1. Dr Dan Beckett Consultant Acute Physician NHS Forth Valley

3.  Warning signals ◦ Four hour emergency access standard ◦ ED LoS - time profiles ◦ Boarding ◦ (Cancelled elective activity) ◦ (Delayed discharges)  Whole system overview ◦ NHSFV capacity and flow dashboard  Elective vs Emergency imbalance ◦ Optimising patient flow by reducing its variability

4.  Four hour emergency access standard ◦ Useful as an indicator of whole system pressure ◦ Poor compliance indicates with ED overcrowding  Associated with an increase in mortality both in patients admitted and patients discharged from the ED ◦ Limited usefulness as an early indicator of pressure to trigger escalation

5.  ED LoS distribution ◦ Can demonstrate pressure in the system that is not evident when just looking at compliance with the four hour emergency access standard ◦ ‘Crisis spike’

10.  ED time curve ◦ Useful for retrospective analysis ◦ Crisis spike correlates with poor performance ◦ Useful for proactive escalation?  Dynamic monitoring of the proportion of patients leaving the ED after 210 minutes?

11. 27% BUT STILL 97% COMPLIANT AT THIS STAGE

12. 91% 86%

13. 77% 79%

14.  Boarders ◦ Different models of boarding exist  Exclusively ‘front door’  Exclusively ‘back door’  Mixed model ◦ Irrespective of model, increasing numbers of boarders indicates system pressure and should be monitored/controlled ◦ Boarded patients have poor outcomes

18.  NHSFV capacity dashboard  Real time information ◦ Pressure vs Capacity ◦ Admissions vs Discharges ◦ Emergency vs Elective ◦ Predicted vs Observed activity ◦ Whole system vs Individual patient ◦ Warning signals across the whole system as a trigger to escalation

23.  Competition between emergency and elective flow ‘silos’ can directly lead to ED overcrowding  Perceived conflict between the 18 week RTT target and the 4 hour emergency access standard  Significant variation in numbers of patients admitted over the week

24. 131%

25. 54%

26. 3288% 131% 54% BUT YOU CAN’T COMPARE WEEKENDS AND WEEKDAYS!

28. 46% 16% 237%

29.  Elective admissions display more variability (artificial variability) than emergency admissions (natural variability) ◦ Counter-intuitive!

30.  Difficult to plan staffing levels for such high levels of variation (largely artificial variation)  Invariably staffed for ‘average’ levels of activity resulting in periods of demand > capacity (leading to ED overcrowding and poor outcomes) and capacity > demand (waste of resources)

31. time Demand Capacity Queue Can’t pass unused capacity forward to next week Reducing waiting times in the NHS: is lack of capacity the problem? Bevan et al Clinician in Management (2004) 12:

32.  Need to eliminate artificial variation and manage natural variation

33. 14%

34.  Reduces overall variation ◦ Reduces ED overcrowding ◦ Less waste  Reduces patient boarding  In 2006 the IOM published a report asking hospitals to use operational management tools (queuing theory) to address patient flow issues that lead to ED overcrowding

35.  Boston Medical Centre ◦ Significant problems with ED overcrowding 2003 ◦ Emergency work more predictable and less varied than elective work ◦ Reprofiled elective cases Monday-Friday  Subsequently eliminated all block scheduling ◦ Split elective and emergency surgical work ◦ Used queuing theory to guide resources for emergency work

36.  Boston Medical Centre ◦ Reduced variability in demand for surgical HDU beds by 55% ◦ Reduced nursing hours – saving $130K per annum ◦ Reduced cancelled/delayed surgery from 334 to 3 (99.5%) for the same time periods April-September 2003/2004 (pre- and post-implementation) ◦ Reduced ED waiting time by 50% and improved ED throughput by 45 minutes per patient

37.  Now many examples of successful implementation ◦ Cincinatti Childrens Hospital  Weekday OR waiting time reduced by 28% (despite an increase in case volume of 24%)  Weekend OR waiting time decreased by 34% despite an increase in volume of 37%)  Capacity boosted by equivalent of 100 bed expansion ◦ Great Ormond Street Hospital

38.  Assign responsibility for the patient flow problem ◦ Chief Operations Officer or Vice President  Establish a multidisciplinary team  Collect and analyze data on bottlenecks  Eliminate or smooth artificial variation  Manage natural variation (queuing theory)  www.ihoptimize.org

39.  Managing Capacity and Demand across the patient journey. Clinical Medicine 2010. 10:1 13-15  Winter Pressures in NHS Scotland 2008-2009. A report for the Emergency Access Team, Scottish Government

40.  Professor Derek Bell, Imperial College  Professor Eugene Litvak, Institute for Healthcare Optimisation  Dr Claire Gordon, NHS Lothian  Bas Gough, Scottish Government  Guy Blackburn, NHSFV  Thanks for listening...