Improving Patient Experience in Outpatient Clinics through Simulation: A Case Study

Abstract

This research aims to present a case study on the use of simulation to support operational decision-making and improve the patient experience in outpatient clinics. A simulation model was developed to represent patient flow through the endocrine clinics of the internal medicine department in a large hospital in Saudi Arabia. The research evaluated the impact of using simulation models on different aspects of healthcare facility operations, such as patient flow, resource utilization, and staffing. Potential bottlenecks and inefficiencies in the clinic’s processes were identified. Furthermore, improvements were suggested and evaluated that could significantly reduce patient waiting times and increase the number of patients served. Different scenarios and strategies were evaluated without the need for real-world implementation, which can be costly and time consuming. The model can also be easily modified and adapted to accommodate changes in patient demand, staffing levels, or other factors that may impact clinic operations. The findings demonstrate the utility of simulation models in healthcare management. Overall, the use of simulation models in healthcare management has the potential to revolutionize the way clinics and hospitals operate, leading to improved patient outcomes and more efficient use of resources.

1. Introduction

Globally, healthcare institutions are experiencing unprecedented difficulties. High service demands and limited healthcare resources create major challenges for decision-makers on a regular basis [1]. Public healthcare systems are obliged to provide quality and cost-effective care as quickly as possible, which is reflected in the patient experience. Nonetheless, managers often face constraints that prevent them from using the available resources to their full potential.

The space for enhancing care delivery is ample [2,3,4]. Reforms and improvement initiatives on various levels are constantly evaluated and executed. Decision-makers are continuously on the lookout for means to maximize the number of treated patients, minimize operation costs, and reach an optimal level of asset utilization, all while maintaining or increasing the quality of the healthcare service.

Outpatient clinics in particular face challenges that affect their effectiveness and operations [5]. Unlike other departments such as operating rooms or intensive care units, outpatient clinics handle high patient volumes within short time frames, requiring efficient scheduling and resource management. These unique dynamics make outpatient clinics crucial to overall healthcare efficiency. Common problems include limited resources, increased demand for services, administrative burdens, staffing challenges, technology adoption, and financial sustainability. They can vary in frequency and severity depending on many factors, such as the size and location of the clinic, the patient population served, and the resources available. Failure to adequately address these challenges can lead to longer wait times for patients, reduced quality of care, and financial difficulties for the clinic.

Simulation can be used to support decision making and improve patient experience in healthcare facilities [6,7,8,9,10,11]. Some of the realized benefits include:

• Improved patient flow: Simulation models can help healthcare facilities identify bottlenecks and inefficiencies in their patient flow, allowing them to make improvements that can reduce wait times and improve patient satisfaction.
• Reduced costs: Simulation models can help healthcare facilities identify opportunities to reduce costs, such as by optimizing staffing levels or improving resource utilization.
• Better resource allocation: Simulation models can help healthcare facilities better allocate resources, such as staff and equipment, to ensure that they are being used effectively and efficiently.
• Improved quality of care: Simulation models can help healthcare facilities identify opportunities to improve the quality of care they provide to patients, such as by reducing errors or improving patient outcomes.
• Enhanced decision-making: Simulation models can provide healthcare facilities with data-driven insights that can inform decision-making and help them make more informed choices about how to allocate resources and improve operations.

Several studies highlight the need to enhance outpatient services in Saudi Arabia due to growing concerns over patient satisfaction with clinic visits. For instance, a study conducted in Riyadh found that while many patients expressed satisfaction with certain aspects of their clinic experience, significant areas such as waiting times and appointment availability were identified as needing improvement [12]. Similarly, patient satisfaction at tertiary care facilities in Riyadh was reported at 73.77%, with accessibility and communication receiving mixed reviews, further emphasizing the need for models that can streamline outpatient processes [13]. Another study assessing ophthalmology clinics in the Makkah region found that 75% of patients were satisfied with services, but factors like patient age and comorbidities greatly influenced satisfaction levels [14]. These findings align with research at a tertiary care hospital, which revealed that patient satisfaction was significantly impacted by waiting times, with only 50% of patients being satisfied [15]. Given these persistent issues, developing a model to optimize outpatient clinic operations is crucial for improving patient experiences and overall healthcare quality.

Nevertheless, previous research has established that simulation applications in the Saudi healthcare sector are rare [16]. The few applications of simulation have yielded tangible benefits. Healthcare providers in Saudi Arabia believe that simulation has great potential to enhance healthcare facilities and improve the patient experience. In general, the challenges to implementing simulation are similar to those in other geographic regions such as Europe and America [17,18,19,20].

There has been a notable trend toward outpatient care in Saudi Arabia and other countries in the region. This shift toward outpatient services necessitates efficient processes and workflows to manage patient flow and ensure timely care delivery. Simulation allows healthcare providers to test and optimize these processes in a controlled environment before implementation, thus improving patient satisfaction and outcomes.

Saudi Arabia and other countries in the region are particularly interesting regarding healthcare simulation due to several factors. The region’s rapid healthcare expansion, driven by initiatives like Saudi Arabia’s Vision 2030, demands the effective management of patient flow, resource allocation, and service delivery. Simulation models are instrumental in optimizing these aspects across various healthcare settings, including outpatient clinics [21].

The focus on improving healthcare efficiency and quality further drives the adoption of simulation. Applying the simulation model in Saudi Arabia is particularly advantageous due to the ease of accessing data and understanding regional conditions, making the simulation process more effective and relevant.

The large size and cultural diversity of Saudi Arabia, along with its similarities to other countries in the region in terms of economic and cultural factors, make it a valuable location for implementing simulation models and studying their outcomes. Therefore, applying a practical model in Saudi Arabia and studying it can have benefits for a wider geographic range of countries with similar challenges and opportunities in the region.

The objective of this research is to present a case study on the use of simulation to support operational decision-making and improve patient experience in outpatient clinics. The research will evaluate the impact of using simulation models on different aspects of healthcare facility operations, such as patient flow, resource utilization, and staffing.

The subsequent sections of this article are structured as follows: Section 2 provides a detailed review of related work, highlighting the existing research on patient flow optimization and healthcare simulation models. The section also discusses the state of simulation in the Saudi healthcare context, identifying both the growing interest and the challenges faced in adopting simulation models. Section 3 describes the system under study, focusing on the specifics of the endocrine clinic and the rationale behind its selection for the simulation. Section 4 outlines the construction of the simulation model using Simio, explaining the methodology, inputs, and assumptions made. Section 5 presents the results of the simulation, analyzing the outcomes of various scenarios and their impact on patient flow. Finally, Section 6 discusses the conclusions drawn from the study and provides recommendations for future work, including potential real-life implementation of the simulation results in healthcare settings.

2. Related Work

There have been considerable efforts over the past decade to address challenges in healthcare management through simulation. An increasing number of publications aimed at resolving a wide range of healthcare issues were published as the popularity of simulation in this field continued to rise [16]. Roy et al. [22] provided a comprehensive review and classification of the literature on healthcare simulation, focusing on research that deals with operations management difficulties at different tiers of healthcare service provision. They found that, despite the widespread use of simulation to solve operational management issues in healthcare, experts believe its deployment is still in its emerging stages.

The use of simulation to model healthcare systems has gained traction among researchers aiming to provide decision-makers with more accurate representations of complex processes. Several authors have employed simulation models to reproduce the behavior of healthcare systems [23,24,25,26,27]. The primary objective of these studies is to provide decision-makers with the tools to assess system efficiency and explore different scenarios for potential improvements. For example, Chemweno et al. [28] simulated the care delivery process for stroke patients throughout their treatment pathway, starting from their arrival at the emergency department and continuing until their final care in a nursing home. The study further analyzed upstream departments, including rehabilitation wards and nursing homes, to identify effective intervention points to reduce patient wait times for bed resources.

Simulation has also been applied as a decision-support tool for managing staffing levels in emergency departments. Ahmed and Alkhamis [2] investigated the potential of combining simulation and optimization techniques to support decision-making in an emergency department unit at a public hospital in Kuwait. The primary focus was to compare the effects of varying staffing levels on service effectiveness, allowing hospital administrators to make informed decisions about staffing that would maximize patient throughput while minimizing wait times within budgetary constraints. Their study outlined a practical approach to optimizing staff size based on these objectives.

Similarly, Abo-Hamad and Arisha [29] presented an interactive simulation-based decision support framework for healthcare processes, specifically focusing on emergency departments. Their findings showed that improving the management of inpatient beds had a more significant impact on overall efficiency than increasing the physical capacity or workforce of emergency departments. This highlights the importance of resource allocation within a hospital setting and the potential of simulation to guide such decisions.

In addition to emergency departments, simulation has been effectively applied in surgical settings. Saadouli et al. [30] proposed a stochastic optimization and simulation approach to schedule orthopedic surgeries and recovery beds. The model aimed to reduce patient wait times in operating rooms and during recovery, while also minimizing the likelihood of overlapping recovery periods for different patients. The study’s outcomes demonstrated how simulation can assist in improving surgical scheduling and patient flow management.

For outpatient clinics, Baril et al. [31] explored the relationship between patient flows, resource capacity, and appointment scheduling using discrete event simulation. They investigated how consulting rooms and nurses could be allocated to different orthopedists based on varying patterns of patient flow, considering the complexity of outpatient processes. By customizing the availability of resources such as consulting rooms and nurses to match patient flow patterns, the study concluded that the performance of outpatient orthopedic clinics could be significantly enhanced.

Several other studies have demonstrated the successful application of simulation models as real-life planning tools for healthcare. Karakra [32] introduced a digital twin system using discrete event simulation to provide real-time monitoring and predict patient pathways in hospitals. This approach allowed for better decision-making and proactive management of patient flow. Ruiz et al. [33] also highlighted the use of simulation-based decision support systems to optimize healthcare processes, showing successful implementation in clinical settings to improve operational efficiency. Furthermore, Lal et al. [34] and Zhang et al. [35] provided examples of simulation-based optimization tools used for improving hospital patient assignments and resource allocation. Despite these advancements, challenges such as integrating new systems with existing infrastructure and overcoming resistance to technological adoption in healthcare settings remain significant hurdles.

In the Saudi healthcare sector, Alrabghi [16] conducted an empirical study to examine the state of simulation practices, revealing limited adoption due to various challenges. The study surveyed healthcare professionals and found that while simulation is not widely used in Saudi healthcare, there is growing interest in its potential benefits. The lack of resources, equipment, and adequate training on simulation techniques was identified as a major obstacle to broader adoption. However, the study emphasized that many healthcare professionals in Saudi Arabia recognize the potential of simulation to enhance care delivery by improving efficiency, reducing costs, and increasing patient safety.

Overall, the reviewed studies demonstrate the increasing use of simulation in healthcare management to address operational challenges and support decision-making. Simulation models have been successfully employed in diverse areas, including stroke patient care, emergency department staffing, surgical scheduling, and outpatient flow optimization. These models have shown potential in reducing patient wait times, decreasing length of stay, and enhancing the overall efficiency of healthcare services [2,5,6,22,23,25,26,27,29,30,31]. However, experts acknowledge that simulation in healthcare is still in its early stages, and further research is needed to fully harness its potential. By integrating simulation models into healthcare management, facilities can make informed decisions and assess the impact of proposed changes before real-world implementation.

3. System Description

We consider the endocrine patient flow in the internal medicine department that is located in a large hospital in Saudi Arabia. The endocrine clinic was selected for this study due to the ability to gather comprehensive data and the strong collaboration from the clinic’s staff. Although endocrine patients make up 30% of the total patient population in the internal medicine department, this made the clinic an ideal setting for studying patient flow. This focused approach allows for detailed insights into patient flow management that can be extrapolated to other outpatient settings.

Patients are treated in outpatient clinics nine hours per day, five days a week. The patient flow is illustrated in Figure 1.

The process starts when a patient enters the department and registers in the reception. When a triage room becomes available, the patient is transferred there to record the vitals and undertake any necessary pre-checks by a nurse. Next, the patient will proceed to the appointed clinic to be treated by the doctor. Altogether, there are 8 clinic rooms in the department. However, in this research, the focus is on endocrinology clinics, which are clinic rooms 5 and 6.

4. Simulation Model

4.1. Data Sources

The data used for the experiment were obtained from historical and observational data over a six-month period. This timeframe was chosen to capture a comprehensive and representative sample of the clinic’s operations. To ensure the data reflected everyday work, the authors monitored activities regularly and verified with staff that the data aligned with typical workflows.

The types of data collected involve the arrival times of patients and the processing times of the reception, triage, and clinic rooms 5 and 6. Arrival data were extracted from the hospital’s database. However, processing times had to be collected. The hospital staff used stopwatches to record the time patients spent in reception, triage, and clinic rooms.

Microsoft Excel was used to prepare the data. StatFit was used for input data analysis and distribution fitting, which we would later use in the simulation model. The distributions of service times at various stages of the clinic process are provided in

Table 1. Distribution fitting of input data.

Data Type	Statistical Distribution
Patient Interarrival times (minutes)	Lognormal (0.784, 0.969)
Reception process time (minutes)	Uniform (2, 3.99)
Triage process time (minutes)	Uniform (3, 4.95)
Clinic process time (minutes)	Uniform (7, 18.7)

.

Interarrival times for all patients who arrive at the department follow a lognormal distribution with a mean and standard deviation of 0.784 and 0.969 min, respectively. Endocrinology patients represent around 30% of the total patients that arrive at the department. The reception processing time follows a uniform distribution with the following parameters: a maximum of 3.99 min and a minimum of 2 min. Similarly, the triage and clinic processing times follow a uniform distribution with varying parameters as shown in Table 1.

The work schedules for the medical team in the department are shown in

Table 2. Work schedule for the medical team in the department.

Time	08:00 a.m.	08:30 a.m.	09:00 a.m.	09:30 a.m.	10:00 a.m.	10:30 a.m.	11:00 a.m.	11:30 a.m.	12:00 p.m.	12:30 p.m.	01:00 p.m.	01:30 p.m.	02:00 p.m.	02:30 p.m.	03:00 p.m.	03:30 p.m.	04:00 p.m.	04:30 p.m.	05:00 p.m.
Duration	4 h								1 h		3 h
Reception	2 Receptionists								Break		2 Receptionists
Duration	9 h
Triage	1 Nurse
Duration	9 h
Endocrine	2 Doctors in 2 clinics

. Usually, the clinic runs for 8 h a day from 8 a.m. until 4 p.m. However, in some cases, doctors have to work an additional hour to serve the remaining patients who are still waiting.

4.2. Model Construction

The model was developed using Simio, a proprietary simulation software designed to represent and analyze complex systems like patient flow in healthcare settings. Simio enables the creation of detailed models that simulate real-world processes, allowing users to visualize and optimize operational flows. In this study, Simio was used to create a discrete event simulation model tailored specifically to represent the flow of patients through the endocrine clinic. This model captures the various stages of patient flow within the internal medicine department, from entry to departure (see Figure 2), with the goal of identifying bottlenecks and providing a comprehensive view of the model in both 2D and 3D. The 2D view illustrates the movement of patients through the department, while the 3D view captures the interactions and behaviors of both patients and staff. Additionally, a dashboard is included to monitor and track variations dynamically during the simulation.

The baseline model involves various objects representing patient arrivals, reception, triage, eight clinic rooms, waiting areas, and patients exiting the department. Endocrinology patients and other patients are distinguished by modeling them as separate entities for verification purposes.

The verification process was carried out to ensure that the simulation model functioned as intended. This was demonstrated through a series of checks, including the use of a dashboard to monitor the number of patients entering the endocrine clinics, ensuring they represented 30% of the total patients in the internal medicine department. Additionally, the model’s behavior was visually monitored using 3D animation, which depicted patient and staff movements within the department. An important verification step involved ensuring that no patients were left waiting in the area when any of the clinics were idle, confirming that the model operated efficiently and as expected. For validation, the baseline model’s results were compared against real-world data to confirm their accuracy. They were closely aligned with actual clinic operations, providing confidence in the model’s validity. Furthermore, the results were presented to the clinic staff, who confirmed that the model’s outcomes closely matched their real-world experience. This dual approach of verification and validation ensured that the model not only operated correctly but also produced realistic and reliable results.

These steps ensured the fidelity of the model’s design and confirmed its alignment with real-world clinic operations. By examining patient flow, resource utilization, and staffing, the research pinpointed operational inefficiencies and proposed strategic improvements. Notably, the simulation allowed the evaluation of various scenarios without actual implementation, saving both time and costs. This research underscores the efficacy of Simio modeling as a transformative tool in healthcare management, promising improved patient outcomes and optimized resource utilization in outpatient clinic settings.

To simulate the movement of entities in the system as they would in real life, the authors used sequences and lists to guide them to their destinations, as depicted in Figure 2. Each entity type was assigned a sequence, such that both endocrinology and other internal medicine entities had to pass through designated routes. Two waiting areas were established to accommodate the waiting patients. By using separate lists at the output nodes of each server, the authors were able to direct the two entity types to the appropriate clinic rooms based on availability.

5. Results

The simulation model was executed with precision, running through a comprehensive set of 300 replications, with each replication representing one full day of operations. This approach was designed to ensure robust and reliable results. This extensive replication process allowed for a thorough exploration of potential variations and uncertainties within the outpatient clinics. By conducting a substantial number of replications, the research aimed to capture a comprehensive understanding of the model’s performance, ensuring statistical significance and supporting the reliability of the insights gained.

The analysis of the baseline model shows that triage is the station where patients wait for the longest time, followed by reception (see Figure 3).

As can be seen from Figure 4, the utilization follows the same pattern, where triage was busy 93% of the time, followed by reception, which was busy 65% of the time. In addition, it seems that the endocrine clinics are underutilized.

Figure 5 shows the accumulation of waiting patients in both reception and triage. The number of waiting patients sharply increases during break time. In general, the delays seem to be a consequence of the shortage of workers and their work shifts.

Next, we will experiment with the simulation model to maximize the number of endocrine patients served and minimize the time patients spend in the system. This can be achieved by rescheduling the shifts of receptionists and increasing the number of triage nurses.

Based on the analysis of the baseline model and the identification of bottlenecks, different scenarios have been developed to address these issues and minimize their impact. Three scenarios have been developed as follows:

• The first scenario: Adding another nurse to triage.
• The second scenario: Rescheduling receptionists’ shifts so they have different break hours, as shown in Figure 6.
• The third scenario: Combines both scenarios 1 and 2. The number of nurses in triage is increased and the receptionists’ shifts are rescheduled.

All these scenarios were tested under two settings of patient volumes. The first setting is the current patient volume where 30% are endocrine patients and the remaining 70% are other internal medicine patients. The second setting tests the scenarios at an increased patient volume where endocrine patients are set to 50% of the total patients.

Table 3. Responses for different scenarios.

Scenario	Current Patient Arrival (30:70)		Increased Patient Arrival (50:50)
	Average Time in System (min)	Number of Patients Served	Average Time in System (min)	Number of Patients Served
Baseline	76.36 ± 1.59	36	83.16 ± 1.83	59
1st scenario	42.61 ± 0.92	41	69.62 ± 1.73	62
2nd scenario	68.08 ± 2.23	37	77.75 ± 2.45	60
3rd scenario	36.04 ± 0.71	42	61.78 ± 2.14	64

presents the simulation results for all scenarios in addition to the baseline model. A quick glance over the results reveals that the performance of the clinics can be significantly improved in terms of the number of patients served and the time spent in the system.

Figure 7 illustrates the average time in the system (TS) for patients across different scenarios assuming the current patient arrival rate. They are presented through boxplots with a 95% confidence interval (CI). The yellowish rectangles within each boxplot represent the upper and lower ends of the 95% percentile confidence intervals, indicating the range within which the majority of the data are expected to fall. The blueish rectangles denote the mean confidence interval ends, providing an estimate of where the true mean TS lies with 95% confidence. The black whiskers extend to show the range of the data, excluding outliers, while the central orange dot marks the mean of the data. This visual representation allows for a clear comparison of the average patient time in the system under different scenarios, with the confidence intervals offering insights into the precision and reliability of the simulation results. It shows that by increasing the capacity of triage by one (first scenario), the average time in the system decreases by around 44% due to the fact that the bottleneck is eased in the triage station.

Also, rescheduling the work plan for the staff in reception (second scenario) decreased the average time in the system to a lesser extent. This is because the patients have not accumulated while waiting for reception workers during the one-hour break as in the current situation.

By combining both changes in the third scenario, the average time in the system decreases to 36 min. This outperforms both former scenarios because it combines the benefits of increasing the triage capacity and decreasing the number of patients waiting in reception.

The total number of patients served showed a similar pattern of improvement, as shown in Figure 8. The number of patients served increased from 35.8 to 41.2 patients in the first scenario. A smaller increase was observed in the second scenario. The number of patients in the third scenario increased to 42, which is a slight improvement compared to the first scenario.

To test the capacity of the system at a higher volume, the patient arrival ratio of endocrine clinics to other clinics was changed from (30:70) to (50:50). The rate of improvement does not seem to be affected compared to the current arrival ratio as shown in Figure 9. However, the total time in system increased in all scenarios. This was mainly due to the patients waiting longer to be admitted to the endocrinology clinics.

On the other hand, significantly more patients can be served, as shown in Figure 10. The third scenario results in an increase of around 9% in total waiting time but with the potential of more than a 60% increase in total patients served compared to the current situation.

6. Discussion

The results of this study show that the implementation of a simulation model can significantly enhance the effectiveness and operation of the internal medicine department. The simulation model allowed for significant improvements in the average time in the system and the number of patients served, resulting in improved patient flow and reduced wait times. These findings are somewhat interested given the fact that many clinics still rely on observation processes to manage patient flow. The simulation model used in this study is an example of how it can be leveraged to enhance clinic operations and improve patient outcomes.

One interesting finding is that the simulation model was effective in identifying bottlenecks in the patient flow process. By simulating different patient scenarios, we were able to identify areas where delays occurred and recommend process improvements to reduce wait times and increase the number of patients served. Another important finding is that the simulation model led to a reduction in patient wait times to see a healthcare provider for both patient arrival sets in all scenarios.

An implication of this finding is the possibility that outpatient clinics could improve their performance by adopting simulation modeling as a tool for operational planning. By simulating different scenarios and testing various operational changes, clinics could identify the most effective strategies for improving patient flow and reducing waiting times. Therefore, healthcare organizations should consider investing in technology to improve their efficiency and effectiveness in delivering high-quality patient care.

7. Conclusions

In conclusion, the simulation has shown promising results for supporting decision-making and enhancing the effectiveness of operations in the endocrine outpatient clinic. By analyzing various scenarios and parameters, potential bottlenecks and inefficiencies in the clinic’s processes were identified. Furthermore, improvements were suggested and evaluated that could significantly reduce patient waiting times and increase the number of patients served.

One of the key benefits of using a simulation model is its ability to test different scenarios and strategies without the need for real-world implementation, which can be costly and time-consuming. The model can also be easily modified and adapted to accommodate changes in patient demand, staffing levels, or other factors that may impact clinic operations. Overall, the use of simulation models in healthcare management has the potential to revolutionize the way clinics and hospitals operate, leading to improved patient outcomes and more efficient use of resources.

The study’s findings not only highlight the effectiveness of the simulation model in improving patient flow within the endocrine clinic but also align with the findings of previous studies. They have demonstrated that simulation can significantly reduce wait times and improve operational efficiency in various healthcare settings, including outpatient clinics, emergency departments, and surgical units. By integrating these insights, this research reinforces the notion that simulation serves as a valuable tool for improving the patient experience across diverse clinical environments.

This study has several limitations. The data and simulation model were specific to a single endocrine clinic, which may limit the generalizability of the findings. The model’s assumptions, such as patient flow and service times, might oversimplify real-world com-plexities. Although the model was verified and validated, it was not fully tested in a live clinical setting.Further study is needed to explore the potential applications of simulation models in other clinical settings and patient populations. Additionally, further investigations are needed to evaluate the impact of other variables on patient outcomes and satisfaction. Finally, future studies could explore the feasibility and cost-effectiveness of implementing simulation modeling.