Using queuing theory and simulation model to optimize hospital pharmacy performance

Optimisation of medication reconciliation using queueing theory: a computer experiment

BACKGROUND

Medication reconciliation (MedRec) in hospitals is an important tool to enhance the continuity of care, but completing MedRec is challenging.

AIM

The aim of this study was to investigate whether queueing theory could be used to compare various interventions to optimise the MedRec process to ultimately reduce the number of patients discharged prior to MedRec being completed. Queueing theory, the mathematical study of waiting lines or queues, has not been previously applied in hospital pharmacies but enables comparisons without interfering with the baseline workflow.

METHOD

Possible interventions to enhance the MedRec process (replacing in-person conversations with telephone conversations, reallocating pharmacy technicians (PTs) or adjusting their working schedule) were compared in a computer experiment. The primary outcome was the percentage of patients with an incomplete discharge MedRec. Due to the COVID-19 pandemic, it was possible to add a real-life post hoc intervention (PTs starting their shift later) to the theoretical interventions. Descriptive analysis was performed.

RESULTS

The queueing model showed that the number of patients with an incomplete discharge MedRec decreased from 37.2% in the original scenario to approximately 16% when the PTs started their shift 2 h earlier and 1 PT was reassigned to prepare the discharge MedRec. The number increased with the real-life post hoc intervention (PTs starting later), which matches a decrease in the computer experiment when started earlier.

CONCLUSION

Using queueing theory in a computer experiment could identify the most promising theoretical intervention to decrease the percentage of patients discharged prior to MedRec being completed.

Analyzing the queuing theory at the emergency department at King Hussein cancer center

OBJECTIVES

This study was conducted in 2022 at King Hussein Cancer Center (KHCC) to analyze the queuing theory approach at the Emergency Department (ED) to estimate patients' wait times and predict the accuracy of the queuing theory approach.

METHODS

According to the statistics, the peak months were July and August, with peak hours from 10 a.m. until 6 p.m. The study sample was a week in July 2022, during the peak days and hours. This study measured patients' wait times at these three stations: the health informatics desk, triage room, and emergency bed area.

RESULTS

The average number of patients in line at the health informatics desk was not more than 3, and the waiting time was between 1 and 4 min. Since patients were receiving the service immediately in the triage room, there was no waiting time or line because the nurse's role ended after taking the vital signs and rating the patient's disease acuity. Using equations of queuing theory and other relativistic equations in the emergency bed area gave different results. The queuing theory approach showed that the average residence time in the system was between 4 and 10 min.

CONCLUSIONS

Conversely, relativistic equations (ratios of served patients and departed patients and other related variables) demonstrated that the average residence time was between 21 and 36 min.

Improving service efficiency and throughput of cardiac surgery patients using Monte Carlo simulation: a queueing setting

Bed occupancy rate (BOR) is important for healthcare policymakers. Studies showed the necessity of using simulation approach when encountering complex real-world problems to plan the optimal use of resources and improve the quality of services. So, the aim of the present study is to estimate average length of stay (LOS), BOR, bed blocking probability (BBP), and throughput of patients in a cardiac surgery department (CSD) using simulation models. We studied the behavior of a CSD as a complex queueing system at the Farshchian Hospital. In the queueing model, customers were patients and servers were beds in intensive care unit (ICU) and post-operative ward (POW). A computer program based on the Monte Carlo simulation, using Python software, was developed to evaluate the behavior of the system under different number of beds in ICU and POW. The queueing simulation study showed that, for a fixed number of beds in ICU, BOR in POW decreases as the number of beds in POW increases and LOS in ICU increases as the number of beds in POW decreases. Also, based on the available data, the throughput of patients in the CSD during 800 days was 1999 patients. Whereas, the simulation results showed that, 2839 patients can be operated in the same period. The results of the simulation study clearly demonstrated the behavior of the CSD; so, it must be mentioned, hospital administrators should design an efficient plan to increase BOR and throughput of patients in the future.

Specifications of a Queuing Model-Driven Decision Support System for Predicting the Healthcare Performance Indicators Pertaining to the Patient Flow

This article has developed specifications for a new model-driven decision support system (DSS) that aids the key stakeholders of public hospitals in estimating and tracking a set of crucial performance indicators pertaining to the patients flow. The developed specifications have considered several requirements for ensuring an effective system, including tracking the performance indicator on the level of the entire patients flow system, paying attention to the dynamic change of the values of the indicator’s parameters, and considering the heterogeneity of the patients. According to these requirements, the major components of the proposed system, which include a comprehensive object-based queuing model and an object-oriented database, have been specified. In addition to these components, the system comprises the equations that produce the required predictions. From the system output perspective, these predictions act as a foundation for evaluating the performance indicators as well as developing policies for managing the patients flow in the public hospitals.

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

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.

Recent Iranian Health System Reform: An Operational Perspective to Improve Health Services Quality

The operational management of healthcare services is expected to directly touch patient experiences. Iranian Ministry of Health and Medical Education (MoHME) for the first time, as such, has sought to improve the operational management of healthcare delivery within a reform agenda by setting benchmarks for ‘number of visit per hour’ and waiting time in outpatient clinics of about 700 affiliated hospitals. As a new initiative, it has faced with mixed reactions and various doubts have been cast on its successful implementation. This manuscript aims to shed some light on the operational challenges of the initiative and the requirements of its successful implementation.

Impact of value added services on patient waiting time at the ambulatory pharmacy Queen Elizabeth Hospital

Background:

Value added services (VAS) are an innovative dispensing system created to provide an alternative means of collecting partial drug supply from our hospital. This in turn was projected to reduce the necessity for patient to visit pharmacy counter and thus reduce the burden of prescription handling.

Objective:

To evaluate the impact of increased VAS uptake following promotional campaign towards patient waiting time and to explore factors that may affect patient waiting time at the Ambulatory Pharmacy, Queen Elizabeth Hospital.

Methods:

A quasi experimental study design was conducted from September 2014 till June 2015 at the Ambulatory Pharmacy. During pre-intervention phase, baseline parameters were collected retrospectively. Then, VAS promotional campaign was carried out for six months and whilst this was done, the primary outcome of patient waiting time was measured by percentage of prescription served less than 30 minutes. A linear regression analysis was used to determine the impact of increased VAS uptake towards patient waiting time.

Results:

An increased in percentage of VAS registration (20.9% vs 35.7%, p<0.001) was observed after the promotional campaign. The mean percentage of prescription served less than 30 minutes increased from 83.2% SD=15.9 to 90.3% SD=11.5, p=0.001. After controlling for covariates, it was found that patient waiting time was affected by number of pharmacy technicians (b=-0.0349, 95%CI-0.0548 : -0.0150, p=0.001), number of pharmacy counters (b=0.1125, 95%CI 0.0631 : 0.1620, p<0.001), number of prescriptions (b=0.0008, 95%CI 0.0004 : 0.0011, p<0.001), and number of refill prescriptions (b=0.0004, 95%CI 0.0002 : 0.0007, p<0.001). The increased in percentage of VAS registration was associated with reduction in number of refill prescription (b=-2.9838, 95%CI -4.2289 : -1.7388, p<0.001).

Conclusions:

Patient waiting time at the Ambulatory Pharmacy improved with the increased in VAS registration. The impact of increased VAS uptake on patient waiting time resulted from reduction in refill prescriptions. Patient waiting time is influenced by number of pharmacy technicians, number of pharmacy counters, number of prescriptions and number of refill prescriptions.

Bed Capacity Planning Using Stochastic Simulation Approach in Cardiac-surgery Department of Teaching Hospitals, Tehran, Iran

BACKGROUND

To determine the hospital required beds using stochastic simulation approach in cardiac surgery departments.

METHODS

This study was performed from Mar 2011 to Jul 2012 in three phases: First, collection data from 649 patients in cardiac surgery departments of two large teaching hospitals (in Tehran, Iran). Second, statistical analysis and formulate a multivariate linier regression model to determine factors that affect patient's length of stay. Third, develop a stochastic simulation system (from admission to discharge) based on key parameters to estimate required bed capacity.

RESULTS

Current cardiac surgery department with 33 beds can only admit patients in 90.7% of days. (4535 d) and will be required to over the 33 beds only in 9.3% of days (efficient cut off point). According to simulation method, studied cardiac surgery department will requires 41-52 beds for admission of all patients in the 12 next years. Finally, one-day reduction of length of stay lead to decrease need for two hospital beds annually.

CONCLUSION

Variation of length of stay and its affecting factors can affect required beds. Statistic and stochastic simulation model are applied and useful methods to estimate and manage hospital beds based on key hospital parameters.