Increased Time between Scheduling Date and Appointment Date Results in Increased No-Show Rates in the Academic Urology Practice

Abstract

Introduction: No-show appointments can weigh heavily on a urology practice's finances and productivity. Our objective was to investigate if a relationship existed between lag time and no-show appointments at the Columbia University Medical Center department of urology. Methods: We queried adult new patient appointments from July 2017 to July 2018 and excluded rescheduled or cancelled visits. We organized appointments by subspecialty training/practice of the urologist (general urology, voiding dysfunction/female urology, male sexual dysfunction/infertility, urological oncology, endourology and reconstructive urology). We performed logistic regression analysis to determine the relationship between lag time and no-show rate. We did the same for age and gender. We also organized lag time into 4 categories (less than 3 days, 3 to 7 days, 8 to 14 days, more than 14 days) and performed a goodness of fit test for no-show rates. Results: A total of 6,060 new patient appointments were scheduled from July 2017 to July 2018. The no-show rate was 14.3% (865 patients). Each daily increase in lag time resulted in a 2% rise in the odds of no-shows for the overall practice (OR 1.02). There were similar results for sexual dysfunction (OR 1.03), general urology (OR 1.02), oncology (OR 1.02) and voiding dysfunction (OR 1.01). There was a positive correlation with increasing lag time category and no-show rates for all subspecialties (R 2 >0.80) except reconstructive urology (R 2 ¼0.68). Each increase in age resulted in a 2% rise in the odds of no-shows (OR 0.98). Conclusions: Lag time for new patient visits is highly correlated with no-show rates, with a 2% rise in the odds of a no-show with each daily increase in lag time. Increasing age also demonstrated a correlation with no-show rates. Practice interventions to reduce lag time will hopefully reduce no-show rates.

Failure to Attend Urology Appointments: A Retrospective Analysis of a Single Urban Academic Practice

“…4 Caputo et al found 14.3% and Nguyen et al found 13.8%. 8,9 Other studies have reported no-show rates between 18.8% and 35% across all adult specialties. 2,3,8,10 It is unclear if our lower nonattendance rate is based on the patient population we serve, our practice model or the fact that our practice is highly subspecialized.…”

“…8,9 Other studies have reported no-show rates between 18.8% and 35% across all adult specialties. 2,3,8,10 It is unclear if our lower nonattendance rate is based on the patient population we serve, our practice model or the fact that our practice is highly subspecialized. In Han et al’s study population, there was a lower percentage of White vs Black and a lower median income for the no-show population.…”

“…Other studies have similarly reported increased nonattendance rates with lead times greater than 30 days. 7,8,17 Drewek et al instituted automated reminder systems starting 1 week prior to appointments with daily reminders until confirmations were received, yet there was no significant impact on nonattendance rate. 17 New types of reminder systems may be needed, with reminders earlier than 7 days prior to the appointment, or perhaps alternatively practice policy can be revised to only schedule appointments within 7 to 21 days.…”

“…Several recent studies reviewed nonattendance at academic urology practices with a focus on type of provider, lead time and socioeconomic factors. 4,7–9 We had the impression that our practice had lower rates of missed appointments, and we had the ability to study several additional factors, eg previous history of missed appointments, office procedures and telemedicine. We hypothesized that our nonattendance rate would be significantly lower than what has been published.…”

Editorial Commentary

“…There are multiple factors that may influence this, including lag time between the scheduling and appointment date and patient age. 1 I would encourage future studies to analyze what differentiates the risk factors for a telehealth vs in-office appointment completion rate. These data could help shape future innovation and aid in developing predictive models of the most appropriate form of followup and care coordination to allow us to best meet the needs of individual patients.…”