Efficient patient care in the digital age: impact of online appointment scheduling in a medical practice and a university hospital on the "no-show"-rate

Abstract

Background: Online appointment scheduling (OAS) increases patient satisfaction and enables more efficient care.

Method: A retrospective study in an ophthalmology practice and an ophthalmology university hospital. Over 20 months, all booked practice-appointments before and after OAS implementation were recorded. Rates of cancellations/rescheduling and unexcused absences ("no-shows") were compared. During the same period, OAS usage, no-show rates, and related factors were analyzed in the hospital.

Results: During the observation period, 16,894 appointments were booked in the practice and 81,173 in the hospital. In both, the rate of appointments scheduled via OAS increased continuously, with an average rate of 22.8% in the practice and 7.2% in the hospital. The no-show rate in the practice was lower for appointments booked online compared to those booked offline (median (x¯) 1.8% vs. 5.9%, p < 0.0001), whereas it was higher in the hospital (x¯ 14.3% vs. 11.2%, p < 0.0001). Regular consultations and SMS reminders were most effective in reducing no-shows in the hospital (Odds Ratio (OR) 0.40 and OR 0.93). The implementation of OAS in the practice reduced the rates of unused appointments (x¯ 22.7% vs. 10.3%, p < 0.0001) and never booked appointments (x¯ 8.6% vs. 1.6%, p < 0.0001), thereby increasing the utilization of available appointments (p < 0.0001).

Conclusion: OAS improves flexibility and resource use in the practice. In the hospital, SMS reminders mostly reduce no-shows, prompting development of a comprehensive reminder model.

Keywords: efficient patient care; online appointment scheduling; ophthalmology; private practice; university hospital.

Figure 1

Utilization of online appointment scheduling in practice and university hospital. (A) Percentage and (B) absolute number of appointments booked via online appointment scheduling relative to all scheduled appointments during the observation period. Line of best fit for practice (blue): Y = 0,37 X + 17,7, R² = 0,15; Line of best fit for university hospital (orange):: Y = 0,12 X + 13,1, R² = 0,24.

Figure 2

No-Show rates for online vs. Offline Booked Appointments. (A) Percentage of missed appointments, (B) percentage of canceled/rescheduled appointments, and (C) percentage of no-shows relative to all (blue), exclusively online (green), and exclusively offline (light gray) booked appointments in the ophthalmology practice. The vertical dark gray line marks the implementation of online appointment scheduling in the practice. Statistical comparisons between online and offline booked appointments were made using the chi-square test. (D) Percentage of no-shows relative to all (orange), exclusively online (green), and exclusively offline (light gray) booked appointments at the University Hospital, Department of Ophthalmology. Statistical comparisons between online and offline booked appointments were made using the chi-square test. (E) Statistical representation of the odds ratios (OR) for various factors associated with a no-show appointment at the University Hospital, Department of Ophthalmology. Eleven months (light gray) are compared to July (this month is excluded from the list, as it exhibited the lowest no-show potential). Online requests are compared to appointments scheduled by a specialist. Male patients are compared to female patients. Age is presented relative to years of life. Appointments with SMS reminders are compared to those without reminders. Specialized consultations (Private, NP, neuroophthalmology and pediatrics; IVI, intravitreal injections) are compared to all other regular patients.

Figure 3

Impact of SMS reminders on No-show rates at the university hospital. (A) Percentage of appointments with SMS reminders relative to all scheduled appointments during the observation period. Line of best fit: Y = 0.76 X + 12.5, R² = 0.85. (B) Correlation between the percentage of appointments with SMS reminders and the no-show rate percentage. Spearman r = −0.67; 95% confidence interval: −0.86 to −0.31, p = 0.0013. The trend curve was created using linear regression analysis, R² = 0.44.

Figure 4

Efficiency improvement through enhanced resource utilization following the Introduction of online appointment scheduling in a medical practice. (A) Percentage of unused appointments (due to cancellations/reschedulings/no-shows/never booked appointments) and (B) specifically, the percentage of never booked appointments relative to all available appointments. The vertical dark gray line marks the introduction of online appointment scheduling in the practice. Statistical comparisons between the pre- and post-introduction phases were made using the chi-square test.

Figure 5

Correlation between increased efficiency and a higher utilization of available appointments with a growing share of online booked appointments. Correlation between the percentage of appointments booked via online appointment scheduling (Figure 1, Practice) and the percentage of unused appointments (Figure 4A). Spearman r = −0.82; 95% confidence interval: −0.93 to −0.58, p < 0.0001. The curve of best fit was created using nonlinear regression analysis, R² = 0.87. A hypothetical intersection at X = 100, Y = 2.36 was assumed. The Y-value corresponds to the average no-show rate for online-booked appointments between March 2023 and April 2024 (Figure 2C).