Comparison of Quality Performance Measures for Patients Receiving In-Person vs Telemedicine Primary Care in a Large Integrated Health System

IMPORTANCE: Despite its rapid adoption during the COVID-19 pandemic, it is unknown how telemedicine augmentation of in-person office visits has affected quality of patient care. OBJECTIVE: To examine whether quality of care among patients exposed to telemedicine differs from patients with only in-person office-based care. DESIGN, SETTING, AND PARTICIPANTS: In this retrospective cohort study, standardized quality measures were compared between patients with office-only (in-person) visits vs telemedicine visits from March 1, 2020, to November 30, 2021, across more than 200 outpatient care sites in Pennsylvania and Maryland. EXPOSURES: Patients completing telemedicine (video) visits. MAIN OUTCOMES AND MEASURES: χ(2) tests determined statistically significant differences in Health Care Effectiveness Data and Information Set (HEDIS) quality performance measures between office-only and telemedicine-exposed groups. Multivariable logistic regression controlled for sociodemographic factors and comorbidities. RESULTS: The study included 526 874 patients (409 732 office-only; 117 142 telemedicine exposed) with a comparable distribution of sex (196 285 [49.7%] and 74 878 [63.9%] women), predominance of non-Hispanic (348 127 [85.0%] and 105 408 [90.0%]) and White individuals (334 215 [81.6%] and 100 586 [85.9%]), aged 18 to 65 years (239 938 [58.6%] and 91 100 [77.8%]), with low overall health risk scores (373 176 [91.1%] and 100 076 [85.4%]) and commercial (227 259 [55.5%] and 81 552 [69.6%]) or Medicare or Medicaid (176 671 [43.1%] and 52 513 [44.8%]) insurance. For medication-based measures, patients with office-only visits had better performance, but only 3 of 5 measures had significant differences: patients with cardiovascular disease (CVD) receiving antiplatelets (absolute percentage difference [APD], 6.71%; 95% CI, 5.45%-7.98%; P < .001), patients with CVD receiving statins (APD, 1.79%; 95% CI, 0.88%-2.71%; P = .001), and avoiding antibiotics for patients with upper respiratory infections (APD, 2.05%; 95% CI, 1.17%-2.96%; P < .001); there were insignificant differences for patients with heart failure receiving β-blockers and those with diabetes receiving statins. For all 4 testing-based measures, patients with telemedicine exposure had significantly better performance differences: patients with CVD with lipid panels (APD, 7.04%; 95% CI, 5.95%-8.10%; P < .001), patients with diabetes with hemoglobin A(1c) testing (APD, 5.14%; 95% CI, 4.25%-6.01%; P < .001), patients with diabetes with nephropathy testing (APD, 9.28%; 95% CI, 8.22%-10.32%; P < .001), and blood pressure control (APD, 3.55%; 95% CI, 3.25%-3.85%; P < .001); this was also true for all 7 counseling-based measures: cervical cancer screening (APD, 12.33%; 95% CI, 11.80%-12.85%; P < .001), breast cancer screening (APD, 16.90%; 95% CI, 16.07%-17.71%; P < .001), colon cancer screening (APD, 8.20%; 95% CI, 7.65%-8.75%; P < .001), tobacco counseling and intervention (APD, 12.67%; 95% CI, 11.84%-13.50%; P < .001), influenza vaccination (APD, 9.76%; 95% CI, 9.47%-10.05%; P < .001), pneumococcal vaccination (APD, 5.41%; 95% CI, 4.85%-6.00%; P < .001), and depression screening (APD, 4.85%; 95% CI, 4.66%-5.04%; P < .001). CONCLUSIONS AND RELEVANCE: In this cohort study of patients with telemedicine exposure, there was a largely favorable association with quality of primary care. This supports telemedicine’s value potential for augmenting care capacity, especially in chronic disease management and preventive care. This study also identifies a need for understanding relationships between the optimal blend of telemedicine and in-office care.