was the proportion of patients who completed a screening mammogram within 3 months. Results: Among 24 632 patients included, the mean (SD) age was 60.4 (7.5) years. In trial A, at 3 months, 15.4% (95% CI, 14.6%-16.1%) of patients in the bulk order arm and 12.7% (95% CI, 12.1%-13.4%) in the no order arm completed a mammogram, showing a significant increase (absolute difference, 2.7%; 95% CI, 1.6%-3.6%; P < .001). In the text messaging comparison arms, 15.1% (95% CI, 14.3%-15.8%) of patients receiving a text message completed a mammogram compared with 13.0% (95% CI, 12.4%-13.7%) of those in the no text messaging arm, a significant increase (absolute difference of 2.1%; 95% CI, 1.0%-3.0%; P < .001). In trial B, at 3 months, 12.5% (95% CI, 11.3%-13.7%) of patients in the clinician endorsement arm completed a mammogram compared with 11.4% (95% CI, 10.3%-12.5%) of those in the standard messaging arm, which was not significant (absolute difference, 1.1%; 95% CI, -0.5% to 2.7%; P = .18). In the text messaging comparison arms, 13.2% (95% CI, 12.0%-14.4%) of patients receiving a text message completed a mammogram compared with 10.7% (95% CI, 9.7%-11.8%) of those in the no text messaging arm, a significant increase (absolute difference, 2.5%; 95% CI, 0.8%-4.0%; P = .003). Conclusions and Relevance: These findings show that text messaging women after initial breast cancer screening outreach via either electronic portal or mailings, as well as bulk ordering with or without text messaging, can increase mammogram completion rates. Trial Registration: ClinicalTrials.gov Identifier: NCT05089903.

A Pilot Study to Evaluate the Use of Automated Nicotine Metabolite Ratio Reporting within Primary Care as an Implementation Strategy to Increase the Use of Tobacco Treatments.

INTRODUCTION: Concerns about safety and effectiveness of tobacco treatments reduce their use. We explored integrating the nicotine metabolite ratio (NMR), and messaging about its potential for improving safety and effectiveness, as a strategy to increase use of tobacco treatments within primary care. METHODS: Through a prospective cohort design, we explored the effects of integrating NMR testing within primary care on the provision of tobacco treatment; 65 patients completed assessments including NMR before a clinic visit. At the clinic visit, patients' clinicians received an electronic health record alert about the patient's NMR and personalized treatment recommendations to improve effectiveness and safety. Being asked about smoking and advised to quit, and a referral for tobacco treatment or medication prescription, were assessed within 30 days of the appointment and were compared to a usual care cohort (N=85). RESULTS: The NMR and usual care cohorts reported similar rates of being asked about smoking (92.3% vs. 92.9%, p=1.0), being advised to quit (72.3% vs. 74.1%, p=0.85), being referred for tobacco treatment (23.1% vs. 36.5%, p=0.11), and receiving tobacco use medications (20% vs. 27.1%, p=0.34). In the NMR cohort, fast vs. slow metabolizers were more likely to receive medication (26% vs. 0%, p=0.003) and all patients who received varenicline (n=8) were fast metabolizers. CONCLUSIONS: NMR results and treatment recommendations did not increase tobacco treatment rates in primary care, although it may increase treatment rates and use of varenicline for fast metabolizers. Future studies could test ways to use the NMR to increase tobacco treatment rates in clinical settings. IMPLICATIONS: This study generated a novel implementation strategy, namely an electronic health record alert about patients' NMR and personalized treatment recommendations, in an effort to increase tobacco treatment rates in primary care. While the strategy did not increase tobacco treatment rates, it may have boosted the rate of varenicline prescription for patients who metabolize nicotine faster, aligning with evidence-based practice.

Development of an Electronic Health Record-Based Clinical Decision Support Tool for Patients With Lynch Syndrome.

PURPOSE: To develop an electronic health record (EHR)-based clinical decision support (CDS) tool to promote guideline-recommended cancer risk management among patients with Lynch syndrome (LS), an inherited cancer syndrome that confers an increased risk of colorectal and other cancer types. MATERIALS AND METHODS: We conducted a cross-sectional study to determine the baseline prevalence and predictors of guideline-recommended colonic surveillance and annual genetics program visits among patients with LS. Multivariable log-binomial regressions estimated prevalence ratios (PRs) of cancer risk management adherence by baseline sociodemographic and clinical characteristics. These analyses provided rationale for the development of an EHR-based CDS tool to support patients and clinicians with LS-related endoscopic surveillance and annual genetics program visits. The CDS leverages an EHR platform linking discrete genetic data to LS Genomic Indicators, in turn driving downstream clinician- and patient-facing CDS. RESULTS: Among 323 patients with LS, cross-sectional adherence to colonic surveillance and annual genetics program visits was 69.3% and 55.4%, respectively. Patients with recent electronic patient portal use were more likely to be adherent to colonic surveillance (PR, 1.67; 95% CI, 1.11 to 2.52). Patients more recently diagnosed with LS were more likely to be adherent to annual genetics program visits (PR, 0.58; 95% CI, 0.44 to 0.76 for 2-4 years; PR, 0.62; 95% CI, 0.51 to 0.75 for ≥4 compared with <2 years). Our EHR-based CDS tool is now active for 421 patients with LS throughout our health system. CONCLUSION: We have successfully developed an EHR-based CDS tool to promote guideline-recommended cancer risk management among patients with LS.

Impact of integrating genomic data into the electronic health record on genetics care delivery.

PURPOSE: Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine. METHODS: The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery. RESULTS: The PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P < .001) and managing (1 vs 5 minutes, P < .001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success. CONCLUSION: The PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.

Behavioral interventions to improve population health outreach for hepatitis C screening: randomized clinical trial.

OBJECTIVE: To evaluate whether opt out framing, messaging incorporating behavioral science concepts, or electronic communication increases the uptake of hepatitis C virus (HCV) screening in patients born between 1945 and 1965. DESIGN: Pragmatic randomized controlled trial. SETTING: 43 primary care practices from one academic health system (Philadelphia, PA, USA) between April 2019 and May 2020. PARTICIPANTS: Patients born between 1945 and 1965 with no history of screening and at least two primary care visits in the two years before the enrollment period. INTERVENTIONS: This multilevel trial was divided into two studies. Substudy A included 1656 eligible patients of 17 primary care clinicians who were randomized in a 1:1 ratio to a mailed letter about HCV screening (letter only), or a similar letter with a laboratory order for HCV screening (letter+order). Substudy B included the remaining 19 837 eligible patients followed by 417 clinicians. Active electronic patient portal users were randomized 1:5 to receive a mailed letter about HCV screening (letter), or an electronic patient portal message with similar content (patient portal); inactive patient portal users were mailed a letter. In a factorial design, patients in substudy B were also randomized 1:1 to receive standard content (usual care), or content based on principles of social norming, anticipated regret, reciprocity, and commitment (behavioral content). MAIN OUTCOME MEASURES: Proportion of patients who completed HCV testing within four months. RESULTS: 21 303 patients were included in the intention-to-treat analysis. Among the 1642 patients in substudy A, 19.2% (95% confidence interval 16.5% to 21.9%) completed screening in the letter only arm and 43.1% (39.7% to 46.4%) in the letter+order arm (P<0.001). Among the 19 661 patients in substudy B, 14.6% (13.9% to 15.3%) completed screening with usual care content and 13.6% (13.0% to 14.3%) with behavioral science content (P=0.06). Among active patient portal users, 17.8% (16.0% to 19.5%) completed screening after receiving a letter and 13.8% (13.1% to 14.5%) after receiving a patient portal message (P<0.001). CONCLUSIONS: Opt out framing and effort reduction by including a signed laboratory order with outreach increased screening for HCV. Behavioral science messaging content did not increase uptake, and mailed letters achieved a greater response rate than patient portal messages. TRIAL REGISTRATION: ClinicalTrials.gov NCT03712553.

Rate and correlates of tobacco treatment during a primary care visit for a largely urban and African American sample of smokers.

Introduction: Evidence-based treatments for tobacco use are under-utilized and primary care visits may be an opportune time to address this gap. This study examined the rate at which primary care visits included tobacco use treatment and examined patient demographics, smoking characteristics, attitudes about tobacco use treatments, and comorbidities as correlates of treatment provision. Methods: This prospective study assessed demographics, smoking characteristics, attitudes about tobacco use treatments, and comorbidities via interview prior to a primary care visit among 105 patients. One week following the appointment, 85 patients were reassessed for the tobacco use treatments they received during their appointment (i.e., asked about their tobacco use, advised to quit, and provided with a referral to a tobacco use treatment program or an FDA-approved tobacco use medication). Results: 93% of patients were asked about their tobacco use, 74% were advised to quit, 37% were provided with a referral for tobacco use treatment, and 27% received an FDA-approved medication (16% NRT, 11% varenicline or bupropion). Patients with higher quit motivation and who endorsed that medications can reduce cravings were more likely to report receiving tobacco use medication. Patients with a self-reported substance abuse history were less likely to report receiving tobacco use medications. Conclusions: The provision of tobacco use medications within primary care remains low. Strategies to increase patient quit motivation and help patients understand that tobacco use medications can mitigate cravings may increase use. Strategies may also be needed to ensure that patients with comorbid substance abuse still receive tobacco use treatments.