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IE417 Project: Analysis of Simulation Modeling for Emergency Department
Transcript of IE417 Project: Analysis of Simulation Modeling for Emergency Department
Analysis of simulation modeling for emergency department
Thorwarth , M., & Arisha, A. (2012). A simulation-based decision support system to model complex demand driven healthcare facilities. IEEE.
By Jiliang Ma, Ryan Mendolla
May 3rd 2013
Simulation modeling for healthcare process is viewed as high degree of complexity due to the significant difference in the statistical attributes of treatment process and patient arrival patterns.
Within the process, there are several interactions between patients and medical staff involved, which makes the process more sophisticated in a discrete event simulation model.
Therefore, Michael Thorwarth and Amr Aisha have developed a Discrete Event Simulation (DES) framework that incorporates Multiple Participant Pathway Modeling (MPPM) and Flexible Resource Allocation (FRA).
The evaluation of their proposed model, including feasibility, potential for clinical implementation, limitations, and assumptions is given in this presentation, which may prove beneficial in illustrating the key features and their interconnected functionality.
General Nature of Problem
Simulation built around...
Emergency Department throughput
Radio Frequency Identification cost benefit
impatient bed occupancy
pediatric intensive care planning effectiveness
They all provide recommendations for improving the aforementioned metrics.
However...they are not easily translated to other healthcare facilities/organizations .
Static modeling of the staff resources (doctors, nurses, medical assistants, etc.) w.r.t. the patients cannot capture a system effectively.
The objective, then, is to develop a robust framework and test it within an Irish Emergency Department, assuring that the model results, when compare to hospital records, fall within 99% confidence interval.
Future healthcare organizations considering using simulation as an evaluation tool
Discrete event simulation modeling researchers (including software package creators and users like the ones here)
healthcare organization management looking to understand workflow w.r.t. bottom line
Creating a DES model for hospital departments with many resources floating to several tasks simultaneously, as well as patients moving through a long and intricate process chain very quickly, becomes a formidable challenge
The level of accuracy of hospital data used for arrival patterns
Treatment times (timeliness and exactness of entries into system).
Solution Method, Technique or Principles
The mechanism behind Multiple Participant Pathway Modeling (MPPM) under the consideration of Flexible Allocated Resources (FRA).
The process of ED considered in this model
Distribution to specific unit
The patient follows a “passive” pathway
the staff follows an “active” pathway, tending to patients
then rotating back to other duties as the patient proceeds to the next step
Multiple Participant Pathway Modeling is employed, where...
Resources include the medical staff, following the flexible resource allocation mechanism where the staff can be shared among many processes depending on the necessity, and evaluation criteria are patient length of stay and staff turnaround time/utilization
Automated modeling rounds out the key components, allowing the user to follow 3 steps to create their own custom process grid.
After running the simulation, the results:
fell under a 99% confidence interval
obtaining staff utilization at less than 75%
second wait approximately 275 minutes
length of stay approximately 500 minutes
The study indicated that using documentation aids significantly reduces process time for staff, and that low utilization is not affecting flow.
Considering the data:
Hospital records were used for arrival patterns, patient groups, wait times and treatment times
Positive attributes of the model
Onsite interviews and job shadowing are necessary to completely capture the system, the organization’s beliefs about improvement could significantly affect the success of the project
Undergoing a simulation project in a large, fast-paced environment expectantly requires:
much time and deliberation
initial investment in the software package
It avoids considering staff as static resources, allows for easy replication by different organizations, and engages in visual simulation so that debugging becomes easier.
Simulation package used is never made explicit, the MPPM is suspect in certain areas, and the transition between steps of the patient pathway is not discussed
For MPPM, we understand it as resources (staff) assigned to process steps once, but there is no evidence of follow-up throughout the remainder of the process
Once the key components are established, the data can be pulled into the simulation and analyzed.
Step 1: create a template of the patient pathway, and assigning “sender” and “receiver” attributes to the template to make implementation of FRA and MMPM feasible.
Step 2 involves adding the necessary amount of patient pathways the facility requires.
Step 3: creates the active pathway of the staff, and assigns the “resources” (staff) to their relevant treatment steps
In addition to the parameters measured by these authors, measurement of the relationship between stress levels and resource utilization, and iatrogenic illness could be incorporated
Extended Cyclomatic Metric 
Markov chain 
Value Stream Mapping could be a congruent tool so that as the simulation runs, the user can see how the value stream is affected directly by changes made
Other groups creating accident and emergency department models concluded that logically sound defaults can make models more translatable, Fletchert et al (2007), and viewing the entire system, similar to the SEIPS model, can increase the effectiveness of the model, Lane et al (2000) .
Overall, the model presented here is effective in terms of reusability and dynamic modeling of a system, however a SEIPS approach in the future might create opportunities to improve holes in patient pathway modeling, and conform the model to encourage the use of other crucial performance metrics and their relationships to different variables.
 Gunal, MM., Pidd, M. (2010). Discrete event simulation for performance modelling in health care: a review of the literature. Journal of Simulation, 2010(4), 42-51. doi:10.1057/jos.2009.25
 Bisgaard Lassen, Kristian., van der Aalst, Wil M.P. (2009). Complexity metrics for Workflow nets. Information and Software Technology, 51(3), 610-626. doi:10.1016/j.infsof.2008.08.005
 Arisha, Amr, Harper, Paul, Thorwarth, Michael (2009, December). Simulation Model To Investigate Flexible Workload Management For Healthcare And Servicescape Environment. Paper presented at the 2009 Winter Simulation Conference, Austin, Texas. Abstract retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5429210