Factors Associated With No-Show to Ambulatory Tele-Video Neurology Visits

Introduction Telehealth visits (TH) have become an important pillar of healthcare delivery during the COVID pandemic. No-shows (NS) may result in delays in clinical care and in lost revenue. Understanding the factors associated with NS may help providers take measures to decrease the frequency and impact of NS in their clinics. We aim to study the demographic and clinical diagnoses associated with NS to ambulatory telehealth neurology visits. Methods We conducted a retrospective chart review of all telehealth video visits (THV) in our healthcare system from 1/1/2021 to 5/1/2021 (cross-sectional study). All patients at or above 18 years of age who either had a completed visit (CV) or had an NS for their neurology ambulatory THV were included. Patients having missing demographic variables and not meeting the ICD-10 primary diagnosis codes were excluded. Demographic factors and ICD-10 primary diagnosis codes were retrieved. NS and CV groups were compared using independent samples t-tests and chi-square tests as appropriate. Multivariate regression, with backward elimination, was conducted to identify pertinent variables. Results Our search resulted in 4,670 unique THV encounters out of which 428 (9.2%) were NS and 4,242 (90.8%) were CV. Multivariate regression with backward elimination showed that the odds of NS were higher with a self-identified non-Caucasian race OR = 1.65 (95%, CI: 1.28-2.14), possessing Medicaid insurance OR = 1.81 (95%, CI: 1.54-2.12) and with primary diagnoses of sleep disorders OR = 10.87 (95%, CI: 5.55-39.84), gait abnormalities (OR = 3.63 (95%, CI: 1.81-7.27), and back/radicular pain OR = 5.62 (95%, CI: 2.84-11.10). Being married was associated with CVs OR = 0.74 (95%, CI: 0.59-0.91) as well as primary diagnoses of multiple sclerosis OR = 0.24 (95%, CI: 0.13-0.44) and movement disorders OR = 0.41 (95%, CI: 0.25-0.68). Conclusion Demographic factors, such as self-identified race, insurance status, and primary neurological diagnosis codes, can be helpful to predict an NS to neurology THs. This data can be used to warn providers regarding the risk of NS.