A data-driven approach to optimizing waiting times in outpatient pharmacy services: Interrupted time series analysis.

Background: Operational efficiency in outpatient pharmacies is a critical factor in healthcare delivery, directly impacting patient satisfaction and adherence to prescribed treatments. Prolonged waiting times in pharmacies can lead to patient dissatisfaction, reduced medication adherence, and potential health risks. Objective: This study aimed to analyze the impact of a data-driven intervention on reducing patient waiting times in an outpatient pharmacy at a tertiary hospital, with a goal of ensuring that patients are served within 30 min of ticket issuance. Methods: The study utilized data from the "Qsmart" ticketing system, covering October 2022 to November 2023. A descriptive analysis was conducted to identify peak service hours and assess staffing patterns. An interrupted time series analysis (ITSA) was employed to evaluate the effectiveness of an intervention implemented between January 22 and February 26, 2023. The intervention included increased staffing during peak hours, adjustments to break schedules, and enhanced pre-peak hour preparations. Results: The descriptive analysis revealed peak service hours between 9 AM and 11 AM, with the highest number of tickets issued at 10 AM. The intervention produced a significant immediate level reduction in waiting times of 0.1540 (95 %CI: 0.0421,0.2659) but there was no additional post-intervention slope change, indicating that the improvement was not progressively increasing over time. Conclusion: The data-driven intervention effectively reduced waiting times in the outpatient pharmacy, with significant immediate improvements observed. This study highlights the potential of strategic operational adjustments to enhance service efficiency and patient satisfaction. Further research is needed to validate the sustainability and generalizability of these findings in other settings.