Clinician characteristics and primary care telemedicine use in a large Midwestern healthcare system: A cross-sectional analysis (Preprint)
Year: 2024
Type: preprint
Study design: Observational Study
Abstract: BACKGROUND While many studies have examined the patient characteristics associated with telemedicine use, few have examined the influence of clinicians on patients’ telemedicine use. OBJECTIVE This study assessed clinician contribution to and characteristics associated with primary care visit mode. METHODS Cross-sectional study of adult (18+ years) primary care patients in a large Midwestern healthcare system in 2022. Our main exposure was clinician characteristics and primary outcomes were primary care visit mode (any telemedicine visits vs. in-person visits only) and telemedicine mode (video vs. audio-only). Logistic regression was used to assess the association between outcomes and clinician characteristics. Pseudo r2 and Bayesian Information Criterion were used to assess changes in variance and model fit. RESULTS In 2022, 3.5% of primary care visits were telemedicine visits and 6% of primary care patients had a telemedicine visit (85% had any video visits and 15% had audio-only visits). Controlling for patient characteristics, patients with clinicians with moderate-sized patient panels (aOR: 1.05, 95%CI: 1.03-1.08), more telemedicine visits (aOR: 4.39, 95%CI: 4.30-4.49), and more patients in racial/ethnic minority groups (OR: 1.04, 95%CI: 1.01-1.07) were more likely to have telemedicine visits. Those with clinicians with large patient panels (aOR: 0.88, 95%CI: 0.86-0.90) and more patients with non-English language preference (NELP; aOR: 0.93, 95%CI: 0.91-0.95) were less likely to have telemedicine visits. Among telemedicine users, patients with only audio-only visits were more likely attributed to clinicians with more telemedicine visits (aOR: 1.33, 95%CI: 1.24-1.42), more patients with NELP (aOR: 1.27, 95%CI: 1.19-1.35), and more elderly patients (aOR: 1.34, 95%CI: 1.26-1.42) and less likely attributed to clinicians who are physicians (aOR: 0.82, 95%CI: 0.76-0.89), with larger patient panels (aOR: 0.47, 95%CI: 0.44-0.50 for ≥1400 patients; aOR: 0.82, 95%CI: 0.77-0.88 for 1000-1399 patients) and with more patients in racial/ethnic minority groups (aOR: 0.75, 95%CI: 0.70-0.81). Adding clinician characteristics improved model fit and explained more variability than patient characteristics alone in the primary care visit mode model. CONCLUSIONS Telemedicine use was associated with clinician type and patient panel characteristics. Clinician characteristics explained greater variability in telemedicine use than patient characteristics alone. Healthcare systems may increase telemedicine uptake by targeting interventions toward clinicians.
Authors: Eva Chang, Amanda Luff
Institution: Aurora Health Care
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4404833997