The Impact of One-Dose Package of Medicines on Patient Waiting Time in Dispensing Pharmacy: Application of a Discrete Event Simulation Model

Abstract

Improvement in patient waiting time in dispensing pharmacies is an important element for patient and pharmacists. The One-Dose Package (ODP) of medicines was implemented in Japan to support medicine adherence among elderly patients; however, it also contributed to increase in patient waiting times. Given the projected increase in ODP patients in the near future owing to rapid population aging, development of improved strategies is a key imperative. We conducted a cross-sectional survey at a single dispensing pharmacy to clarify the impact of ODP on patient waiting time. Further, we propose an improvement strategy developed with use of a discrete event simulation (DES) model. A total of 673 patients received pharmacy services during the study period. A two-fold difference in mean waiting time was observed between ODP and non-ODP patients (22.6 and 11.2 min, respectively). The DES model was constructed with input parameters estimated from observed data. Introduction of fully automated ODP (A-ODP) system was projected to reduce the waiting time for ODP patient by 0.5 times (from 23.1 to 11.5 min). Furthermore, assuming that 40% of non-ODP patients would transfer to ODP, the waiting time was predicted to increase to 56.8 min; however, introduction of the A-ODP system decreased the waiting time to 20.4 min. Our findings indicate that ODP is one of the elements that increases the waiting time and that it might become longer in the future. Introduction of the A-ODP system may be an effective strategy to improve waiting time.

Discrete-Event Simulation in Healthcare Settings: A Review

“…The health care system as a whole was studied 11 times (5.5%), primarily for system diagnosis purposes (validating different DES models and techniques). There was one study each of emergency medical transport (EMT) (Nawaz et al, 2018), a pharmacy (Furushima et al, 2018), and a medical supply distributor (Al-Fandi et al, 2019) (0.5% each). Table 1.…”