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Risk Factors Driving "No-Shows" Across Orthopaedic Subspecialty Outpatient Clinics
John Martin1, Gabriella Rivas, Jason Silvestre
1From the Department of Orthopaedics and Physical Medicine, Medical University of South Carolina, Charleston, SC (J. Martin, Silvestre, Glaser, Reitman, Lawrence, Leddy, and Ravinsky), Trident Medical Center, Department of Internal Medicine, Charleston, SC (Rivas), the Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada (Crawford), and Biomedical Informatics and Data Science, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (B. Martin).
Introduction:
Unattended clinic visits, or patient no-shows, can have harmful effects on patient health and well being and an unfavorable impact on the revenue stream and financial efficiency of the health system. The objective of this study was to assess potential risk factors associated with clinic no-shows across orthopaedic subspecialties in a large, tertiary care, academic, medical center.
Methods:
Clinic appointments labeled as "completed" or "no-show" between January 1, 2021 and June 30, 2023, were retrospectively collected from an outpatient orthopaedic department at a single institution. Patient demographic, socioeconomic, and visit characteristics evaluated as potential risk factors for no-shows included patient age, sex, race, social deprivation index (SDI) at the zip code level, documented social support, employment status, insurance coverage, time between appointment creation and appointment date, orthopaedic subspecialty, appointment type (ie, new versus established patient status), and referral status (ie, self-referred versus referral from a medical provider). Variable selection was done with univariate logistic regression analysis, and adjusted risk of no-shows was estimated using multivariable logistic regression. Matching was done to control for potential confounding from patient age and sex, subspecialty, and appointment type.
Results:
Overall, 148,352 clinic appointments were included in the primary analysis. Multivariable logistic regression analysis before matching cohorts demonstrated statistically significant associations between no-show appointments and advanced age, female sex, self-identified Black or African American race, unemployment, lack of social support, and Foot and Ankle visits. After cohorts were matched, a total of 22,154 clinic appointments were included in the final analysis with an equal number of "completed" and "no-show" visits. Multivariable logistic regression modeling of the matched no-shows and completed visits demonstrated a higher odds of no-show appointments among patients self-identifying as Black/African American, lacking social support, being unemployed, being a student or retiree, having Medicare insurance, and a having a higher SDI.
Conclusion:
Higher odds of patient no-shows were determined for several patient- and visit-level variables, including Black/African American race, lack of social support, unemployment, Medicare insurance, and higher SDI.
Level Of Evidence:
III.
