Understanding Demand and Capacity

1
Understanding Demand and Capacity

• Kate Silvester BSc MBA FRCOphth
• NHS Osprey Programme Coach
• 20.06.07

2
Agenda

• What are we trying to achieve?
• Measure for Improvement
• The issue
• Why have we got a waiting list?
• Demand and capacity
• What we need to do next
• Questions

3
The Issue

• 18 weeks from GP referral to 1st definitive
  treatment
• Patient pathways have many steps
• So to deliver 18 weeks
• No queue at any step?
• How do we do it?
• No increase in cost
• No loss of quality

4
What are we trying to improve?
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
5
What is the 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 ?

6
Summary

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

7
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
11
If av. Demand av. Capacity, variation mismatch
queue
Target
Cant pass unused capacity forward to next week
12
Impact of Carve-out
13
Carve out in CT schedule
3 types of urgency!
Mathematically impossible to balance this
number of queues
14
Advantages of no queue

• Diary ahead is empty
• Capacity meets demand
• patient can be booked in whenever they want or
  need to be
• Advanced Access
• Do todays work today
• No delay, no risk
• Demand goes down
• No management cost of queue
• Frees clinical and admin staff to add value
• Quality goes up!

15
Understand Demand.
Have we got past average activity data or demand
data?
What Capacity (clinic slots / per week) is
required?
16
Beware of Choose and Book!

• We loose visibility of our demand!
• Can only book an appointment if a slot is
  available
• Therefore records planned activity not demand

17
Additions to IP waiting list (demand for
operations)
What Capacity is required? (operating slots)
Need at least 20 points to get an idea of the
variation
18
Capacity how many day case slots required?
Need at least 20 points to get an idea of the
variation
19
How much capacity required?

• Capacity required to meet variations in demand
• 500 clinic slots per week
• 200 in-patient operating slots per week
• 75 day case slots per week

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Adjusted to 42/52 weeks / year

• New patient (including fractures)
• 500 / week 100 appointments / day.
• 100/(12(42/52)) 100/9.7
• 10.3 clinic appointments/ day per consultant
• In Patient Operation slots
• 200/month 50 op slots / week 10 slots/day
• 10/9.7 operations per week / consultant
• 1 operations/consultant/per day
• Day case slots
• 75 month 19 per week 19/5 4 day
• 4/9.7
• 0.4 slots per consultant per day

21
Is the business viable?

• Capacity required
• 10 clinic slots / day
• 1 In-Patient operation slot / day
• 0.4 Day Case operation slot / day
• Income average activity x average PbR rate
• Current Cost of Supply
• 12 consultants
• other staff, materials, fixed asset
  depreciation etc

22
Risks of 18/52

• Old mindset 18/ 52 is frightening
• we havent got enough resources
• 6/52, 6/52, 6/52 approach patient breaches
• Fiddling data
• Carving out for sub groups of patients
• Rather than redesign for all patients
• Data
• Must be presented in SPC format.

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What we need to do

• Plan capacity to meet demand
• Deal with the backlog
• Temporary short term increase in capacity
• NB anther short term increase as backlog wave
  from OP comes through treatment and follow up.
• Reduce variations in capacity
• Quality improvement
• Reduce carve out

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1. Plan capacity to meet demand

• Measure demand
• Need a minimum of 20 values to expose variation
• (2 years monthly or 1 year weekly data)
• Average capacity ? average demand
• Plan av. capacity to meet variations in demand
• Instant access Peaks in normal demand
• Anything less than 80 of peaks in normal demand
  trouble (Erlang rule of thumb)

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2. Deal with the backlog

• Calculate the short term additional capacity
  required to drain out backlog at the rate we can
  afford
• (March and Dec. 2008 targets and beyond)
• NB
• Draining out patient back log ?
• temporary ? inpatient backlog

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3. Capacity management

• Reduce variations in capacity
• Concentrate on improving quality
• Standard work
• (Lean / 6 Sigma philosophy)
• Reduce carve out caused by
• Prioritising
• Sessions
• Specialisation not matched to case mix in demand
• Pareto 8020 rule

27
Summary

• 18 / 52 is a milestone
• A Challenge
• A real opportunity to think differently
• we havent got enough resources
• To
• Releasing Waste, glorious waste!
• Data presented differently
• Demand and capacity training