Dr Dan Beckett Consultant Acute Physician NHS Forth Valley

   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

 ◦ ◦ ◦ 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

 ◦ ◦ 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’

 ◦ ◦ ◦ 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?

SRI A&E attendances 08:00 - 12:00 hrs

3 2 5 4 1

BUT STILL 97% COMPLIANT AT

20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 240 260 280 290 300

THIS STAGE

Time in ED SRI A&E attendances 12:00 - 16:00 hrs

27% 3 2 1 0 7 6 5 4 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 240 260 280 290 300

Time in ED

SRI A&E attendances 16:00 - 20:00 hrs

2 1 0 7 4 3 6 5

91%

20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 240 260 280 290 300

Time in ED SRI A&E attendances 20:00 - 00:00 hrs

3 2 1 0 7 6 5 4

86%

20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 240 260 280 290 300

Time in ED

SRI A&E attendances 00:00 - 04:00 hrs

7 6 5 4 3 2 1 0

79%

20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 240 260 280 290 300

Time in ED SRI A&E attendances 04:00 - 08:00 hrs

2 1 0 4 3 6 5

77%

20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 240 260 280 290 300

Time in ED

 ◦ ◦ ◦ 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

  ◦ ◦ ◦ ◦ ◦ ◦ 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

   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

3000 2500 2000 1500 1000

Hospital admissions, NHSScotland, October 2010

131%

500 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Total

3 000 2 500 2 000 1 500 1 000 500

Hospital admissions, NHSScotland, October 2010 131%

54%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Emergency Total

3 000

Hospital admissions, NHSScotland October 2010

131%

2 500 2 000 1 500 1 000

BUT YOU CAN’T COMPARE WEEKENDS AND WEEKDAYS!

54%

500

3288%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Elective Emergency Total

3 000 2 500 2 000

Hospital admissions, NHSScotland October 2010

46%

1 500

16%

1 000 500

237%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Elective Emergency Total

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

  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)

Queue Demand Capacity Can’t pass unused capacity forward to next week time Reducing waiting times in the NHS: is lack of capacity the problem?

Bevan et al Clinician in Management (2004) 12:

 Need to eliminate artificial variation and manage natural variation

2500 2000 1500 1000

Eliminating artificial variation (Mon-Fri) NHSScotland October 2010

14%

500

Artificial variability eliminated Monday-Friday

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Elective Emergency Total

   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

 ◦ ◦ ◦ ◦ ◦ 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

 ◦ ◦ ◦ ◦ 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

 ◦ ◦ 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

      ◦ 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

  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

     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...