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Redesigning Emergency Care
Lessons from the UK
Paul Walley
Associate Professor
Warwick Business School
[email protected]
Introduction
• The UK government applies a “4 hour target” journey time
for all patients attending A&E departments
• A&E departments’ performance has improved from 65% target
achievement (2001) to 96% in 2005/6
• A key catalyst of the improvement was the Emergency Services
Collaborative which applied “whole system process redesign” to all
200 sites in England with 24-hour A&E departments
• Work is now being done to repeat this improvement in Scotland
• This presentation summarises some of the technical lessons we
have learned during the programmes
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1. Really Understand Demand
Don’t confuse demand with activity
Activity:
Patient
is ill
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is a significantly modified measure for demand
often “double-counts” demand
includes “failure demand” - for example rework
No space
at GP
Phones
NHS Direct
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Demand varies over time for a number of reasons:
Medical admissions 7-day moving average
30.00
25.00
20.00
15.00
10.00
5.00
0.00
1
9
17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 177 185 193 201 209 217 225 233 241 249 257 265 273 281 289 297 305 313 321 329 337 345 353 361
Ye a r f r om A pr i l
Demand varies by
1. Day of week
2. Weather related
3. Special cause events
4. Random factors
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BUT
Healthcare is arguably one of the
least seasonal services we know
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2. Develop the Right Capacity Plans
15.0
Capacity ?
10.0
5.0
8
6
4
2
12pm
10
8
6
4
2
0.0
10
Demand
12am
Hourly arrival rate
Daily arrival pattern at A&E (all patients)
Time of day
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What is the relationship
between capacity, demand
and queue length?
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High
Queue length
0
0%
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Utilisation
100%
7
Server
Server
Server
Server
Server
Server
Server
Server
Queue type A
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Queue type B
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3. Demand variation is introduced by the system…
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3. … and is amplified by supply chain effects
120
100
No.
80
Admissions
Discharges
60
40
20
24
/6
/0
2
27
/5
/0
2
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/4
/0
2
1/
4/
02
0
10
4. Don’t cluster demand by symptom…
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Minor
Patients
Chest Pain
Respiratory
Distress
Abdominal
Pain
Elderly
Care
“Off Legs”
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4. … instead “Think Process”
Assess
Treat
Discharge
Assess
Investigate/
Observe
Treat
Discharge
Assess
Investigate/
Observe
Admit to
medical ward
Treat
Discharge
Assess
Investigate/
Observe
Admit to
surgical ward
Theatre
Discharge
Assess
Investigate/
Observe
Transfer to
MH care
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5. Design to absorb variation (and eliminate waste!)
a) The conventional model with 4 in-process queues
Reception
Wait
Triage
Wait
Assess
Wait
Treat
Wait
Discharge
b) See & Treat (one in-process queue)
Reception
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Wait
Assess, treat & discharge
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6. Look at capacity yield losses
About half of A&E target breaches are due to lack of bed availability BUT
Beds are not usually the true bottleneck
Why is this patient still in hospital?
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
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Responding to treatment but still poorly (60%)
Not seen a doctor yet
Successfully treated but given another disease
Waiting for tests/treatment
Waiting for results of tests
Waiting for someone to discharge him
Waiting for TTOs (drugs)
Waiting to see OT/Physio
Staying for meal (nothing at home in fridge)
Waiting for relatives to collect (after work)
Waiting for other transport
Going home tomorrow
Complex discharge (social services)
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Test Question: Has this investment worked?
You have spent £2m (capital) on an new “Medical Assessment Unit.
The staff costs are £2m p.a. A&E target achievement was measured
1 month before opening and 1 month after:
80.00
74.15
70.00
60.00
Target %
50.00
40.83
40.00
30.00
20.00
10.00
0.00
Before MAU
After MAU
% Major patients admitted or discharged within 4 hours
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7. Use time series data (SPC) to measure performance
Avoid “two-point comparisons” as they disguise system behaviour
Target achievement
90
80
70
60
50
40
30
MAU
opens
20
10
MAU
fills
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Use SPC to:Monitor and Control a process
Measure the effect of changes made
Look at system behaviour
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SPC makes the impact of changes very obvious
LOS data – 80% shorter LOS
These peaks occur when there are more
than 2 pts with fractured neck of femur on the
ward
This run of seven points above
the mean suggests that the
process has changed – possibly
due to the increased use of day
surgery
Source: David Tomlinson
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Summary
The improvement of emergency care is a whole system
problem
The first challenge is to understand true demand
Healthcare introduces most demand variation, rather
than suffers from adverse seasonality
System redesign practices can be used to reduce (sometimes eliminate) built-in delays
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