BACKGROUND: One-quarter of U.S. patients do not have a primary care provider or do not have complete access to one. Work and personal responsibilities also compete with finding convenient, accessible care. Telehealth services facilitate patients' access to care, but whether patients are satisfied with telehealth is unclear. OBJECTIVE: We assessed patients' satisfaction with and preference for telehealth visits in a telehealth program at CVS MinuteClinics. DESIGN: Cross-sectional patient satisfaction survey. PARTICIPANTS: Patients were aged ≥18 years, presented at a MinuteClinic offering telehealth in January-September 2014, had symptoms suitable for telehealth consultation, and agreed to a telehealth visit when the on-site practitioner was busy. MAIN MEASURES: Patients reported their age, gender, and whether they had health insurance and/or a primary care provider. Patients rated their satisfaction with seeing diagnostic images, hearing and seeing the remote practitioner, the assisting on-site nurse's capability, quality of care, convenience, and overall understanding. Patients ranked telehealth visits compared to traditional ones: better (defined as preferring telehealth), just as good (defined as liking telehealth), or worse. Predictors of preferring or liking telehealth were assessed via multivariate logistic regression. KEY RESULTS: In total, 1734 (54 %) of 3303 patients completed the survey: 70 % were women, and 41 % had no usual place of care. Between 94 and 99 % reported being Bvery satisfied^with all telehealth attributes. Onethird preferred a telehealth visit to a traditional in-person visit. An additional 57 % liked telehealth. Lack of medical insurance increased the odds of preferring telehealth (OR=0.83, 95 % CI,

Most patients who initiate a medication for osteoporosis do not continue to take it as prescribed. Although several patient characteristics significantly correlated with compliance, adjusted models explained little of the variation.

IntroductionHealth care utilization databases have been increasingly used for studies of rheumatoid arthritis (RA). However, the accuracy of RA diagnoses in these data has been inconsistent.MethodsUsing medical records and a standardized abstraction form, we examined the positive predictive value (PPV) of several algorithms to define RA diagnosis using claims data: A) at least two visits coded for RA (ICD-9, 714); B) at least three visits coded for RA; and C) at least two visits to a rheumatologist for RA. We also calculated the PPVs for the subgroups identified by these algorithms combined with pharmacy claims data for at least one disease-modifying anti-rheumatic drug (DMARD) prescription.ResultsWe invited 9,482 Medicare beneficiaries with pharmacy benefits in Pennsylvania to participate; 2% responded and consented for review of their medical records. There was no difference in characteristics between respondents and non-respondents. Using 'RA diagnosis per rheumatologists' as the gold standard, the PPVs were 55.7% for at least two claims coded for RA, 65.5% for at least three claims for RA, and 66.7% for at least two rheumatology claims for RA. The PPVs of these algorithms in patients with at least one DMARD prescription increased to 86.2%-88.9%. When fulfillment of 4 or more of the ACR RA criteria was used as the gold standard, the PPVs of the algorithms combined with at least one DMARD prescriptions were 55.6%-60.7%.ConclusionsTo accurately identify RA patients in health care utilization databases, algorithms that include both diagnosis codes and DMARD prescriptions are recommended.

Teva Pharmaceuticals.