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SIMULATION IN ARENA TO DETERMINE POTENTIAL BOTTLENECKS FOR DERMATOLOGY CLINIC REDESIGN

Tiffany N. Adams

H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, Georgia

Abstract

As demand increases for timely healthcare, the

expected queues in the registration process must be anticipated and ideally reduced. Anne Arundel Dermatology Clinic in Annapolis, Maryland is preparing for an increase from currently thirty patients per day to ultimately one hundred per day. Using the simulation software Arena, the impact of growth can be evaluated and bottlenecks in reception area then studied. Assuming an increase in population, this simulation predicts that patient wait time is negligible, which allows for a layout design with the administration suite to be adjacent to the registration area for staff convenience.

Introduction

Anne Arundel Dermatology Clinic (AADC) in

Annapolis, Maryland has a current throughput over 7,500 patients per year (30 per day). The clinic was able to provide some specific data, but other data used in this simulation had to be based on management estimates. AADC plans to expand their facility (Figure 1), and with

that, their patient population, hoping that change will not potentially crowd the registration area.

The ideal future design for AADC currently places the administration area with convenient access, but only has one passage from the waiting area to the exam rooms, with the check-in and check-out windows facing this same hallway. The optimal clinic configuration, from the perspective of a clinic owner or administrator, should simultaneously maximize clinic profit, patient satisfaction, and staff satisfaction. However, determining an optimal staffing and facility size is complicated by the (often conflicting) nature of these objectives (Swisher, Jacobson, Jun, & Balci, 2001). For example, in this clinic, a configuration that maximizes staff satisfaction by placing the administration suite near the registration desk may create a potential bottleneck, thereby decreasing patient satisfaction. Bottlenecks are areas that increase longer wait time for the patient, and more concerning for the clinic, a crowded area.

Arena software was chosen to model this simulation because of its ease of use. It is compatible with flow charts, which were easily understood and approved by the clinic. The receptionists were also easily modeled as

Figure 1: Proposed Future Clinic Floor Layout Design

Single throat

 

Office Area

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servers in Arena, with the respective queues representing wait lines for check-in or check-out, which was the main focus of this simulation.

This simulation runs for an 8-hour day, for a replication of 10 days. The clinic schedules appointments in fifteen-minute intervals, with the first appointment at 8:00am and the last at a time to allow the clinic to close at 5:00pm. The data recording system for the clinic only tracks the appointment times and approximate length, not the exact length of time for the check-in or check-out process, so these times were estimated by the clinic. These estimated times are very important, and a concern for error here is great, given that the data supplied is only a conservative estimate and not recorded by a computer system.

Problem

The current design supports the current capacity, but with an increase of more than 200% of its current population, the clinic needs to know that three servers for the registration process are adequate. The most ideal design (Figure 1) unfortunately has a single throat between the waiting area and the exam rooms, which also serves as the registration window area; however, this design is the most ideal for the administrative offices, with the office manager, medical records, and reception area all easily accessible. A second layout option is shown in Figure 2, which does have a double throat connecting the waiting

area to the exam rooms, but unfortunately separates the administrative office area from the reception area. The clinic would like to use the layout pictured in Figure 1, as long as wait times and queue lengths are reasonable, and revert to the plan in Figure 2 as an alternative option.

Project Objectives

The purpose of this project is to prepare the clinic for likely results as the number of patient appointments per day increases. The goals of this project are as follows:

• Confirm that the single throat, in Figure 1, is adequate for a predicted increase in volume

• Collect and analyze data to predict wait times in check-in and check-out processes

• Evaluate adequacy of three receptionists, as opposed to two or four as possibilities

• Advise practice on predictions and probability of wait time, given current data provided by clinic

Methodology

Literature Search After searching literary databases, few articles appear

to have been written on the specific matter at hand. Most references for discrete-event simulation are focused on scheduling policies and admissions, concluding that admission decision rules for scheduling appointments may

Double throat

 

Separates administrative area from registration area

Figure 2: Alternate Future Clinic Floor Layout Design

Office Area

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increase utilization of staff and patient throughput (Rising, Baron, & Averill, 1973); however, the option for altering schedule rules was out of the scope of this specific project. Information Sources

At the beginning of this project, data from August and September 2010 were collected from the practice managers. This clinic schedules patient appointments every fifteen minutes, but will check them in early if they arrive before appointment time; therefore, the exponential distribution occurs naturally when describing the lengths of the inter-arrival times in a homogeneous Poisson process. However, since this clinic schedules specific appointment times, this does not satisfy the memoryless property, and could possibly fall under another distribution (Alexopoulos, Goldsman, Fontanesi, Kopald, & Wilson, 2008). For simplistic reasons and compatibility with Arena simulation software, all arrivals are estimated to be in a random Poisson distribution. When scheduling, the clinic also may schedule two patients with the same appointment time, so the simulation allows for two patients to arrive simultaneously throughout the day, to account for this higher possibility. The simulation automatically ends

when the 60th, 80th, or 100th patient leaves the system, depending on the model. The estimates of arrival times and frequencies are shown in Table 1. Additional data times supplied, estimated, or agreed upon by the clinic are shown in Table 2, with their respective distributions.

Model Building

The simulation software Arena was used for this project. The patients were considered entities and the receptionists were the resources of focus. A simplified flowchart of a patient’s experience is shown in Figure 3. The black background boxes are the processes of focus, where the patient would be in this potential bottleneck area: check-in, walking from wait area to exam room, and check-out.

Assumptions are included in the model for a realistic result; three receptionists, versatile for the check-in or check-out process, are each allotted fifteen minutes per hour for other tasks, such as answering phones, scheduling patients, or taking a break. The clinic mails registration forms to patients before their appointment, as an opportunity for a faster check-in; the clinic estimates about half of their patients complete forms prior to appointment

Process Time assigned Check-in (new) Normal (1, 0.22) Fill out clipboard (new) Normal (10, 22) Return clipboard (new) Tri (2, 4.5, 6) Check-in (est) Tri (1, 2, 3) Wait in area Tri (0, 30, 40) Med.Asst. walk patient to room Normal (0.5, 0.12) Patient in exam room (new) Tri (15, 45, 90) Patient in exam room (est) Tri (10, 30, 45) Check-out (new) Tri (0.5, 3, 4) Check-out (est) Normal (1, 0.22) Check-out (accutane) Normal (3, 0.22)

Patient Volume

Arriving 1 or 2 at a time? Frequency

100 patients 1 4 min 100 patients 2 25 min 80 patients 1 5 min 80 patients 2 30 min 60 patients 1 8 min 60 patients 2 60 min

For Testing: 30 patients 1 15 min 30 patients 2 180 min

Figure 3: Patient Flow at Anne Arundel Dermatology Clinic

Table 1: Arrival Frequency Based on Patient Volume Table 2: Process Times (Poisson)

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time, so the probability used in the simulation is 0.5. In the clinic layout as shown in Figure 1, nine exam rooms are available for use. Two months of clinic data, analyzed by reason for appointment, show the distribution of 18% new patients and 81% established patients. The clinic also wanted to take into consideration Accutane patients, which account for the remaining 1% of the patient population, who take slightly longer to check-out because they must schedule the next appointment. These probabilities are incorporated into the model, and follow the respective process times.

Arena also allows the creation of time plots (Figure 4), which were used as a visual for the number of patients in the check-in or check-out process. The x-axis is time, in minutes, beginning at 8:00am; the y-axis is number of patients in the process. Figure 4 is specifically a replication for a 60 patient volume scenario.

Verification, Validation, and Testing

Several verification, validation, and testing (VV&T) techniques were applied during this project. Balci describes verification as building the model right and validation as building the right model (Balci, 1997). To gather results as realistic as possible, the simulation was run for ten eight-hour days. Another common technique for testing is to run the new model with historical data, and compare performance measures (Elbeyli & Krishnan, 2000); this model was tested by running a simulation with the same assumptions, but current volume of approximately thirty patients per day. This resulted in arrival times every fifteen minutes, an average utilization per receptionist of 7.8%, and an almost nonexistent wait

Check-in

Check-out

Figure 4: Time Plots

Number of

Patients in

Process

Number of

Patients in

Process

8:00am 9 10 11 1 2 3 4 5:00pm Noon-1pm is Lunch

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time, as seen in Table 3. These observations are very similar to the current state of the practice, and therefore the model performs adequately well and provides results at the level of accuracy desired for this project.

In addition to the other VV&T techniques mentioned above, the simulation was reviewed and discussed with the Chief Executive Officer of Anne Arundel Dermatology Clinic.

Results and Conclusion

This project was initially simulated in order to gain a better understanding of the utilization of receptionists, which in turn identifies the amount of patients in the bottleneck area. The Process Analyzer program in Arena was used to compare the utilization of receptionists and wait times in line from the increase of 30 patients to 100 patients, as seen in Table 3. “Receptionists Busy” indicates the number of receptionists busy on average; for example, 0.793 * 480 minutes is 381 minutes of work per day among all three receptionists checking in and checking out patients in the registration process. Individual utilization per receptionist is also predicted at about 26.4% on a day with patient volumes at 100; the maximum utilization per receptionist would be 75%, given the fifteen-minute allotment for other tasks per hour. As seen from check-in and check-out times, few patients wait to register, but those that do, rarely wait more than two minutes. Given the earlier analysis of optimal clinic configuration, the clinic layout with the office administrative area adjacent to the registration area will be the most beneficial to the clinic up to 100 patients in volume.

Next Steps

After the completion of this initial project, many additions could be made to strengthen the model and

expand its application. The simulation could determine optimum utilization of exam rooms or include a possibility of extended hours for this specific clinic. This simulation could also be further reviewed for accuracy once the clinic’s volume has expanded. Since time was a restricting factor in this project, direct observation was not available as an option for data collection; in the future, direct observation could provide better data estimates.

When a simulation expands with more process steps, the room for error increases; this project along with other simulations could be studied to determine if adding a process step, even if only estimated, could prove beneficial to the model. Also, once these wait times are confirmed with the given assumptions, the simulation could be used to report expected wait times based on patient volume in a clinic, in order to establish benchmark data.

This model could also be used for other clinics to determine wait lines based on individual clinic daily volume.

Acknowledgements

The author wishes to acknowledge the constant

support and advice of David Cowan from the Health Systems Institute of the Georgia Institute of Technology as well as Anne Arundel Dermatology Clinic for supplying data, reviewing the simulation, and approving the use of the information gathered during this research. She would also like to recognize Professor Christos Alexopoulos for his help with the Arena simulation program.

References Alexopoulos, C., Goldsman, D., Fontanesi, J., Kopald, D.,

& Wilson, J. (2008). Modeling patient arrivals in community clinics. Omega: The International Journal of Management Science, 36(1), Retrieved from

Patient  Volume  

Repli-­‐cations  

Rep  Length  (min)  

Receptionists  Busy    (out  of  3)  

Receptionist  Utilization  (per  receptionist)  

Check-­‐in    wait  average  

Check-­‐  in    wait    max  

Check-­‐out    wait  average  

Check-­‐out  wait  max  

100  Patients   10   480   0.793   0.264   0.137   1.898   0.071   1.651  80  Patients   10   480   0.643   0.214   0.104   1.491   0.055   1.196  60  Patients   10   480   0.477   0.159   0.033   1.037   0.041   0.892  30  Patients   10   480   0.233   0.078   0.005   0.138   0   0  

Table 3: Comparison among Potential Patient Volumes

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http://www2.isye.gatech.edu/~sman/publications/OME790_final.pdf

Balci, O. (1997). Principles of Simulation Model Validation, Verification, and Testing. Transactions of the Society for Computer Simulation, 14(1), 3-12.

Elbeyli, S., & Krishnan, P. (2000). In-patient flow analysis using promodel™ simulation package. Informally published manuscript, Department of Food and Resource Economics, University of Delaware, Newark, Delaware. Retrieved from https://ageconsearch.umn.edu/bitstream/15827/1/sp000002.pdf

Rising, E. , Baron, R. , & Averill, B. (1973). Systems analysis of a university-health-service outpatient clinic. Operations Research, 21(5), 1030-1047.

Swisher, J., Jacobson, S., Jun, J., & Balci, O. (2001). Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation. Computers & Operations Research, 28(2), doi: 10.1016/S0305-0548(99)00093-3

Biographical Sketch

Tiffany Adams is an Industrial and Systems

Engineering student in her senior year at Georgia Institute of Technology. This project was completed as part of her undergraduate research with the Health Systems Institute. Tiffany will graduate in May 2011 with a Bachelors of Engineering and plans to continue her studies by pursuing her Masters of Science degree in Health Systems. Tiffany joined the Institute of Industrial Engineers during her freshman year and Society of Health Systems her junior year with hopes to further her understanding and knowledge of the healthcare system in order to improve the efficiency of the current system.

E-mail: tadams7@gatech.edu