Effects of Behavioral Economics-Based Messaging on Appointment Scheduling Through Patient Portals and Appointment Completion: Observational Study.

BACKGROUND: Behavioral economics-based techniques have been an increasingly utilized method in health care to influence behavior change by modifying language in patient communication (through choice architecture and the framing of words). Patient portals are a key tool for facilitating patient engagement in their health, and interventions deployed via patient portals have been effective in improving utilization of preventive health services. OBJECTIVE: We examined the impacts of behavioral economics-based nudge health maintenance reminders on appointment scheduling through a patient portal and appointment completion for 2 preventive services: Medicare wellness visits and Pap smear. METHODS: We conducted a retrospective observational study using electronic health record data from an integrated health care system in Northern California. Nudge health maintenance reminders with behavioral economics-based language were implemented for all sites in November 2017 for Medicare wellness visits and for selected sites in February 2018 for Pap smears. We analyzed 125,369 health maintenance reminders for Medicare wellness visits, and 585,358 health maintenance reminders for Pap smear sent between January 2017 and February 2020. The primary outcomes were rate of appointments scheduled through the patient portal and appointment completion rate. We compared the outcomes between those who received the new, behavioral economics-based health maintenance reminders (the nudge group) and those who received the original, standard health maintenance reminders (the control group). We used segmented regression with interrupted time series to assess the immediate and gradual effect of the nudge for Medicare wellness visits, and we used logistic regression to assess the association of nudge health maintenance reminders, adjusting for the propensity to receive a nudge health maintenance reminder, for Pap smear. RESULTS: The rates of appointments scheduled through the patient portal were higher for nudge health maintenance reminder recipients than those for control health maintenance reminder recipients (Medicare wellness visits-nudge: 12,537/96,839, 13.0%; control: 2,769/28,530, 9.7%, P<.001; Pap smear-nudge: 8,239/287,149, 2.9%; control: 1,868/120,047, 1.6%; P<.001). Rates of appointment completion were higher for nudge health maintenance reminders for Pap smear (nudge: 67,399/287,149, 23.5% control: 20,393/120,047, 17.0%; P<.001) but were comparable for Medicare wellness visits (nudge: 49,835/96,839, 51.5% control: 14,781/28,530, 51.8%; P=.30). There was a marginally gradual effect of nudge on number of appointments scheduled through the patient portal for the overall Medicare wellness visits sample (at a monthly rate of 0.26%, P=.09), and a significant gradual effect among scheduled appointments (at a monthly rate of 0.46%, P=.04). For Pap smear, nudge health maintenance reminders were positively associated with number of appointments scheduled through the patient portal (overall sample: propensity adjusted odds ratio [OR] 1.62; 95% CI 1.50-1.74; among scheduled appointments: propensity adjusted OR 1.61, 95% CI 1.47-1.76) and with appointment completion (propensity adjusted OR 1.07; 1.04-1.10). CONCLUSIONS: Nudges, a behavioral economics-based approach to providing health maintenance reminders, increased the number of appointments scheduled through the patient portal for Medicare wellness visits and Pap smear. Our study demonstrates that a simple approach-framing and modifying language in an electronic message-can have a significant and long-term impact on patient engagement and access to care.

History of Cholecystectomy and Risk of Clostridium Difficile Infection.

BACKGROUND: Cholecystectomy affects bile acid physiology. There is growing evidence that both primary and secondary bile acids play a role in the pathogenesis of Clostridium difficile infections (CDIs). AIMS: The aim of this study is to elucidate the relationship and risk of CDI in patients with cholecystectomy. METHODS: We performed a matched cohort study of patients in an integrated healthcare system in Northern California from January 2000 to December 2018. Patients with cholecystectomy (cases, n = 12,617) identified based on Current Procedure Terminology codes were age- and sex-matched to patients without cholecystectomy (controls, n = 37,851). We excluded those with history of CDI at baseline and calculated the hazard ratio (HR) for development of CDI after adjusting for confounders. RESULTS: We found total of 351 incident CDI during average of 4.66 years of follow-up among cases and controls. In multivariate analysis, cholecystectomy was associated with elevated risk of CDI (HR 1.53, 95% confidence interval 1.14-2.04) compared with controls. Stratified analysis shows this effect does not differ according use of proton pump inhibitors (Pinteraction = 0.142), antibiotics (Pinteraction = 0.387), and hospitalization (Pinteraction = 0.252). CONCLUSIONS: Cholecystectomy is associated with mild increased risk of incident CDI, but this effect is not influenced by use of proton pump inhibitors, antibiotics, or hospitalization. Future prospective studies should be conducted to validate these findings and evaluate bile acid changes after a cholecystectomy.

Early Weight Loss and Treatment Response: Data From a Lifestyle Change Program in Clinical Practice.

INTRODUCTION: The purpose of this study was to develop and validate a predictive model for the early identification of nonresponders to a 12-month lifestyle change program in clinical practice. METHODS: Investigators identified lifestyle change program participants in the electronic health records of a large healthcare delivery system between 2010 and 2017. Nonresponse was defined as weight gain or no weight loss at 12 months from the program initiation (baseline). Logistic regression with percentage weight change at 2-12 weeks from baseline was used as an independent predictor of nonresponse. Baseline demographics and clinical characteristics were also tested as potential predictors. The authors performed ten-fold cross-validation for model assessment and examined model performance with the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values. The analyses were conducted in 2019. RESULTS: Among 947 program participants, 30% were classified as nonresponders at 12 months. The model with the best discrimination of responders from nonresponders included weight change at 12 weeks from baseline as the sole predictor (area under the receiver operating characteristic curve, 0.789). Sensitivity and positive predictive value were maximized at 0.56 (specificity and negative predictive value, 0.81 each). CONCLUSIONS: In a cohort of lifestyle change program participants from clinical practice, percentage weight change at 12 weeks from baseline can serve as a single indicator of nonresponse at the completion of the 12-month program. Clinicians can easily apply this algorithm to identify and assess participants in potential need of adjunctive or alternative therapy to maximize treatment outcomes.