Factors Associated with Increased Risk of Patient No-Show in Telehealth and Traditional Surgery Clinics

Kemp, Michael T.; Liesman, Daniel R.; Brown, Craig S.; Williams, Aaron M.; Biesterveld, Ben E.; Wakam, Glenn K.; Wilson, Jesse K.; Alam, Hasan B.

BACKGROUND: With growing telehealth utilization, understanding factors affecting patient follow-up in traditional and telehealth settings is important. Few data exist examining the utilization of telehealth compared to traditional settings. Bridging this gap is critical to optimizing telehealth utilization and reducing barriers. STUDY DESIGN: A retrospective cohort study of return and post-operative (electronic video [eClinic] and traditional) visits from January 2018 - March 2020 at single tertiary care center. There were 12,359 unique first-encounter patients with 903 eClinic and 11,456 traditional visits. 11,547 patients completed visits while 812 patients did not show up. Multivariable logistic regression modeling was performed to identify factors associated with no-show. County-level mapping was used to identify patterns in no-show rates. RESULTS: eClinic patients had twice the odds of no-show compared to traditional clinic (p<0.001). Age was inversely proportional to odds of no-show with each additional decade associated with a 16% decrease in these odds (p<0.001). African-American patients have greater odds of no-show compared to Caucasian patients (OR 2.47; 95% CI 1.95-3.13, p<0.001). Marital statuses of single and legal separation were associated with higher odds of no-show compared to married marital status (p<0.001 and p=0.04, respectively). Minimally-invasive and endocrine surgery clinics had lower odds of no-show compared with acute care surgery clinic (p<0.001 for both). County-level no-show rates demonstrate similar patterns between clinic settings. CONCLUSIONS: Several factors are associated with increased odds of no-show including the visit being in eClinic. County-level analysis suggests no-show variation is not dependent on geographical location. Understanding these patterns allows for prospective identification of barriers and development of interventions to optimize access and patient care.