1. Faculty of Industrial Engineering and Management Technion – Israel Institute of Technology David Sinreich and Yariv, N Marmor Winter Simulation Conference 2004 Washington DC, December 5 - 8 A Simple and Intuitive Simulation Tool for Analyzing Emergency Department Operations

2. The Service Industry Until a few decades ago, service industries used simple methods, if any, to design, analyze and operate systems. In recent years we are witnessing an increase in customer demand for fast, efficient services of the highest standard coupled with an increase in the competition between service providers. Management and other decision makers have realized that new approaches to reduce cost, improve resource productivity especially through the utilization of information are needed. The service industries are changing, introducing modern design and evaluation tools and techniques such as MRP and CRM which are based on data gathering and information technology.

3. The Healthcare Industry Cost increase due to use of more advanced hi-tech equipment and drugs. Increase in the number of patients who seek medical care leads to overcrowding in many Emergency Departments (ED) of large urban hospitals.. Increased demand by patients for high quality fast and efficient treatment. Faced with these problems, hospital managers and other healthcare policy makers are being forced to search for ways to distribute efficiently scarce resources, reduce costs, improve productivity while maintaining quality and the highest standard treatment.

4. U.S.AIsrael The Healthcare Industry and Numbers The annual U.S. expenditure on healthcare in 2003 was estimated at $1.5 trillion. This expenditure is expected to almost double and reach $2.8 trillion by the year 2011. In the year 2000 healthcare accounted for 13.2% of the GDP and by 2011 it may reach 17% of the GDP. Hospitals represented 31.7% of the total healthcare expenditure in 2001. This expenditure is expected to decrease to 27% by 2012. The ICBS reports that the annual healthcare spending in Israel in 2001 reached 43 billion NIS, which accounts for 8.8% of the GDP. Hospitals accounted for 36% of the annual healthcare budget in 1999. These numbers are a clear indication that increasing the efficiency and productivity of hospital operations is critical to the success of the entire healthcare system

5. The Emergency Department The ED which serves as the hospital ’ s “ gate keepers “ is the most difficult department to manage especially since it is large, complex, and highly dynamic. The ED has to handle efficiently and effectively a random arrival stream of patients. The ED has to be highly versatile and flexible to be able treat a large array of incidents ranging from minor cuts and bruises to life treating situations. The ED is required to have the ability to react quickly to fast unfolding events which involve a large of casualties.

6. Simulation as a Modeling Tool Discrete-event simulation tools are particularly suitable for modeling large, complex, and highly dynamic systems. Simulation models can provide management with an assessment of the dynamic behavior of different system operational measures such as: ‐ efficiency ‐ resource needs ‐ utilizations and others in face of dynamic changes in different system settings and parameters. simulation can assist management in developing and enhancing their decision-making skills e.g. : ‐ Using What-if scenarios simulation models can assist management in understanding the mutual interactions between different system parameters and their effects on the system ’ s performance (exhibited through the different systems ’ operational measures).

7. Simulation of Healthcare Systems A growing number of studies used simulation in modeling and analyzing Healthcare system in general and ED performance in particular. Simulation is still not widely accepted as a viable modeling tool in these systems due to: ‐ The reluctance of hospital management (especially the physicians in charge) to accept change, particularly if the suggestions come from a 'black-box' type of tool. ‐ Management often does not realize the benefits to be gained by using simulation-based analysis tools. ‐ Management is well aware of the time and cost that have to be invested in building detailed simulation models. ‐ In some cases hospital management believes that spending money to improve the operational performance of systems only diverts funds from patient care. ‐ Lack of experts with experience in modeling large, complex systems As a result only a few successful implementations are reported.

8. Objectives In order to accelerate the proliferation and acceptance of simulation in healthcare systems and EDs, hospital management should be directly involved in the development of simulation projects in order to build up the models ’ credibility. The development should be done in-house by hospital personal instead of by outside experts. As a result the simulation tool has to be based on the following principals: ‐ The simulation tool has to be general and flexible enough to model different possible ED settings. ‐ The tool has to be intuitive and simple to use. This way hospital mangers, engineers and other nonprofessional simulation modelers can run simulation models with little effort. ‐ The tool has to include default values for most of the system parameters. This will reduce the need for comprehensive, costly and time-consuming time studies ‐ The tool has to include a decision support system for easy display of simulation results

9. Essential Basic Conditions The governing process which determines the ED performance operation is similar for different EDs The differences that do exist between these processes are limited to several well defined parameters. Only if these conditions are true the objectives set forward can be achieved

10. Fixed Processes The Model's Basic Building Blocks Generic Activities Generic Processes High abstraction level Flexible enough to model any system and scenario Difficult to use; requires knowledge and experience Medium abstraction level Flexible enough to model any system which uses a similar process Simple and intuitive to use after a brief and short introduction Low abstraction level Can only model and analyze the system it was designed for Simple and easy to use after a quick explanation Modeling Options

11. Laying the Foundation During a two year study which was funded by the Israel National Institute for Health policy (NIHP) a Generic Process was determined and a simulation tool was developed. ‐ 6 out of 25 – 27 major hospital operating in Israel participated in the Study. ‐ teams of supervised students equipped with standardized code lists of the different process elements conducted time and motion studies in the selected hospitals (hundreds of man-hour in each hospital). ‐ Additional data was gathered from each hospital ’ s information system. This data included the patient admission data, lab work data and imaging center data. Based on the observations the gathered data and interviews with senior staff members 19 individual process charts each representing a typical patient types were determined. Clustering similar process charts based on a similarity measure.

12. The Similarity Measure Average Similarity Level – 0.44 Average Similarity Level – 0.66 Average Similarity Level – 0.75 Average Similarity Level – 0.54 Average Similarity Level – 0.62 Based on this it is safe to argue that in the hospitals that participated in this study, patient type has a higher impact in defining the operation process than does the specific hospital in which the patients are treated

13. Patient Types Element Precision ElementInternalSurgicalOrthopedicTraumaFast-Track Vital Signs 3.6%5.7%8.9%6.7%3.2%2.2% E.C.G. Check 3.6%11.3%16.0%13.1%9.7%3.0% Treatment Nurse 5.5%12.6%11.1%10.8%15.6%3.9% Follow-up Nurse 10.1%47.5%43.0%19.7%50.1%7.9% Instructions Prior to Discharge 16.5%30.7%29.1%25.2%43.2%11.9% First Examination 4.6%6.3%4.4%7.4%10.2%2.8% Second or Third Examination 6.7%11.4%8.0%11.8%30.2%4.3% Follow-Up Physician 5.9%27.8%26.0%32.9%----5.4% Hospitalization /Discharge 11.0%13.0%19.3%32.9%15.0%7.5% Handling Patient and Family 6.5%15.9%9.3%9.5%18.4%4.6% Treatment Physician 11.3%12.9%15.4%21.2%49.9%7.1% Patient Precision 5.2%9.4%8.1%9.5 %7.6% Precision of the Different Time Elements The combined precision values indicate, that aggregating element duration according to patient type, regardless of the hospital in which the patients are treated, actually improves the precision levels of all the different elements. It is possible and it makes sense to develop a general simulation tool based on a unified process

14. ARENA’s Simulation Model Graphical User Interface based on the Generic Process Mathematical Models Decision Support System Suggested Structure of the Simulation Tool

15. Model Validation The validation process is comprised of two stages: First, five simulation models were created using the developed tool in conjunction with the suggested default values and the other specific values for each of the five EDs that participated in the study. Ten 60-day simulation runs were performed for each of the five EDs. The performance of each of these models was compared to the actual data that was obtained from each of hospital's information systems and the field study conducted in the different EDs. Comparison of the Results Obtained for the ED in Hospital 1 P-Value Practical Difference Simulation Std. Simulation Average (10 runs) Database Average (2 years) Patient Type 0.336.7%13182195Internal 0.186.6%10211198Surgical 0.284.5%7150157Orthopedic

16. Model Validation Comparison of the Results Obtained for the ED in Hospital 2 P-Value Practical Difference Simulation Std. Simulation Average (10 runs) Database Average (2 years) Patient Type 0.672.2%20399408Internal 0.751.7%11240236Surgical 0.286.1%9156166Orthopedic P-Value Practical Difference Simulation Std. Simulation Average (10 runs) Database Average (2 years) Patient Type 0.486.7%13143134Fast-Track 0.1414.5%19197172Internal 0.068.4%810395Surgical 0.3214.8%69381Orthopedic Comparison of the Results Obtained for the ED in Hospital 3

17. Model Validation Comparison of the Results Obtained for the ED in Hospital 4 Comparison of the Results Obtained for the ED in Hospital 5 P-Value Practical Difference Simulation Std. Simulation Average (10runs) Database Average (2 years) Patient Type 0.316.5 %18261279Internal 0.0914.4%13125146Surgical 0.596.0%15142134Orthopedic P-Value Practical Difference Simulation Std. Simulation Average (10 runs) Database Average (2 years) Patient Type 0.3210.6%17178161Internal 0.595.7%16149158Surgical 0.681.6%6127125Orthopedic

18. Model Validation Internal Patients During a Weekday in the ED of Hospital 1 Orthopedic Patients During a Weekend day in the ED of Hospital 4

19. Model Validation Internal Patients During a Weekday in the ED of Hospital 5

20. Model Validation Comparison of the Results Obtained for the ED in Hospital 6 P-Value Practical Difference Simulation Std Simulation Average (10 runs) Database Average (2 years) Patient Type 0.369.5%16161147Internal 0.673.2%11149154Surgical 0.0913.8%7132116Orthopedic A sixth ED was chosen and data on its operations was gathered from the hospital's information systems and through observations. A simulation model was created using the tool's default values augmented by some of the gathered data and ten 60-day simulation runs were performed.

21. Model Validation Surgical Patients During a Weekday in the ED of Hospital 6 Internal Patients During a Weekend day in the ED of Hospital 6

22. The relative importance of the different performance measures The system that translates desired performance values to required changes in the system Operational system's parameters update ED system description after validation Current status of the different performance measures Expert System Simulation Model Augmenting the System with an Expert Model

23. Discrete Event Simulation Mathematical Models Decision Support system Simulation Tool System’s alert Hospital’s Information System’s Data- Base Periodical Trigger Control and Short Term Decision Support System

24. To the Israeli National Institute for Health Policy and Health Services Research NIHP To all the students from the IE&Mgmt. Faculty and the Research Center for Human Factors and Work Safety which assisted in gathering the data and analyzing it and especially to Almog Shani and Ira Goldberg Thanks

25. Staff ’ s walking time Patient Arrivals at the Imaging Center Patient Arrivals to the ED Mathematical Model Development Based on the gathered information the following mathematical models were developed to be used for estimating:

27. Imaging Center

28. Specialists

29. Scheduling Medical Staff

30. תוספת זמן בשניות ED Physical Characteristics

33. Statistical tests reveal that the square-root of the patients' arrival process can be described by a normal distribution. Let X pihd be a random variable normally distributed which represents the square-root of the number of patients of type p who arrive at the ED of hospital i at hour h on day d. Estimating the Patient Arrival Process Patient Type HospitalInternalSurgicalOrthopedic 11.1801.2931.187 20.9581.0380.840 30.8620.6690.974 It is clear from these factors that hospital 1 is larger than the other two hospitals

34. Estimating the Patient Arrival Process Internal Patients on Saturday Internal Patients on Monday

35. Estimating the Patient Arrival Process Surgical Patients on Wednesday

36. Estimating Patient Arrival Process to the Imaging Center To accurately estimate the waiting time ED patients experience at the imaging center it is important to estimate the following: ‐ patients' walking time ‐ the time it takes to perform an X-ray ‐ the time it takes the radiologist to view the X-ray to return a diagnose Imaging centers (X-ray, CT and ultrasound) are not always ED-dedicated. In some cases these centers serve the entire hospital patient population. In these cases two different patient streams are sent for service to the imaging center: ‐ patients who come from the ED ‐ patients who come from all other hospital wards. These two streams interact and interfere with each other and compete for the same resources In these case it is imperative to estimate the hospital patient arrival process.

37. Estimating Patient Arrival Process to the Imaging Center A linear regression model was used to estimate the hospital patient stream. In order to maintain the model's linearity, four separate regression sub- models were developed. ‐ A sub-model to estimate the arrivals between 6 AM and 12 midnight on weekdays. ‐ A sub-model to estimate the arrivals between 6 AM and 12 midnight on weekends. ‐ A sub-model to estimate the arrivals between 12 midnight and 6 AM on weekdays and weekends. ‐ A sub-model to estimate the arrivals between 12 noon and 5 PM in the cases the combined imaging center only operates part of the day.

38. Estimating Patient Arrival Process to the Imaging Center Patient Arrivals to the Imaging Center on A Tuesday

39. Estimating the Staff ’ s Walking Time From the observations made in the five hospitals it was clear that the medical staff spends a considerable amount of time, during each shift, walking between the different activity points in the ED. ‐ patient beds ‐ medicine cabinet ‐ nurse's station ‐ ED main counter The estimation model is based on the following parameters: ‐ The distances between the different activity points ‐ The number of beds each staff member is in charge of ‐ The ED space dimensions each staff member operates.

40. Estimating the Staff ’ s Walking Time Physician ’ s Walking Model Nurse ’ s Walking Model The fit of the above models as indicated by R 2 is 0.737 for the physician's walking model and 0.675 for the nurse's walking models,