Improving healthcare systems by managing variation

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Improving healthcare systemsby managing
variation patient flow

• Richard Lendon MBChB FRACGP, Innovation
  Knowledge Group, MA
• Hugh Rogers MB BCHIR FRCS, National Clinical
  Lead, IPH
• Kate Silvester BSc FRCOphth MBA, Innovation
  Knowledge Group, MA
• Richard Steyn MS FRCSEd(C-Th)FIMCRCSEd MRCGP,
  National Clinical Lead

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Outline

• Introduction
• variation
• Demand Capacity
• how queues form
• how we manage queues
• setting the right capacity
• Relating this to a whole healthcare system
• Your own data

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What is the problem ?

• For patients and staff
• delays
• unpredictable process times
• unpredictable outcomes?
• deteriorating clinical condition
• physical psychological
• chaos

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If we didnt have queues.

• patients wouldnt have unnecessary waits
• we would see and treat everyone today
• we could spend more time and money on sorting out
  patients
• we could treat more patients
• we could hit our targets more readily

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Hitting targets

• Distort the data
• Distort the system
• Improve the system

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Better Care Without Delay

• Safety
• Efficacy
• Patient-centredness
• Timeliness
• Equity
• Efficiency

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Variation is the key

• Root cause of delays for patients is variability
• not volume
• We create most of the variability
• Reduce variability by creating a steady flow of
  patients through the system at a steady rate
• reduce delays

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Understanding variation

• Every process displays variation
• Common cause variation
• stable, consistent pattern of variation
• chance, constant causes
• Special cause variation
• assignable
• pattern changes over time

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Special cause
Common Cause
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Given 2 numbers, one will be bigger than the
other!
What action is appropriate?
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Trends

• take 2 numbers, one will be bigger
• beware point measures, before after
• A major reduction in waiting time from an
  average of 70 days before the change to 35 days
  after the change

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Reduction in waiting time
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Reduction in waiting timeHospital A - seemingly
real improvement
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Reduction in waiting timeHospital B - existing
trend, did the change make any difference?
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Reduction in waiting time Hospital C -
sustaining the gains?
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Trends- or plot the bl. dots!

• 7 or more points continuously increasing or
  decreasing
• 7 consecutive points either all above or all
  below the average

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Outline

• Introduction to data
• variation variation variation
• Demand Capacity
• why do queues form
• how we usually manage queues
• setting the right capacity
• Relating this to healthcare systems
• Your own data

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NHS waiting lists and evidence of national or
local failureMartin et al, BMJ, 25th January
2003

• little evidence supports the notion that the
  waiting list phenomenon in most hospitals in
  England arises from an overall mismatch between
  supply and demand

Health warning Some of this is counterintuitive
Well what is it then?
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Why do queues form?

• because demand exceeds capacity?
• mismatch between demand capacity?
• we want queues to keep us busy
• utilised?

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1. Demand gt capacity
www.steyn.org.uk
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Equilibrium
Did Not Attend Cancellations Deaths
River flows in
Evaporation
Demand
Water level stays the same
River flows out
Activity
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2. Variation
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Variation mismatch queue
Cant pass unused capacity forward to next week
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Demand capacity for breast clinic
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Queue keeps utilisation 100
Did Not Attend Cancellations Deaths
River flows in
Evaporation
Demand
Water level stays the same
River flows out
Activity
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Moment of truth

• Even if
• average demand average capacity,
• variation in demand variation in capacity
• QUEUE !
• Is your system causing your queue?

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How we traditionally manage queues

• delay
• forced booking
• carved out capacity (ring fencing)

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Carve out (ring fencing)

• Reserve capacity for selected groups
• fixed session working
• urgency
• condition
• specialisation
• sub-specialisation
• other

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3. Carve-out
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Endoscopy
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Endoscopy Demand, Capacity Activity
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Carve out
73 queues
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4. Pooling demonstration
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Examples of pooling

• Post office queues
• British airways check-in desks
• People do it spontaneously now!
• It doesnt have to apply to all the work
• Pool what you can
• Specialise where you must

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To reduce delays, we need to minimise variability
by creating a steady flow of patients through the
system at a steady rate.
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Reducing variation

• Reduce carve out/ring fencing
• pooling
• Smooth flow
• booked admissions
• Process redesign
• reduce complexity
• Reduce variation in capacity

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Booked Appts. - Chemotherapy
Baseline 3-5/2002
Carve Out 6/2002
Booked Appts. from 7/2002
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Lung Cancer GP referral to First Treatment
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Setting the right capacity
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See todays demand today

• set capacity at 80 of fluctuation in demand
• flex capacity to meet demand
• annualised hours
• and then
• can we reduce variation in demand?

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Key messages

• we probably have enough capacity
• waiting list is result of variation
• demand capacity mismatch at bottleneck
• carve out makes it worse
• waiting list initiatives fail
• cause tidal waves
• exhaust staff

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Outline

• Introduction to data
• variation variation variation
• Demand Capacity
• how queues form
• how we usually manage queues
• setting the right capacity
• Relating this to healthcare systems
• Your own data

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Patient flow across a healthcare system
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The Issue - Waiting in AE

• Aim - NHS target
• By 2004 no-one should be waiting more than 4
  hours in accident and emergency from arrival to
  admission,transfer or discharge.

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Board report patients lt 4 hours in AE
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Fudge the system! (Toast scraping)
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Traditional process map
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Streams in the system
Presents at AE
Numbers discharged

• discharged
• transferred
• admitted to bed

Phones GP
AE time
28 days
Planned admission
Readmitted because we got quality wrong
Seen by GP
time as outpatient
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Key measures

• Demand capacity
• admissions
• discharges
• Throughput
• AE time
• length of stay
• test turnaround
• Quality
• Readmissions in 28 days
• In-hospital deaths

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Number of Admissions
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Discharges
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Discharges vary more than Admissions
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Sorting out the system 1

• what is causing discharge variability?
• i.e. variability in capacity of beds?

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Length of Stay
Medical patients
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Variable capacity

• 5 day service
• Decision making
• ward rounds
• test availability results
• Discharge process
• ward rounds
• test availability results
• many handoffs
• pharmacy etc

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Sorting out the system 2

• What is causing the admission variability?
• i.e. demand for beds?

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Which is most variable Emergency or Planned?
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Moment of truth

• The planned elective activity is more variable
  than emergency demand
• this is under our control !
• the electives impact emergency flow
• smooth the elective flow
• better scheduling

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Which is most variable Emergency or Planned?
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Summary

• major variation in demand is elective admissions
• which are under our control
• greatest variation is in capacity i.e. discharges
• which are also under our control
• mismatch in these causes the queues
• trolley wait, cancellations etc

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Where is the bottleneck?
Presents at AE

• test turnaround
• ward rounds
• TTAs
• transport

Numbers discharged
Phones GP
AE time
Length of stay
28 days
Readmitted because we got the quality wrong
Seen by GP
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What would you do?

• smooth all discharges?
• reduce variability in length of stay ?
• daily ward rounds, plan discharge,
• test turnaround, pharmacy, discharge process
• smooth elective admissions?
• more beds?
• if so, how many?

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Summary

• Variation mismatch between admissions and
  discharges causes queues
• Increasing bed capacity doesnt work
• Reducing variability is key
• Discharges
• Planned admissions
• Common sense is not common
• Its hard work!

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50 fewer cancelled operations April 2002
August 2003
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10 more elective admissions April 2002 August
2003