Patient and physician predictors of hyperlipidemia screening and statin prescription.

OBJECTIVES: Appropriate lipid management has been demonstrated to reduce cardiovascular events, but rates of hyperlipidemia screening and statin therapy are suboptimal. We aimed to evaluate patient and physician predictors of guideline-concordant hyperlipidemia screening and statin prescription. STUDY DESIGN: Retrospective study of patients with primary care provider (PCP) visits from 2014 to 2016 at the University of Pennsylvania Health System. METHODS: Data on patients, screening orders, and prescriptions were obtained from the electronic health record. Multivariate logistic regression models were fit to binary outcomes of lipid screening and statin prescription. RESULTS: Among 97,189 eligible patients, 79.9% had an order for hyperlipidemia screening. In adjusted models, significant patient predictors of greater odds of having screening ordered included a history of diabetes (odds ratio [OR], 1.19; 95% CI, 1.10-1.29; P <.001) or hypertension (OR, 1.16; 95% CI, 1.10-1.23; P <.001). Significant provider predictors of lower odds of having screening ordered were being a resident PCP (OR, 0.63; 95% CI, 0.43-0.93; P = .021) or being trained in family medicine (OR, 0.37; 95% CI, 0.30-0.47; P <.001). Among 40,845 eligible patients, 56.1% were prescribed a statin. In adjusted models, significant patient predictors of greater odds of being prescribed a statin were if they had a history of diabetes (OR, 2.70; 95% CI, 2.32-3.13; P <.001) or clinical cardiovascular disease (OR, 2.26; 95% CI, 1.85-2.76; P <.001). Significant provider predictors of lower odds of being prescribed a statin were being a physician assistant (OR, 0.65; 95% CI, 0.52-0.81; P <.001) or female (OR, 0.82; 95% CI, 0.70-0.96; P = .01). CONCLUSIONS: Both patient and provider factors significantly predicted guideline-concordant care for hyperlipidemia screening and statin therapy.

Social Incentives and Gamification to Promote Weight Loss: The LOSE IT Randomized, Controlled Trial.

BACKGROUND: Social networks influence obesity patterns, but interventions to leverage social incentives to promote weight loss have not been well evaluated. OBJECTIVE: To test the effectiveness of gamification interventions designed using insights from behavioral economics to enhance social incentives to promote weight loss. DESIGN: The Leveraging Our Social Experiences and Incentives Trial (LOSE IT) was a 36-week randomized, controlled trial with a 24-week intervention and 12-week follow-up. PARTICIPANTS: One hundred and ninety-six obese adults (body mass index ≥ 30) comprising 98 two-person teams. INTERVENTIONS: All participants received a wireless weight scale, used smartphones to track daily step counts, formed two-person teams with a family member or friend, and selected a weight loss goal. Teams were randomly assigned to control or one of two gamification interventions for 36 weeks that used points and levels to enhance collaborative social incentives. One of the gamification arms also had weight and step data shared regularly with each participant's primary care physician (PCP). MAIN OUTCOME MEASURES: The primary outcome was weight loss at 24 weeks. Secondary outcomes included weight loss at 36 weeks. KEY RESULTS: At 24 weeks, participants lost significant weight from baseline in the control arm (mean: - 3.9 lbs; 95% CI: - 6.1 to - 1.7; P < 0.001), the gamification arm (mean: - 6.6 lbs; 95% CI: - 9.4 to - 3.9; P < 0.001), and the gamification arm with PCP data sharing (mean: - 4.8 lbs; 95% CI: - 7.4 to - 2.3; P < 0.001). At 36 weeks, weight loss from baseline remained significant in the control arm (mean: - 3.5 lbs; 95% CI: - 6.1 to - 0.8; P = 0.01), the gamification arm (mean: - 6.3 lbs; 95% CI: - 9.2 to - 3.3; P < 0.001), and the gamification arm with PCP data sharing (mean: - 5.2 lbs; 95% CI: - 8.5 to - 2.0; P < 0.01). However, in the main adjusted model, there were no significant differences in weight loss between each of the intervention arms and control at either 12, 24, or 36 weeks. CONCLUSIONS: Using digital health devices to track behavior with a partner led to significant weight loss through 36 weeks, but the gamification interventions were not effective at promoting weight loss when compared to control. TRIAL REGISTRATION: clinicaltrials.gov Identifier: 02564445.

Scheduling primary care appointments online: Differences in availability based on health insurance.

BACKGROUND: Digital platforms that allow patients to go online or use smartphone applications to view and schedule physician appointments have not been well evaluated. METHODS: We conducted systematic searches for primary care physician appointments in 20 cities using ZocDoc, an online appointment scheduling platform. Availability was determined for three insurance types (self-pay, Medicare, and Medicaid) in states with and without Medicaid expansion. We collected data on physician characteristics, number of appointments available, and distance to clinics. RESULTS: The sample comprised 4150 physician observations across 17 states. Overall, the mean distance to clinic was 8.9 miles (SD: 8.4 miles), mean total number of appointments available within 3 days for the 10 closest physicians was 20.1 (SD: 27.1), and the mean number of physicians available within 5 miles was 5.4 (SD: 6.6). There were no differences in physician characteristics by insurance type. Access to appointments did not differ between Medicare and self-pay. However, compared to self-pay, appointments for Medicaid were further away (Mean difference in miles: 5.4, P < 0.001), and there were fewer physicians available within 5 miles (Mean difference in # of physicians: -4.9, P < 0.001). States that did not adopt Medicaid expansion had fewer appointments within proximity, but this differed similarly across insurance types. CONCLUSIONS: There were a substantial number of available appointments at close distances. However, Medicaid patients had less access to appointments within proximity than self-pay or Medicare patients.

Effect of an Automated Patient Dashboard Using Active Choice and Peer Comparison Performance Feedback to Physicians on Statin Prescribing: The PRESCRIBE Cluster Randomized Clinical Trial.

Importance: Statins are not prescribed to approximately 50% of patients who could benefit from them. Objective: To evaluate the effectiveness of an automated patient dashboard using active choice framing with and without peer comparison feedback on performance to nudge primary care physicians (PCPs) to increase guideline-concordant statin prescribing. Design, Setting, and Participants: This 3-arm cluster randomized clinical trial was conducted from February 21, 2017, to April 21, 2017, at 32 practice sites in Pennsylvania and New Jersey. Participants included 96 PCPs and 4774 patients not previously receiving statin therapy. Data were analyzed from April 25, 2017, to June 16, 2017. Interventions: Primary care physicians in the 2 intervention arms were emailed a link to an automated online dashboard listing their patients who met national guidelines for statin therapy but had not been prescribed this medication. The dashboard included relevant patient information, and for each patient, PCPs were asked to make an active choice to prescribe atorvastatin, 20 mg, once daily, atorvastatin at another dose, or another statin or not prescribe a statin and select a reason. The dashboard was available for 2 months. In 1 intervention arm, the email to PCPs also included feedback on their statin prescribing rate compared with their peers. Primary care physicians in the usual care group received no interventions. Main Outcomes and Measures: Statin prescription rates. Results: Patients had a mean (SD) age of 62.4 (8.3) years and a mean (SD) 10-year atherosclerotic cardiovascular disease risk score of 13.6 (8.2); 2625 (55.0%) were male, 3040 (63.7%) were white, and 1318 (27.6%) were black. In the active choice arm, 16 of 32 PCPs (50.0%) accessed the patient dashboard, but only 2 of 32 (6.3%) signed statin prescription orders. In the active choice with peer comparison arm, 12 of 32 PCPs (37.5%) accessed the patient dashboard and 8 of 32 (25.0%) signed statin prescription orders. Statins were prescribed in 40 of 1566 patients (2.6%) in the usual care arm, 116 of 1743 (6.7%) in the active choice arm, and 117 of 1465 (8.0%) in the active choice with peer comparison arm. In the main adjusted model, compared with usual care, there was a significant increase in statin prescribing in the active choice with peer comparison arm (adjusted difference in percentage points, 5.8; 95% CI, 0.9-13.5; P = .008), but not in the active choice arm (adjusted difference in percentage points, 4.1; 95% CI, -0.8 to 13.1; P = .11). Conclusions and Relevance: An automated patient dashboard using both active choice framing and peer comparison feedback led to a modest but significant increase in guideline-concordant statin prescribing rates. Trial Registration: ClinicalTrials.gov Identifier: NCT03021759.

Effect of a Price Transparency Intervention in the Electronic Health Record on Clinician Ordering of Inpatient Laboratory Tests: The PRICE Randomized Clinical Trial.

Importance: Many health systems are considering increasing price transparency at the time of order entry. However, evidence of its impact on clinician ordering behavior is inconsistent and limited to single-site evaluations of shorter duration. Objective: To test the effect of displaying Medicare allowable fees for inpatient laboratory tests on clinician ordering behavior over 1 year. Design, Setting, and Participants: The Pragmatic Randomized Introduction of Cost data through the electronic health record (PRICE) trial was a randomized clinical trial comparing a 1-year intervention to a 1-year preintervention period, and adjusting for time trends and patient characteristics. The trial took place at 3 hospitals in Philadelphia between April 2014 and April 2016 and included 98 529 patients comprising 142 921 hospital admissions. Interventions: Inpatient laboratory test groups were randomly assigned to display Medicare allowable fees (30 in intervention) or not (30 in control) in the electronic health record. Main Outcomes and Measures: Primary outcome was the number of tests ordered per patient-day. Secondary outcomes were tests performed per patient-day and Medicare associated fees. Results: The sample included 142 921 hospital admissions representing patients who were 51.9% white (74 165), 38.9% black (55 526), and 56.9% female (81 291) with a mean (SD) age of 54.7 (19.0) years. Preintervention trends of order rates among the intervention and control groups were similar. In adjusted analyses of the intervention group compared with the control group over time, there were no significant changes in overall test ordering behavior (0.05 tests ordered per patient-day; 95% CI, -0.002 to 0.09; P = .06) or associated fees ($0.24 per patient-day; 95% CI, -$0.42 to $0.91; P = .47). Exploratory subset analyses found small but significant differences in tests ordered per patient-day based on patient intensive care unit (ICU) stay (patients with ICU stay: -0.16; 95% CI, -0.31 to -0.01; P = .04; patients without ICU stay: 0.13; 95% CI, 0.08-0.17; P < .001) and the magnitude of associated fees (top quartile of tests based on fee value: -0.01; 95% CI, -0.02 to -0.01; P = .04; bottom quartile: 0.03; 95% CI, 0.002-0.06; P = .04). Adjusted analyses of tests that were performed found a small but significant overall increase in the intervention group relative to the control group over time (0.08 tests performed per patient day, 95% CI, 0.03-0.12; P < .001). Conclusions and Relevance: Displaying Medicare allowable fees for inpatient laboratory tests did not lead to a significant change in overall clinician ordering behavior or associated fees. Trial Registration: clinicaltrials.gov Identifier: NCT02355496.