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

Abstract

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. To examine whether quality of care among patients exposed to telemedicine differs from patients with only in-person office-based care. 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. Patients completing telemedicine (video) visits. χ2 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. 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 A1c 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). 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.