• Michael T Kemp, Daniel R Liesman, Craig S Brown, Aaron M Williams, Ben E Biesterveld, Glenn K Wakam, Jesse K Wilson, and Hasan B Alam.
• Department of Surgery, University of Michigan, Ann Arbor, MI.
• J. Am. Coll. Surg. 2020 Dec 1; 231 (6): 695-702.

BackgroundWith the growing use of telehealth, understanding factors affecting patient follow-up in traditional and telehealth settings is important. Few data exist examining the use of telehealth compared with traditional settings. Bridging this gap is critical to optimizing telehealth use and reducing barriers.Study DesignThis is a retrospective cohort study of return and postoperative (electronic video [eClinic] and traditional) visits from January 2018 to 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.ResultsPatients from the eClinic had twice the odds of no-show compared with those from a 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 had greater odds of no-show compared to Caucasian patients (odds ratio [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 with 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.ConclusionsSeveral 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 geographic location. Understanding these patterns allows for prospective identification of barriers and development of interventions to optimize access and patient care.Copyright © 2020 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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