1. Introduction to Health Systems Engineering Instructor: Prof. Jingshan Li Dept. of Industrial and Systems Engineering University of Wisconsin - Madison Fall 2014 ISyE 417 1

2. Discrete Event Simulation 2

3. Health Care Delivery Today LIMITED Access to Care Emergency departments No Beds Admitted patients waiting--BOARDERS Patients in the HALLWAY Ambulance DIVERSION Dissatisfied Providers & Patients 3

4. Example: Emergency Department of a Hospital in PA Targets 37 stations (1350 visits/station) 44K sq ft 0 stations short <1% Patients Leaving Without Being Seen 0 Hallway beds 0 Boarders Satisfied Providers & Patients 4

5. Example: Emergency Department of a Hospital in PA Reality Today 20 stations (2589 visits/station) 24K sq ft 17 stations short >7% Patients Leaving Without Being Seen Hallway Beds Daily 10+ Boarders Dissatisfied Providers & Patients 5

6. Hospital Capacity Management Targets 100% 0 bed needs 0 boarders Surge capacity YES, to transfers Right care, Right location Reality Today >>100% many beds needed many boarders Always at capacity Not always, beds Some care, any location (changing) 6

7. Hospital emergency departments (ED) across the US are overcrowded. The number of EDs is decreasing, while patient volume is rising. According to the 2004 report by the Center for Disease Control and Prevention (CDC), from 1992 to 2002, annual ED visits were up by 23%, while the number of EDs was down by 15%. According to the 2006 National Hospital Ambulatory Medical Care Survey (NHAMCS), the number of annual ED visits in the U.S. grew from 90.3 million in 1996 to 119.2 million in 2006, and the number of hospital EDs has decreased from 4019 to 3833, which implies 32% increase in ED visits and 5% drop in ED capacity. 7 Hospital ED Crowding

8. Overcrowding results in delayed treatment, long patient waiting time and stay, overburdened working staff, patient elopement and low throughput, also leads to many other problems, e.g., medical errors due to overloading, high turnover, diverting of ambulances, etc. In addition, it has a significant financial implication, since revenues generated by ED are a major contributor to the hospital revenues as a whole. Moreover, ED overcrowding is a world wide problem. 8 Hospital ED Crowding

9. Solutions: Traditional Approach Transfer Patients Away Don't go to the ED … it is for “emergencies” Go to PCP (who's not in or already overbooked) Go to urgent care centers Diversion to other EDs  to where? Increase capacity BUILD more beds FIND more STAFF Double the size of the ED Discharge EARLIER Its NOT enough  and its not working PLUS  Where's the Capital ??? 9

10. What can we do now? Understand workflow, map processes Critical Resources Demand-Capacity Interdependencies Redefine & Expand Capacity to Care Limited Resources Limited Capital 10

11. Analyzing the patient flow to minimize length of stay, improve efficiency and reduce crowding is necessary and important. Evaluate performance in terms of patient outcomes, such as length of stay, and patient throughput, waiting time, staff and resource utilization, etc., Identify system bottlenecks and redistribute limited resources optimally using the data obtained from the clinical information system, and Determine optimal workforce configuration and resource allocation to achieve desired performance. 11 Solution: Models

12. Prediction / What – if analysis Apply analysis tools Queueing Model Markov Chain Model Discrete Event Simulation 12

13. Discrete Event Simulation (cont.) 13

14. Case Study: Improving Patient Flow at an Emergency Department Prediction / What – if analysis By developing a model of the current ED, the system analysts would test their theories identify key bottlenecks and problem areas seek a solution that would maximize the existing resources and reduce the duration of the patient stay 14

15. Queueing Theory Queueing simply means forming a line while waiting for something All queueing systems possess the same basic elements: Customers Servers (resources) Queue(s) When analyzed, it is clear that queues are very common in healthcare 15

16. Modeling various Queuing Systems 16

17. Queueing theory models to analyze patient flow and staffing levels are available, but mainly at a higher level. Such models typically use single or multiple servers to represent ED or other operations as a whole, without considering the details and complexities involved. Typical phenomena, such as blocking, repeated services, etc., are often ignored. The specific assumptions, lack of sophistication and computational intensity for complex cases, restrict the applicability of queueing theory models. Markov Models Demand Capacity 17 Queuing Models

18. Simulation Modeling Simulation Involves creating a representation of a real process in such a way that experiments we perform and conclusions we draw on the model reflect what would happen with the real process in much more details. Goal: to model the behavior of a system, estimate its performance under various scenarios (what if analysis), and recommend possible changes to the system The simulation model helps us analyze complex processes in which variability has a significant effect 18

19. Modeling Basics Entities E.g., patients, files Attributes: E.g., patient’s age, sex, health status Resources E.g., physicians, nurses, MRI equipment Flow logic Events and event calendar Simulation clock Starting/stopping conditions Variables E.g., current number of patients in system 19

20. Output Performance Measures Anything you are interested in tracking and reporting Patient length of stay Total patients through the system (throughput) Average waiting time of patients Maximum waiting time of patients Utilization of a resource (proportion of time it is busy) Leave without being seen 20

21. Simulation Models Specialized simulation software E.g., Arena, Simul8, MedModel, Flexsim Benefits Do not need to be a computer programmer to build models (nice GUIs) Automatically handles event calendar, random variable generation, statistical output, etc. Provides animation capabilities 21

22. Discrete Event Simulation (cont.) 22

23. Central Baptist Hospital, Lexington, KY Pre-bed In-room 23 ED Simulation Example

24. 24

25. Arrivals 25 ED Simulation Example

26. Resources 26 ED Simulation Example

27. Operations 27 ED Simulation Example

28. Simulation model 28 ED Simulation Example

29. In patient room 29 ED Simulation Example

30. Validation 30 ED Simulation Example

31. Sensitivity 31 ED Simulation Example

32. Adding resource Improvement Combining registration and triage: LOS reduced by 5% Physician visit within 30 minutes: LOS reduced by 7% Simultaneous improvement in operations: LOS reduced by 3-4% 32 ED Simulation Example

33. New schedule: LOS reduced by 24% Adding float nurse: up to 30% LOS reduction 33 ED Simulation Example

34. Discrete Event Simulation (cont.) 34

35. 35 Pediatric Simulation Example

36. Simulation Model Resources Available Exam Rooms Patients Enter System Patients Exit System Simulation Model 36

37. Length of Stay is calculated from time patient registers until time patient checks out 37 Pediatric Simulation Example

38. Wait for MD is calculated from end of prior service (rooming, RN or resident) until the MD enters the exam room 38 Pediatric Simulation Example

39. 39 Pediatric Simulation Example

40. Discrete Event Simulation (cont.) 40

41. 41 Pharmacy Simulation - Preparation and delivery of Antineoplastic medication Antineoplastic medication – most commonly prescribed treatment for cancer Target rapidly dividing cancer cells by disrupting their life cycles Prior to, concurrently or after surgery, radiation therapy, bone marrow transplant Narrow therapeutic index – only safe when used within strict guidelines Under dosing: little or no impact; Overdosing: potentially life threating adverse effects Stability is short Efficient, accurate preparation, prompt delivery

42. 42 Patient entry Checking prescription Dose calculation Label preparation Drug mixing and delivery

43. 43 Pharmacy Simulation - Preparation and delivery of Antineoplastic medication Problems: Long waiting time Required steps Delivery process Improvement Remote chemo hood Dedicated delivery Early preparation of labels

44. Pharmacy Simulation 44

45. Pharmacy Simulation 45 Implementation: Early preparation + dedicated delivery >50% reduction, 21 minutes

46. Discrete Event Simulation (cont.) 46

47. Possible Simulation Applications Facilities design (labs, clinics, radiology, ER’s, OR’s, etc.) Planning for future changes Staff planning Analyzing patient capacity Equipment planning and logistical analysis Emergency preparedness Bed capacity management Improving the transport of critically ill patients Alternative designs for the surgical suites Residential care capacity planning 47

48. Simulation Applications Pros Details Animation Variety Cons Time Dependency Difficult in findings Issues Process Data Insights 48

49. Simulation Applications Medical Simulation Hybridization Computer and medical Simulation and analytical model 49