Identifying Patterns of Primary Care In-Person and Telemedicine Use in the Veterans Health Administration: A Latent Class Analysis

• Published in: Journal of general internal medicine : JGIM Vol. 39; no. 12; pp. 2241 - 2248
• Main Authors: Staloff, Jonathan, Gunnink, Eric, Rojas, Jorge, Wong, Edwin S., Nelson, Karin, Reddy, Ashok
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2024; Springer Nature B.V
• Subjects: Adult; Aged; Comorbidity; COVID-19; COVID-19 - epidemiology; Driving ability; Female; Humans; Internal Medicine; Latent Class Analysis; Male; Medicine; Medicine & Public Health; Middle Aged; Original Research; Pandemics; Primary care; Primary Health Care - statistics & numerical data; Retrospective Studies; Subgroups; Telemedicine; Telemedicine - statistics & numerical data; United States; United States Department of Veterans Affairs; Veterans - statistics & numerical data; United States; telemedicine; primary care; Veterans Health Administration
• ISSN: 0884-8734; 1525-1497; 1525-1497
• DOI: 10.1007/s11606-024-08751-5

Background The Veterans Health Administration increased synchronous telemedicine (video and telephone visits) in primary care in response to the COVID-19 pandemic. Objective Our objective was to determine veteran use patterns of in-person and telemedicine primary care when all modalities were available. Design A retrospective cohort analysis. We performed a latent class analysis of primary care visits over a 1-year period to identify veteran subgroup (i.e., class) membership based on amount of primary care use and modality used. Then, we used multinomial logistic regression with a categorical outcome to identify patient characteristics associated with class identification. Participants A random national sample consisting of 564,580 primary care empaneled veterans in June 2021. Main Measures Latent class membership. Key Results We identified three latent classes: those with few primary care visits that were predominantly telephone-based (45%), intermediate number of visits of all modalities (50%), and many visits of all modalities (5%). In an adjusted model, characteristics associated with the “few” visits class, compared to the intermediate class, were older age, male sex, White race, further driving distance to primary care, higher Gagne, optimal internet speed, and unmarried status (OR 1.002, 1.52, 1.13, 1.004, 1.04, 1.05, 1.06, respectively; p < .05). Characteristics associated with membership in the “many” visits class, compared to the intermediate class, were Hispanic race, higher JEN Frailty Index and Gagne (OR 1.12, 1.11, 1.02, respectively; p < .05), and higher comorbidity by Care Assessment Need score quartile (Q2 1.73, Q3 2.80, Q4 4.12; p < 0.05). Conclusions Veterans accessing primary care in-person or via telemedicine do so primarily in three ways: (1) few visits, predominantly telephone; (2) intermediate visits, all modalities, (3) many visits, all modalities. We found no groups of veterans receiving a majority of primary care through video.