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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.4 pp.758-773
DOI : https://doi.org/10.7232/iems.2020.19.4.758

Radiotherapy Service Improvement: Simulation Study

Chawis Boonmee, Auttharat Kosayanon, Imjai Chitapanarux, Chompoonoot Kasemset*
Center of Healthcare Engineering System, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University Chiang Mai 50200, Thailand
Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University Chiang Mai 50200, Thailand
Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University Chiang Mai 50200, Thailand
Center of Healthcare Engineering System, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University Chiang Mai 50200, Thailand
*Corresponding Author, E-mail: chompoonoot.kasemset@cmu.ac.th
February 5, 2020 July 1, 2020 December 15, 2020

ABSTRACT


Radiotherapy service has seen a spur of interest in the health care system. Many cancer centers aim to develop and enhance radiotherapy service to support the increasing demand. Because Thailand is a developing country, some cancer centers still have limited resources, both staff and machinery, while the number of cancer patients in Thailand has increased every year. To enhance the operational efficiency of radiotherapy service, this research aims to propose an integrated simulation approach and theory of constraints (TOC) approach for increasing radiotherapy service capability in Thailand. As simulation and TOC were applied in the case study, the real bottlenecks were identified in each treatment room. Considering four treatment rooms, only two rooms were selected for improvement (Room 2 and Room 3) after the simulation. The eight proposed solutions concentrated on improving both human- and machine-related bottlenecks. The simulation experiments were conducted to evaluate each solution. The results presented the best solution as adding one technician to Room 3 and replacing the radiation machine in Room 2 with the same machine as in Room 3. With this solution, the number of patients served was increased by 12.32% from the current system. As for the investment needed, the benefit-cost ratio and payback period were calculated as 1.89 and 2.80 years, respectively, for this solution.



초록


    1. INTRODUCTION

    Healthcare management or healthcare administration is the management or administration of healthcare systems, public health systems, hospitals, whole hospital networks, and other medical facilities. Healthcare management aims to ensure that individual sections run smoothly and efficiently, qualified employees are hired, information is disseminated efficiently throughout the organization or network, specific outcomes are reached, and resources are used efficiently—among many other responsibilities. Recently, healthcare management has experienced enormous demand since it can assist in managing a hospital and related medical facilities. The scope of the healthcare management system is steadily increasing worldwide. The solutions are not only related to management in the healthcare system but also related to healthcare services, health policies and enhancement in the demand for world-class healthcare facilities in healthcare management as well. Nowadays, healthcare services still suffer from inefficient operation. Many hospitals and other medical facilities urgently need to improve their processes and services. However, redesign projects in healthcare service systems have often been unsuccessful (Boonmee and Kasemset, 2019).

    Since 2003, the number of cancer patients in Thailand has been continuously increasing, with the number of dead having increased in proportion (about 60,000 people per annum on average since 2003). Most of the males (16.2%) have died from rectum and colon cancer while most of the females (37.5%) have died from breast cancer (Social and Quality of life database system, 2018). Radiotherapy is one of the major methods of cancer treatment; it utilizes curative therapy and palliative therapy. Curative therapy is treatment that aims to completely cure patients; palliative therapy is treatment for relieving the symptoms and reducing the suffering caused by cancer. As the number of cancer patients has increased, the demand for radiotherapy has continually increased in kind. Therefore, the operational efficiency of radiotherapy service is an important issue at present.

    Currently, Thai cancer centers are endeavoring to support the enhancement of radiotherapy service capability. However, redesign projects in radiotherapy service systems have often been unsuccessful due to complicated systems, misidentification of the real problems, and the limitation of resources—both of staff and machinery. Hence, this research aims to propose solutions for radiotherapy service improvement at the Thai cancer center case study using simulation-based procedures and the concept of Theory of Constraints (TOC). There are three contributions of this research. Firstly, this research presents the application of simulation-based procedures for TOC implementation in healthcare system improvement. Secondly, this research considers the complicated healthcare system including multiple patient types, multiple machines with different capabilities, and multiple other resources. The third contribution is to present several solutions based on multicharacteristic parameters and evaluate solutions under uncertain situations using simulations.

    The remainder of this paper is organized as follows: Section 2 presents related literature review in the health care system. Section 3 presents the research methodology of this research. Section 4 presents a case study in the Thai cancer center. Section 5 presents the results of research, including data collection, simulation models, bottleneck identification, implemented design, and improvement plans evaluation. Finally, a conclusion and discussion are given in Section 6.

    2. LITERATURE REVIEW

    This section presents an overview of the relevant literature. Healthcare management has become an interesting topic due to the increased demand for service. Many researchers aim to develop and enhance all systems in the health care supply chain in order to support the increasing demand in the future by employing a variety of methods for improvement in the health care system. The simulation approach is one popular technique that can be applied in this field (Boonmee and Kasemset, 2019;Monks et al., 2015). The simulation approach can deal with more complicated systems and requires fewer simplification assumptions. Moreover, it is able to be used to study non-existing systems, conduct experiments that are expensive to perform in reality, and predict complicated outputs of actions and developments (Lagergren, 1998). Aboueljinane et al. (2014) proposed a discrete event simulation model for analyzing the performance of SAMU (the acronym of Urgent Medical Aid Services in French) regarding 94 processes. The model determined the possible changes in the SAMU which led to enhanced operational efficiency for coverage performance in the simulation. Five strategies of scenarios were proposed for the improvement related to the location of rescue teams and the level of resources used throughout the service area. The results suggested that repositioning a portion of the existing teams into potential bases increased the 20- minute coverage performance up to 4.5% on average. Furthermore, this improvement in coverage reached 7.3% when the whole fleet was relocated based on the multiperiod redeployment plan obtained from simulation optimization. Lu et al. (2014) proposed a genuine agentbased adoptive scheduling model for service that aims to decrease outpatient waiting time in the Hand and Foot Team Clinic of the orthopedic surgery department at a hospital in Taiwan. The results showed improvement: The agent-based collaborative control system was able to decrease waiting time between 29% and 36% for walk-in patients as well as 61% to 63% for scheduled patients. Similarly, Kaushal et al. (2015) presented an agent-based simulation model in order to evaluate fast-track treatment in the hospital emergency department. The objective of this research aimed to reduce patient waiting time and to study the behavior change of entities and resources in a complex emergency department system including static and dynamic fast-track treatment processes. Implementation of this research reduced the patient waiting time by 50%. Bhattacharjee and Ray (2016) proposed a simulation and analysis of appointment systems for scheduling patients. This paper applied the simulation approach in order to control and synchronize the arrival of patients with resource availability, thereby reducing the patient waiting time and increasing the utilization of resources. The paper determined the appointment system with multiple classes of patients where different classes of patients might vary in punctuality, no-show probabilities, mean service times and service time variability. Other related papers that represented the simulation approach in the health care system were Abuhay et al. (2016), Ahmed and Amagoh (2008), Andreev et al. (2013), Brenner et al. (2010), Jones and Evans (2008), Lee et al. (2010), Ngowtanasuwan and Ruengtam (2013), and Monks et al. (2012).

    In order to provide more efficiency in the implementation of health care service, several papers integrated the simulation model with other techniques, such as plant layout Kritchanchai and Hoeur (2018), optimization (Cabrera et al., 2012), six-sigma (Venkatadri et al. 2011), data envelopment analysis (Weng et al., 2011), what-if analysis Wong et al. (2011), pull system concept (Tiwari and Sandberg, 2016), and Theory of Constraints (TOC) (Somayeh, 2009).

    Theory of Constraints (TOC) also known as constraint( s) management, is a management theory which emphasizes the importance of enhancing system performance by including a smarter utilization of existing resources, especially by considering the bottleneck, before increasing the system’s capacity. The principal of TOC focuses on the weakest point of any system (i.e., “constraint” or “bottleneck”) and emphasizes the improvement of that constraint’s performance—directly resulting in improving the overall performance of the system (Somayeh, 2019). The method of TOC comprises five steps: 1) identifying the bottleneck process, 2) bottleneck exploitation, 3) bottleneck subordination, 4) system evaluation, and 5) looping back to the first step to find any additional bottleneck in the system (Kasemset and Kachitvichyanukul, 2007;Kasemset, 2011). To enhance healthcare service, some papers applied the simulation approach and TOC in healthcare system improvement. Tiwari and Sandberg (2016) proposed perioperative bed capacity planning by using the TOC and simulation approach with the logic of the simulation model being guided by the TOC concept. The paper focused on space planning and bed capacity decisions under the various stages of the perioperative system: preoperative, intraoperative, and postoperative capacity. Then, Grida and Zeid (2019) proposed a system-dynamics-based model to implement the TOC in a healthcare system. The system dynamic model simulated a typical medium-sized hospital where different types of patients are served using the same limited resources based on the TOC philosophy. The results demonstrated that the number of served patients increased by 6% without any resource elevation.

    Few papers presented application of the simulation approach and the TOC approach in the radiotherapy sector. Mohammadi and Eneyo (2012) presented an application of TOC’s drum-buffer-rope approach in scheduling the radiotherapy system of a hospital. This research aimed to reduce the waiting time and waiting lists for radiotherapy treatments. The same paper was proposed by Somayeh (2009), who proposed the adoption of a scheduling policy based on TOC’s drum-buffer-rope tool for an outpatient cancer facility. All above-mentioned papers are concluded in Table 1. From the literature reviews, when resource planning was considered, TOC is one concept that help in this kind of problem.

    Based on the above-mention review, few papers have focused on the radiotherapy service system, so there is a lack of knowledge in this area includes multiple patient types, multiple machines, multiple rooms, and multiple radiation therapy techniques. Hence, this paper aims to propose an application of the simulation approach and the TOC approach for enhancement of the radiotherapy service system. The paper determines not only multiple patient types, multiple machines, and multiple rooms but also multiple radiation therapy techniques in the system. Furthermore, this paper aims to propose several solutions that could be effective even with the limited resources in the case study in order to enhance the radiotherapy service system, in which economic analysis is applied to evaluate all solution candidates as well.

    3. METHODOLOGY

    The methodology of this research consisted of six steps as shown in Figure 1. Detailed descriptions of each process are presented as follows:

    3.1 Data Collection

    The existing process of radiation therapy service of the case study was studied and the case study’s data were collected including the proportion of patients, treatment site type, radiotherapy technique, and processing time in each activity.

    3.2 Simulation Model Formulation

    This step was to formulate the simulation model. For data analysis, the number of patients and the processing time of each operation were fitted to appropriate statistical distribution using the Input Analyzer from ARENA software. Then the simulation model was formulated to present the existing process of radiation therapy service via ARENA software. Finally, model verification and validation were conducted to test whether the model was well-structured and a correct representative or not. To calculate the number of replications in simulation experiments, Equation (1) was conducted in this research (Kelton et al., 2009):

    n n 0 h 0 2 h 2
    (1)

    where

    • n = the number of replications,

    • n0 = the number of initial replications (10 to 15 replications are usually considered),

    • h0 = the half-width obtained from the initial set of replications, and

    • h = the desired half-width from decision makers.

    3.3 Bottleneck Identification

    The simulation model of the existing process was run to collect statistical data. The results from the simulation model were used to identify the system bottleneck (constraints) using TOC technique based on the proposed method of Kasemset and Kachitvichyanukul (2007).

    3.4 Proposing Improvements

    After the system bottlenecks were identified, improvement plans were proposed. During this step, the concept of TOC was considered to improve the system until the system bottlenecks were removed.

    3.5 Evaluation Improvement Plans

    This step was to evaluate each improvement plan using the simulation technique. The results of each solution were compared with the current system. All comparisons were conducted based on statistical comparison using 95% CI (significance level [α] = 0.05) and economic perspectives including payback period and benefit-cost ratio. The payback period and benefit-cost ratios were calculated as Equation (2) and Equation (3) (Blank and Tarquin, 2012):

    0 = P + N C F ( P / A , i , n p )
    (2)

    B e n e f i t   c o s t   r a t i o   ( B / C ) = B e n e f i t D i s b e n e f i t C o s t s
    (3)

    where

    • i = interest rate,

    • P = investment cost,

    • NCF = average net cash flow (per year), and

    • np = payback period.

    3.6 Discussion and Conclusion

    Finally, the discussion and conclusion were conducted at the end of this research.

    4. CASE STUDY

    This section presents the case study in which we applied our approach: a Thai cancer center. This cancer center is located in the northern region of Thailand. Supertertiary medical care is provided for residential patients from 8 provinces. The center provides 4 techniques for radiotherapy services: Conventional Radiotherapy (2D), Three-Dimensional Conformal Radiotherapy (3D-CRT), Intensity Modulated Radiation Therapy (IMRT), and Image Guided Radiation Therapy (IGRT). More details of the radiotherapy techniques can be found in studies by American Cancer Society (2019) and Rehman et al. (2018). Currently, there are four treatment rooms in which different radiation machines are used for different treatment techniques. The details of techniques in each room are presented in Table 2.

    The general process of radiotherapy service is presented in Figure 2. When patients arrive at the division, they are classified as two categories: (1) ambulant or (2) non-ambulant patients. Ambulant patients are able to move to other operations without assistance from the division’s staff, while non-ambulant patients need assistance. The proportions of ambulant and non-ambulant patients are presented in Table 3. The preparation step is started when patients arrive to radiotherapy rooms. Then, their postures are adjusted in the immobilization device based on treatment locations. For the preparation, there are two technicians working at each room to support patients. There are five treatment sites: the central nervous system (CNS); head and neck (H&N); thoracic; abdomen; and pelvis. The proportions of patients for each treatment site are presented in Table 4. The different treatment sites and types of patients are affected by different operations and movement times. Before a patient is treated with each technique (the proportion of patients with each technique can be found in Table 5), simulation and verification should be carried out to confirm the treatment plan. After the radiotherapy is finished, patients are discharged and move out, and the room is cleared to serve the next patient.

    5. RESULTS

    5.1 Current System

    Following the exiting process of radiation therapy addressed in Section 4, data including the arrival pattern of patients, operation time of each process, and transfer time were randomly collected every working day during the official working hours (50 values for each data item).

    5.2 Building the Simulation Model for the Existing System

    There were three main tasks presented as follows.

    5.2.1 Data Analysis

    Collected data were fitted to an appropriate basic statistical distribution using the Input Analyzer of Arena software. When the number of each item was 50 values, the chi-square test was employed with a significance level (α) of 0.50. The results of the data fitting are presented in Table 6 - Table 10.

    5.2.2 Model Building

    The simulation model of the existing system was developed via Arena software (as presented in Figure 3). From Figure 3, the model consisted of 4 parts represented in each treatment room with similar structures. Starting from creating entities to represent patients, entities were designated as ambulant or non-ambulant patients based on the proportions presented in Table 3. Then entities were sent along the flow modules based on their different assigned treatment sites and radiation techniques. The operation time of each type is different. At the end, entities were counted separately for each room before they were disposed of.

    5.2.3 Model Verification and Validation

    After the simulation model was developed, the model was verified and validated to confirm the appropriateness of the model. The flows of the entities were compared with the flow process of the existing system by the model developer. Test runs were conducted initially using 10 replications. The outputs from the simulation tests were investigated and used to find the appropriate number of replications for the simulation experiments. Initial results of 10 replications presented the average system output as 117.20 persons per day with the initial half-width (95% confidence interval, CI) as 6.66 or 5.68% error in comparison with the point estimator (average value). To calculate the appropriate number of replications as addressed as Equation (1), the desired half-width smaller than the initial half-width was provided from the decision-maker’s preference as 3 or 2.56% error from the point estimator. Calculations following Equation (1) for 49.28 replications (or approximately 50 replications) were set to perform the validation test. Therefore, the parameter settings for the simulation experiments were determined to be 1 day (8 hours) for run length and 50 replications for each scenario. Then the simulation test was conducted again to verify the model.

    A comparison of the results is presented in Table 11. The total output and the output of each room are presented and were compared with the real data from the existing system using a 95% confidence interval on mean (average) of patients served per day. When, the two real numbers are (lower bound, upper bound) of 95% confidence interval on mean calculated based on equation (4) (Montgomery and Runger, 2014).

    x ¯ t α 2 , n 1 S n μ x ¯ + t α 2 , n 1 S n
    (4)

    when α = 0.05 and n = 50, the lower bound of mean is x ¯ t α 2 , n 1 S n and the upper bound of mean is x ¯ + t α 2 , n 1 S n .

    The results presented no significant difference between the output ranges of the two systems, when the intervals of both systems overlap, so we concluded that the developed simulation model can be used to represent the real system.

    5.3 Bottleneck Identification

    To improve the system, bottleneck identification was firstly carried out following the 5 steps of TOC implementation. The simulation-based procedure proposed by Kasemset and Kachitvichyanukul (2007) was employed to identify the system bottlenecks of the existing system. The results from the simulation test previously conducted in the validation step were analyzed. Operation time of 5 processes referred to Figure 2 are presented in Table 12.

    There are 2 resources used during the process of radiotherapy: man (technicians) and machine (radiation machines). There are 3 operations mainly worked by technicians while 2 operations are mainly conducted by a radiation machine. However, all operations occupy both man’s and machine’s time for each treatment.

    To identify the system bottleneck (Kasemset and Kachitvichyanukul, 2007), the first task was to select the bottleneck candidates. Then all candidates were tested until the real system bottleneck could be identified using the simulation-based method. Considering Table 12, Figure 4 is presented to classify the operation time mainly used by man and machines.

    From Figure 4, the bottleneck candidates can be selected based on longer operation time occupied by either man or machine. Bottleneck candidates were listed as follows: (i) for Room 1 was man and machine, (ii) for Room 2 was machine, and (iii) for Room 3 and Room 4 were man.

    To identify the real system bottleneck, simulation tests were conducted by increasing the capacity of each bottleneck candidate and observing whether the significant improvement existed or not (Kasemset and Kachitvichyanukul, 2007). The simulation experiments were designed based on the bottleneck candidates as follows: (i) man: increasing the number of technicians; and (ii) machine: reducing 50% of the processing time at the treatment operation. The results from simulation tests are presented in Table 13.

    The results presented in Table 13 were used in identifying the system bottleneck for each room when the number of patients treated per day was significantly increased and different compared to the existing results. Either adding a technician or decreasing treatment time is designed for increasing the resource’s capacity to identify the bottleneck when output can be improved. The intervals of outputs of three situations with (*) did not overlap. Finally, the conclusions of this step were as follows:

    • - The real bottleneck was the radiation machine for Rooms 1, 2, and 4; and

    • - The real bottleneck was technicians for Room 3.

    5.4 Improvement Plans

    Following the 5 steps of TOC implementation, after bottlenecks were identified, exploitation was considered. The key point of the bottleneck exploitation is to maximize the bottleneck’s utilization. Figure 5 presented the utilization of each treatment room.

    From Figure 5, the utilization of each room is quite high (approximately 90% or over) excluding Room 3, which is the lowest at 83.68%. Interviews of the center’s experts were conducted; these experts reported that the number of appointed patients per day is fixed and cannot be increased because the variation of treatment time for this service is quite large. The policy is to make the number of appointments that can be completely serviced within the office hours. Thus, the cancer center sets a buffer time that cannot be reduced to maximize the utilization of each room to be close to 100%. Then the improvements were planned based on the bottleneck subordination and system elevation concepts from TOC. The concept of subordination is to support the bottleneck to work continuously, while the concept of elevation is to enhance the system performance by adding new resources.

    From Table 13, there were three rooms that machines were identified as bottlenecks, whereas only the room no. 3 that the bottleneck was identified as man. Therefore, the improvement for the room no. 3 was set as adding one technician as scenario 1.

    For the treatment rooms at which machines were identified as bottlenecks, the process time of treatment step was considered. From Table 12, considering total processing time of all treatment rooms, the room no. 2 has the longest total processing time following by the room no. 1, 4, and 3, respectively. Also, the treatment time of the room no. 2 was the longest among other rooms. Then the department’s experts (doctors and staffs) recommended to improve this treatment room by replacing the current machine (using for a long time and outdated technology) by new machine models that can work faster with updated technology. Thus scenario 2 was proposed for the improvement of the room no. 2.

    For the room no. 1 and 4 having machines as bottlenecks, they were not considered any improvement because (i) the machine at the room no. 1 is the newest with highest technology reserved for complicated cases that resulted in long treatment time and (ii) the number of output patients of the room no. 4 is the highest comparing with other rooms as shown in Table 11. Thus, solutions were proposed only for the room no. 2 and 3 as follows:

    • - Scenario 1: Adding one technician to Room 3; and

    • - Scenario 2: Replacing the radiation machine in Room 2.

    For scenario 2, to replace the radiation machine in Room 2, there were 3 sub-scenarios mentioned as follows:

    • - Scenario 2.1: Replacing with the same radiation machine as in Room1;

    • - Scenario 2.2: Replacing with the same radiation machine as in Room 3; and

    • - Scenario 2.3: Replacing with the same radiation machine as in Room 4.

    As previously noted in Table 2, the radiation machine in Room 3 can serve the same treatment techniques as those served in Room 2. Although the radiation machine in Room 1 is used for IMRT and IGRT techniques for complicated cases, this machine can be used for all techniques covering the same techniques as in Room 3. Consequently, the simulation tests for scenario 2.1 and 2.3 could be conducted without changing the proportion of patients for each technique in these scenarios. Conversely, when the radiation machine in Room 4 was considered to replace the one in Room 3, the proportion of patients for each technique had to change among all treatment rooms because this machine can serve only 2D and 3D techniques. Thus, the patients assigned to IMRT and IGRT in Room 3 were moved to other rooms as they would be affected by the proportion of patients assigned to each room and each technique. Consequently, for scenario 2.3, there were three sub-cases proposed (Table 14).

    The scenarios presented in Table 14 were formulated by balancing the proportion of each technique when changing the combination of techniques in each room.

    Finally, seven simulation tests were conducted follows:

    • - Scenario 0: Current system;

    • - Scenario 1: Adding a technician to Room 3;

    • - Scenario 2.1: Replacing with the same radiation machine as in Room 1;

    • - Scenario 2.2: Replacing with the same radiation machine as in Room 3;

    • - Scenario 2.3.1: Replacing with the same radiation machine as in Room 4 and switching 2D-patients in Room 3 with IMRT-patients in Room 2;

    • - Scenario 2.3.2: Replacing with the same radiation machine as in Room 4, switching 3D-patients from Room 3 with IMRT-patients from Room 2, and moving IGRT-patients from Room 3 to Room 1; and

    • - Scenario 2.3.3: Replacing with the same radiation machine as in Room 4, assigning 2D-patients to Room 2, 3D-patients to Room 4, and IMRTpatients to Room 3.

    5.5 Evaluation of the Improvements

    All scenarios mentioned previously were evaluated by simulation tests. All results—the average outputs per day—were compared with the existing system and are shown in Table 15.

    As presented in Table 15 and Figure 6, there were three scenarios from the first tests (Scenarios 1, 2.1, and 2.2) in which the average numbers of patients served of the overall system was significantly improved (without overlapping with scenario 0 (current)). Then, Scenarios 3 and 4 were proposed from the combination of Scenarios 1 and 2.1 and the combination 1 and 2.2, respectively. The results from Scenarios 3 and 4 also presented non-overlap interval comparing with Scenario 0, so the system outputs of both scenarios showed significant improvement comparing with the current situation, as well.

    6. DISCUSSION

    The results from the simulation tests can be addressed as follows:

    • - Adding one additional technician to Room 3 can help to increase the number of patients served for the system.

    • - For replacement of the radiation machine in Room 2, only the machine that works similarly to the one in Room 1 and 3 can help to increase the number of patients served for the system; machines that work similarly to one in Room 4 should not be considered.

    • - The combination between adding one technician to Room 3 and replacing the machinein Room 2 with a machine like the one in Room 1 or 3 can also help to increase the number of patients served for the system.

    All scenarios excluding 2.3.1 to 2.3.3 can significantly increase the number of patients served for the system in comparison with the existing system. However, some discussion can be drawn as follows:

    • (i) Scenarios 1, 2.1, 2.2, and 3 were not significantly different. Thus, any one of them can be applied with similar results.

    • (ii) Scenarios 1 and 2.1 were dominated by Scenario 4, so both solutions in scenario 1 and 2.1 can be eliminated.

    • (iii) From (ii), alternatives for the system improvement were reduced to Scenarios 2.2, 3 and 4. These three scenarios can help in improving the system output similarly when considering only simulation results.

    As more than one of the solutions are acceptable, each scenario was also evaluated based on economic perspectives. This research conducted economic assessment using payback period and benefit-cost ratio following Equations (2) and (3). The calculation for Scenarios 1, 2.1, 2.2, 3 and 4 are presented in Table 16.

    As in the discussion point (iii), Scenarios 2.2, 3 and 4 can increase the system output similarly. In addition, these three solutions need investment. Considering payback period and benefit-cost ratio, Scenario 4 should be prefered with a maximum benefit-cost ratio of 1.89 and minimum payback period of 2.80 years.

    Scenario 1 should be recommended if the cancer center cannot accept the investment; also, this solution is the easiest for real implementation.

    7. CONCLUSION AND RECOMMENDATION

    This research presented a simulation study of the radiotherapy service of a cancer center case study. The current system was studied and the simulation model was developed to represent the system. Based on TOC, bottleneck identification was carried out using simulation as a tool. Resource capacity constraints (or bottlenecks) of the system were identified and evaluated. Then solutions were provided for two out of four treatment rooms as follows: add an additional technician to Room 3, and replace the radiation machine in Room 2. Based on the simulation experiments, the number of patients served by the system can be increased when adding one technician to Room 3. Moreover, this solution can be practically implemented with no investment.

    To replace the machine in Room 2, three different machines can be considered. Based on the simulation tests, either machine as in Rooms 1 or 3 can help to increase the number of patients served from the system. Furthermore, for the combination scenarios, adding one technician to Room 3 together with replacing the machine in Room 2 with machines as in either Room 1 or 3 can also help to improve in the number of patients served.

    The recommendation when considering economic assessments is to add one technician to Room 3 and replace the radiation machine in Room 2 with the model as used in Room 3 because the benefit-cost ratio is the highest and payback period is minimum of all proposed solutions.

    This research presents the application of simulation and TOC concepts in improving the healthcare service of the case study. Radiotherapy service has multiplicity in types of patients and symptoms, treatment techniques and detail in each operation. Simulation is a useful tool for representing the system and providing information leading to system improvement, and TOC is a concept that concentrates on improving the system bottleneck to improve the overall system’s output. Simulation-based procedure to identify bottleneck following TOC concept help in reducing the number of simulation tests when only the bottleneck candidate(s) are considered in simulation tests (Kasemset and Kachitvichyanukul, 2010). Implementing this procedure can help in identifying the bottleneck, as well as providing and evaluating the solutions. Finally, practical solutions can be proposed to decision-makers for further real implementation.

    Although, this paper is mainly presented the application of simulation in healthcare real case study, the new general academic insight is to apply the concept of TOC in healthcare in systematic procedure. As we know TOC is widely applied in manufacturing systems for identifying system’s bottlenecks and improving system’s throughput, but we found few research works applied this concept in healthcare research. Among few researches, TOC was mentioned as in idea of bottleneck identification and improvement without systematic approach (Grida and Zeid, 2019;Somayeh, 2009), whereas, TOC with systematic approach based on simulation was presented in this research with the real case of healthcare system. When TOC was applied to improve any system, the step of bottleneck identification is to consider basic performance measurements (i.e. resource utilization, processing time, number in queue), whereas, this paper applied simulation experiments to identify real system bottleneck. The advantage of this procedure is to identify the system bottleneck correctly at the beginning, so the solution can be proposed at the most effectiveness. Moreover, simulation experiment for bottleneck identification is useful when key system parameters are high variability as in healthcare system.

    Further study is recommended for bottleneck exploitation. Currently, there are fixed numbers of appointed patients per day that cannot be changed. The recommendation is to analyze the current number of appointments per day. To analyze this issue, simulation and scheduling techniques can be employed. In addition, a stochastics model should be considered due to the variation of the problem as mentioned previously.

    ACKNOWLEDGMENTS

    This research was supported by the Murata Science Foundation under the Memorandum of Understanding between Chiang Mai University and The Murata Science Foundation.

    Figure

    IEMS-19-4-758_F1.gif

    The methodology of the research.

    IEMS-19-4-758_F2.gif

    Process of radiotherapy service in each room.

    IEMS-19-4-758_F3.gif

    The simulation model of the Division of Radiation Oncology at the Thai cancer center case study.

    IEMS-19-4-758_F4.gif

    Operation times classified based on main resources (man or machine).

    IEMS-19-4-758_F5.gif

    The average utilization of each treatment room (man and machine are the same).

    IEMS-19-4-758_F6.gif

    95% CI of the average number of patients served per day (lower bound, upper bound).

    Table

    List of studies in the literature review

    Techniques of each treatment room at the case study division.

    The proportion of ambulant and non-ambulant patients of each room

    The proportion of treatment sites of each room.

    The proportion of radiotherapy service to patients with respect to technique

    The number of patients (persons/day)

    Operation time of the verification (minutes)

    Operation time of preparation for each treatment site (minutes)

    Transferring time of each patient type (minutes)

    Operation time for radiotherapy in each room with different techniques (minutes)

    Results comparison between real system and simulation

    Average time of each process in each radiotherapy room

    Comparison of results for the bottleneck identification using 95% CI of the average number of patients per day presented as the interval as (lower bound, upper bound)

    The new service schedule of each scenario for adjusting the proportion of patients

    Results comparison for additional tests using 95% CI of the average number of patients served per day presented as the interval as (lower bound, upper bound).

    Economic assessment results

    REFERENCES

    1. Aboueljinane, L. , Sahin, E. , Jemai, Z. , and Marty, J. (2014), A simulation study to improve the performance of an emergency medical service: Application to the French Val-de-Marne department, Simulation Modelling Practice and Theory, 47, 46-59.
    2. Abuhay, T. M. , Krikunov, A. V. , Bolgova, E. V. , Ratova, L. G. , and Kovalchuk, S. V. (2016), Simulation of patient flow and load of departments in a specialized medical center, Procedia Computer Science, 101, 143-151.
    3. Ahmed, S. and Amagoh, F. (2008), Modeling hospital resources with process oriented simulation, Central Asia Business, 1(1), 5-20.
    4. American Cancer Society (2019), Getting external beam radiation therapy, cited 2019 Feb. 15, Available from: https://www.cancer.org/treatment/treatments-and-side-effects/treatment-types/radiation/external-beam-radiation-therapy.html.
    5. Andreev, V. P. , Head, T. , Johnson, N. , Deo, S. K. , Daunert, S. , and Goldschmidt-Clermont, P. J. (2013), Discrete event simulation model of sudden cardiac death predicts high impact of preventive interventions, Scientific Reports, 3, 1771.
    6. Basole, R. C. , Bodner, D. A. , and Rouse, W. B. (2013), Healthcare management through organizational simulation, Decision Support Systems, 55(2), 552-563.
    7. Bhattacharjee, P. and Ray, P. K. (2016), Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: A case study, Operations Research for Health Care, 8, 71-84.
    8. Blank, L. T. and Tarquin, A. J. (2012), Engineering Economy (7th ed.), Mc GrawHill, PA.
    9. Boonmee, C. and Kasemset, C. (2019), The improvement of healthcare management in Thailand via IE tools: A survey, Proceeding of the 2019 International Conference on Industrial Engineering and Operations Management, Bangkok, 264-274.
    10. Brenner, S. , Zeng, Z. , Liu, Y. , Wang, J. , Li, J. , and Howard, P. K. (2010), Modeling and analysis of the emergency department at University of Kentucky chandler hospital using simulations, Journal of Emergency Nursing, 36(4), 303-310.
    11. Cabrera, E. , Luque, E. , Taboada, M. , Epelde, F. , and Iglesias, M. L. (2012), Optimization of emergency departments by agent-based modeling and simulation, Proceedings of the Proceedings of the 13th International Conference on Information Reuse & Integration (IRI), 423-430.
    12. Charfeddine, M. and Montreuil, B. (2010), Integrated agent-oriented modeling and simulation of population and healthcare delivery network: Application to COPD chronic disease in a Canadian region, Proceedings of the 2010 Winter Simulation Winter Conference, IEEE, 2327-2339
    13. Grida, M. and Zeid, M. (2019), A system dynamics-based model to implement the Theory of Constraints in a healthcare system, SIMULATION, 95(7), 593-605.
    14. Jones, S. S. and Evans, R. S. (2008), An agent based simulation tool for scheduling emergency department physicians, AMIA Annual Symposium Proceedings, American Medical Informatics Association, 338-342.
    15. Kasemset, C. (2011), A review on quality improvement and Theory of Constraints (TOC), Proceedings of the 2011 IEEE International Conference, Bangkok, Thailand, 327-330.
    16. Kasemset, C. and Kachitvichyanukul, V. (2007), Simulation-based procedure for bottleneck identification, Asian Simulation Conference, Springer, Berlin, Heidelberg, 46-55.
    17. Kasemset, C. and Kachitvichyanukul, V. (2010), Effect of Confidence Interval on Bottleneck Identification via Simulation, Proceedings of the 2010 IEEE International Conference on Industrial Engineering and Engineering Management, Macao, China, 1592-1595.
    18. Kaushal, A. , Zhao, Y. , Peng, Q. , Strome, T. , Weldon, E. , Zhang, M. , and Chochinov, A. (2015), Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department, Socio-Economic Planning Sciences, 50, 18-31.
    19. Kelton, W. D. , Sadowski, R. P. , Swets, N. B. (2009), Simulation with Arena (5th ed.), McGraw-Hill, New York.
    20. Kritchanchai, D. and Hoeur, S. (2018), Simulation modeling for facility allocation of outpatient department, International Journal of Healthcare Management, 11(3), 193-201.
    21. Lagergren, M. (1998), What is the role and contribution of models to management and research in the health services? A view from Europe, European Journal of Operational Research, 105(2), 257-266.
    22. Lee, B. Y. , Brown, S. T. , Korch, G. W. , Cooley, P. C. , Zimmerman, R. K. , Wheaton, W. D. , Zimmer, S. M. , Grefenstette, J. J. , Bailey, R. R. , Assi, T. , and Burkea, D. S. (2010), A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic, Vaccine, 28(31), 4875-4879.
    23. Lu, T. P. , Tsai, P. F. , and Chu, Y. C. (2014), An agent-based collaborative model for orthopedic outpatient scheduling, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Hsinchu, Taiwan, 621-626.
    24. Mohammadi, A. and Eneyo, E. S. (2012), Application of drum-buffer-rope methodology in scheduling of healthcare system, Architectural Engineering - Faculty Publications, 92, 1-18.
    25. Monks, T. , Pearson, M. , Pitt, M. , Stein, K. , and James, M. A. (2015), Evaluating the impact of a simulation study in emergency stroke care, Operations Research for Health Care, 6, 40-49.
    26. Monks, T. , Pitt, M. , Stein, K. , and James, M. (2012), Maximizing the population benefit from thrombolysis in acute ischemic stroke: A modeling study of in-hospital delays, Stroke, 43(10), 2706-2711.
    27. Montgomery, D. C. and Runger, G. C. (2014), Applied Statistics and Probability for Engineers (6th ed.), Wiley, Singapore.
    28. Ngowtanasuwan, G. and Ruengtam, P. (2013), Applied simulation model for design of improving medical record area in out-patient department (OPD) of a governmental Hospital, Procedia-Social and Behavioral Sciences, 101, 147-158.
    29. Rehman, J. , Zahra, Ahmad, N. , Khalid, M. , Noor ul Huda Khan Asghar, H. M. , Gilani, Z. A. , Ullah, I. , Nasar, G. , Akhtar, M. M. , and Usmanie, M. N. (2018), Intensity modulated radiation therapy: A review of current practice and future outlooks, Journal of Radiation Research and Applied Sciences, 11, 361-367.
    30. Shi, J. , Peng, Y. , and Erdem, E. (2014), Simulation analysis on patient visit efficiency of a typical VA primary care clinic with complex characteristics, Simulation Modelling Practice and Theory, 47, 165-181.
    31. Social and Quality of life database system (2018), Reported a record number of cancer patients in 2003-2015, cited 2018 Nov. 13, Available from: http://soci al.nesdc.go.th/social/Default.aspx?tabid=131.
    32. Somayeh, S. (2009), Theory of constraints for publicly funded health systems, Doctor thesis, University of Toronto, Ontario, Canada.
    33. Tiwari, V. and Sandberg, W. S. (2016), Perioperative bed capacity planning guided by theory of constraints, In Proceedings of the 2016 Winter Simulation Conference, Washington, DC, 1894-1903.
    34. Venkatadri, V. , Raghavan, V. A. , Kesavakumaran, V. , Lam, S. S. , and Srihari, K. (2011), Simulation based alternatives for overall process improvement at the cardiac catheterization lab, Simulation Modelling Practice and Theory, 19(7), 1544-1557.
    35. Weng, S. J. , Tsai, B. S. , Wang, L. M. , Chang, C. Y. , and Gotcher, D. (2011), Using simulation and data envelopment analysis in optimal healthcare efficiency allocations, Proceedings of the 2011 Winter Simulation Conference (WSC), Phoenix, AZ, 1295-1305.
    36. Wong, S. Y. , Tsui, K. L. , Chin, K. S. , and Xu, M. (2011), A simulation study to achieve healthcare service quality improvement in accident and emergency department (AED), Proceedings of the 2011 IEEE International Conference onQuality and Reliability (ICQR), Bangkok, Thailand, 259-263.
    37. Zeinali, F. , Mahootchi, M. , and Sepehri, M. M. (2015), Resource planning in the emergency departments: A simulation-based metamodeling approach, Simulation Modelling Practice and Theory, 53, 123-138.