1. Christy Dempsey, RN MBA CNOR August 26, 2009

2.  Understand how other areas of the hospital directly impact the flow of patients in the ED  Demonstrate how queuing analysis and simulation modeling can be employed inside and outside the ED to improve flow and increase capacity without building infrastructure or hiring more staff  Learn how other organizations have used this information and methodology for significant and sustainable results

3.  "Hospital chief executive officers should adopt enterprise-wide operations management and related strategies to improve the quality and efficiency of emergency care.”  “By smoothing the inherent peaks and valleys in patient flow, and eliminating the artificial variabilities that unnecessarily impair patient flow, hospitals can improve patient safety and quality while simultaneously reducing hospital waste and cost.”

4.  ED and PACU boarding/overcrowding  Staff shortages - nursing and physician  The “any bed available” phenomenon  Quality concerns related to nurse:patient ratios, medical errors, and adverse events  Frustration due to unpredictable schedules and inability to care for patients the way physicians want to care for them  Increasing workloads and decreasing reimbursement

5. Hospital Census Direct Admits ORED Which do we have the most control over??

6.  NO ◦ These are usually sick patients ◦ Sent from physician office ◦ May be scheduled through Cath Lab or other procedural area – higher risk patients ◦ Random arrivals

7.  NO

8.  “ED overcrowding is caused by a complex set of conditions that occur across hospital units and across the entire health care system. Inability to move admitted patients from the ED to the appropriate inpatient unit stands out as a major driver of ED overcrowding.” Emergency Department Utilization and Capacity July 2009

9.  YES!! ◦ Variability in the elective surgery schedule is the culprit ◦ Totally schedulable ◦ Totally within our control ◦ Peaks and valleys in the elective schedule result in peaks and valleys in inpatient census

10.  Boarding ◦ ED ◦ PACU ◦ OR  Inappropriate patient placement ◦ Any bed available  Increased length of stay  Increased risk of morbidity/mortality  Increased risk of adverse events

11.  Physicians ◦ Frustration due to unpredictable schedules ◦ Rounding in multiple locations ◦ Long waits to do cases – elective and non-elective ◦ Frequent phone calls from nurses unaccustomed to care for their patients ◦ Longer lengths of stay result in increased risk of complications, infection, adverse events ◦ Inability to grow, practice and revenue implications

12.  Unpredictable schedules ◦ OT ◦ Low workload days ◦ Staffing unfamiliar cases  Equipment competition  Recruitment and retention issues  Training issue for downstream nursing units

13.  Overcrowding  Boarding  Diversions  Safety  Quality  Liability  Burnout  Recruitment/Retention

14.  Lower overall utilization despite overcrowding  Loss of contracted payors  Liability  Reduced reimbursement – medical errors, never events, boarding  Capital constraints  Duplication of human and material resources during peaks  Wasted human and material resources during valleys

15.  Recognize flow is an organizational issue  ED is at the mercy of the inpatient census  Manage uncontrollable variability  ED admissions  Reduce/eliminate controllable variability  Smooth elective hospital admissions  Assure transparent and credible data  Involve physicians  Make progress

16. The Cause? Controllable (Artificial) Variability

17. Types of Variability  Uncontrollable (Natural) –Random but often predictable –Manageable but cannot be eliminated –Example: emergent/urgent ED volume  Controllable (Artificial) –Non-random –Caused by management practices such as scheduling, staffing practices –Example: elective surgery schedule

18.  Combine the hard science of rigorous data collection and analysis with the soft science of change management and operations expertise  Collaboration between physicians and hospital leadership  Culture must change—if you always do what you’ve always done, you’ll always get what you’ve always gotten!

19. Real-Life Variability in the OR

20. Inappropriate Patient Placement

21.  Build and staff to peak demand in EDs, ORs and in downstream units; tolerate overspending on staff and material expenses, underutilization during non-peak times  Staff below the peaks; tolerate ED diversions, nursing overloading and medical errors  Staff for averages and try to flex up or down to manage unpredictable demand; tolerate the same negative effects Three Typical “Fixes”

22. The Real Solution 3-step process Smooth artificial variability and provide resources to meet patient-driven (vs. schedule-driven) peaks in demand

23. Step 1 Separate Scheduled from Unscheduled OR Flow Step 1 Separate Scheduled from Unscheduled OR Flow Step 1 implementation Collect and analyze data on emergent/urgent (add-on) cases, including arrival patterns and urgency Apply queuing theory to determine capacity needed to accommodate add-on cases within clinically acceptable wait times Adjust plan based on physician and hospital input Allocate resources to meet the separate demands of scheduled and unscheduled volumes

24. Step 2 Step 1 Separate Scheduled from Unscheduled OR Flow Step 1 Separate Scheduled from Unscheduled OR Flow Step 2 Smooth flow of scheduled patients Step 2 Smooth flow of scheduled patients Step 2 implementation Evaluate daily case variation in scheduled cases by surgical service as well as by destination units Work in collaboration with surgical practices to redesign the OR schedule to smooth daily case volume based on destination unit Smoothing should take into account clinic schedules, surgeons’ teaching and other responsibilities, hospital case mix, and size of destination units

25. Step 3 Step 1 Separate Scheduled from Unscheduled OR Flow Step 1 Separate Scheduled from Unscheduled OR Flow Step 2 Smooth flow of scheduled patients Step 2 Smooth flow of scheduled patients Step 3 Determine Bed and Staffing needs Step 3 Determine Bed and Staffing needs Step 3 implementation Apply simulation models to determine the number of beds and staff needed to achieve a desired level of service Maximize throughput by streamlining the discharge process and addressing length of stay issues Implement process improvement in downstream units including admission and discharge processes, ED specific flow improvements, hospitalist and medicine specific flow improvements

26. Queuing Theory

27. What is Queuing Theory?  Mathematical tool used to determine capacity needed to handle random arrivals with constrained resources  Used in industry since the early 1900’s  Relevance to improving patient flow newly recognized  Can be applied to any procedural area with a mix of elective and add-on cases

28. Queuing Theory Used When:  Arrivals are random – ED volume and urgent/emergent OR cases  Average service time - ED visit lengths or urgent/emergent surgical case duration + room turnover time - can be calculated  Number of servers (ED treatment rooms/ physicians, OR, cath labs) is limited

29. Provides Guidance on:  Optimum number of treatment or operating rooms for add-on (urgent/emergent) cases  Optimum number of ED physicians  Average wait time by triage or urgency class  Percent of time an ED, ED physician, or OR will be available immediately for an emergency patient  Utilization rates of ORs and ED rooms or physicians

30. ED Data Needed for Queuing Analysis  Patient arrivals  Triage level of patient arrivals  Average visit length – door to door

32.  Inputs ◦ Arrival rates by hour ◦ Acuity of arrivals ◦ Average service rate ◦ Room turnover between patients ◦ Staffed shifts ◦ Desired waiting times  Outputs ◦ Waiting time for each acuity ◦ Utilization rates of rooms ◦ Outputs by shift

33. # Classes 555555 Start ClassAAAAAA End ClassEEEEEE # Servers404142434445 Day TypeWD Start Time777777 End Time15 TOT555555 Results Service Rate0.335 Arr Rate 10.14 Arr Rate 21.89 Arr Rate 36.17 Arr Rate 43.19 Arr Rate 50.47 Wait 1(Immediate)4.54.44.34.24.14 Wait 2 (Emergent)5.35.254.94.74.6 Wait 3 (Urgent)13.512.711.911.210.610 Wait 4 (Less Urgent)76.36353.245.84035.4 Wait 5 (Non-urgent)256.7185.4141111.490.775.5 % Avail0.330.40.460.510.570.62 Util % 88.4686.384.2482.2880.4178.63

34. # Classes 555555 Start ClassAAAAAA End ClassEEEEEE # Servers404142434445 Day TypeWD Start Time333333 End Time11 TOT555555 Results Service Rate0.335 Arr Rate 10.17 Arr Rate 22.62 Arr Rate 36.11 Arr Rate 43.95 Arr Rate 50.39 Wait 1 4.54.44.34.24.14 Wait 2 5.75.55.45.25.14.9 Wait 316.815.514.413.512.611.9 Wait 4319.8190.5132.6100.179.565.3 Wait 58590.71833.9819.2470.4308.1219 % Avail0.030.090.150.210.260.32 Util % 98.7596.3494.0591.8689.7787.78

35. # Classes 55555 Start ClassAAAAA End ClassEEEEE # Servers1819202122 Day TypeWD Start Time11 End Time77777 TOT55555 Results Service Rate0.335 Arr Rate 10.09 Arr Rate 21.26 Arr Rate 32.28 Arr Rate 40.8 Arr Rate 50.05 Wait 1 9.99.38.88.48 Wait 2 12.711.811.110.49.8 Wait 331.527.223.821.219 Wait 49270.35646.138.9 Wait 5142.4101.977.561.450.3 % Avail2.042.342.612.863.08 Util % 74.2570.3466.8363.6460.75

36.  Arrival patterns change ◦ New hospital or closure of an ED increases volumes ◦ Flu season  Treatment times change ◦ Additional physician or nursing staff ◦ Reduction of boarding allows for a reduction in average treatment time

37. # Urgency Classes (ESI groups) Included 555555 Start Class111111 End Class555555 # Treatment Rooms 404142434445 Day TypeWD Start Time11p End Time7a Service Rate150 mins TOT555555 Arr Rate 10.17 Arr Rate 22.62 Arr Rate 36.11 Arr Rate 43.95 Arr Rate 50.39 Results Wait 1-Immediate 3.93.83.73.63.5 Wait 2-Emergent 4.84.64.54.34.24.1 Wait 3-Urgent 11.110.49.89.28.88.3 Wait 4-Less Urgent 53.34538.733.829.826.7 Wait 5-Not Urgent 156.5119.294.376.86454.3 % Avail0.430.50.560.610.670.72 Util % 85.5183.4281.4479.5477.7376.01

38. Interpreting the Results  Trade-offs between waiting time and resources applied  Hard science vs soft science balance

39. Ensuring Compliance  Active involvement by project committee of physician leaders, top hospital management  Timely review of questionable urgency/acuity classifications  Performance monitoring

40.  Average wait time by triage/urgency class  Compliance with maximum wait time by triage/urgency class  Treatment room/physician utilization  Availability of a room when a level one (emergency) case arrives  Boarding days/times  Appropriate patient placement in downstream units

41.  Frequency of Re-evaluation ◦ Quarterly under normal circumstances ◦ Immediately if major issues  Triggers for Change ◦ Non-elective volume increases or decreases ◦ New services or surgeons with non-elective cases ◦ Expansion or contraction of ED or OR capacity  Trade-offs related to Changes ◦ Staff availability ◦ Resource constraints

45. Demonstrated Results: Physician Satisfaction Physician Satisfaction Increase patient flow The most noticeable shifts (scores which changed >= +/- 3.0 points) tended to involve patient flow issues. Ease of Admitting Patients

46. BEFORE AFTER Highest volume day (69 cases) is 1.6 times the lowest volume day (42 cases) vs. Substantial variability in elective surgery cases before: highest volume day (82 cases) is 2.15 times the lowest volume day (38 cases) Wellstar-Kennestone Reduction of Variability

47.  http://www.rwjf.org/pr/product.jsp?id=45929&c http://www.rwjf.org/pr/product.jsp?id=45929&c  http://www.acep.org/uploadedFiles/ACEP/Membersh ip/Sections_of_Membership/intnatl/news/2008Boardi ngReport.pdf http://www.acep.org/uploadedFiles/ACEP/Membersh ip/Sections_of_Membership/intnatl/news/2008Boardi ngReport.pdf  http://www.referenceforbusiness.com/encyclopedia/ Pro-Res/Queuing-Theory.html http://www.referenceforbusiness.com/encyclopedia/ Pro-Res/Queuing-Theory.html  http://www.hhnmag.com/hhnmag_app/jsp/articledis play.jsp?dcrpath=HHNMAG/Article/data07JUL2008/0 80715HHN_Online_Eitel&domain=HHNMAG http://www.hhnmag.com/hhnmag_app/jsp/articledis play.jsp?dcrpath=HHNMAG/Article/data07JUL2008/0 80715HHN_Online_Eitel&domain=HHNMAG  Litvak E, Long MC, Cooper A, McManus M. Emergency department diversion: Causes and solutions. Academic Emergency Medicine. 2001;8(11):1108- 1110.

48. Christy Dempsey, RN MBA CNOR SVP of Clinical Operations Press Ganey Associates, Inc 417-877-7666 cdempsey@pressganey.com