Effective demand management of services

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Effective demand management of services
London 29th of April 2008

• Kate Silvester BSc MBA FRCOphth
• Lean Academy, Heart of England NHS Trust

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Agenda

• Achieving 18/52
• The Issue
• Why have we got a waiting list?
• Demand and Capacity
• Planning Capacity
• Questions

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The elective process
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The Issue

• Planning capacity to deliver
• 18 /52 from GP referral to 1st definitive
  treatment
• Sustainable
• No increase in cost
• No loss of quality

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What is the performance ?
Days from Addition to Waiting List to Operation
primary hip replacement
Special Cause Flag
700
600
500
400
Individual Value
300
200
100
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
Upper process limit for IP wait needs to be here!
Consecutive patients from 01/04/06 to 31/07/06
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What is this chart telling us?

• Process not capable of meeting 18/52
• Elective process
• very variable !
• out of statistical control !
• Waiting times not going up
• Demand doesnt exceed capacity (resources)
• Unless
• growing waiting list ?

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What is the Waiting list for operations?
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What are the charts telling us?

• Waiting time is flat
• Backlog is not going up
• (hidden waiters?)
• (patients diverted elsewhere?)
• So why have we got a waiting list ?

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Why do we get waiting lists?
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Demand and capacity definitions
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3 reasons for queues

• average demand gt average capacity
• average demand average capacity
• BUT
• mismatch between variations in D C
• Queue maintains high utilisation of resource

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1. Demand gt capacity
For model go to www.steyn.org.uk/models/demand
analysis.xls
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2. Variation mismatch
For model go to www.steyn.org.uk/models/demand
analysis.xls
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If av. Demand av. Capacity, variation mismatch
queue
Target
Cant pass unused capacity forward to next week
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Demand capacity for breast clinic
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3. Impact of carve out
For model continue with www.steyn.org.uk/models/de
mand analysis.xls
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Carve out in CT Variation in capacity
Scanner 1
Scanner 2
Mathematically impossible to balance this number
of queues
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The Flaw of Averages
Plans based on averages
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What should we do instead?
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Computer model demonstration
For model go to www.steyn.org.uk/models/demand
analysis 2.xls
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Pareto Analysis
15
30
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Planning the right capacity
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Erlangs Rule
Rule of Thumb In the presence of a varying
demand, it is impossible to run a service beyond
85 utilisation (occupancy) without a queue.
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Reduce variation to reduce cost
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What we need to do

• Understand demand
• Plan capacity to meet demand
• No waiting
• Deal with backlog
• Temporary short term increase in capacity
• Reduce variation in capacity
• Reduce carve out pooling
• Level scheduling
• Reduce the number of process steps (redesign)
• Reduce variation in demand
• Reduce unnecessary demand