Transcript System Dynamics Modelling for Emergency / On

Using System Dynamics in
practice: a case study from
emergency health services
Sally Brailsford1, Valerie Lattimer2,
PanayiotisTarnaras1 and Joanne Turnbull2
1School
of Management 2School of Nursing and Midwifery
University of Southampton, UK
UBC Centre for Health Care Management, 8 Dec 2006
Outline of talk
• Brief background to the Nottingham
Emergency Care / On Demand project
• Using system dynamics – qualitative and
quantitative approaches
• Our practical experiences
• Patient preference study
• Key results, implementation of findings,
and conclusions
2
The city of Nottingham
• Robin Hood’s home town
• City with population just under 650,000
in east Midlands of England
• Mainly urban population with some
areas of social deprivation
3
Health services in Nottingham
• Two large NHS Trusts (i.e. hospitals)
– Queens Medical Centre: University teaching
hospital, 1100 beds
– Nottingham City Hospital: 850 beds
• One Accident & Emergency (A&E - the ER)
department – at QMC
• 5 Primary Care Trusts, 350 GP’s
4
Nottingham Health Authority
5
Queens Medical Centre,
Nottingham
6
Background to the project
• Increasing emergency hospital admissions in Nottingham
(>4% year on year increase since 1999)
• Busiest (?) Accident & Emergency Department in the
country; >122,000 patients in 2000/01
• Winter beds crises: “red alerts” and ward closures
• Pressure on staff – stress, recruitment and retention
problems
• Steering Group set up in 2001 to develop Local Services
Framework for unscheduled care
• University of Southampton commissioned to provide
research support to project
7
Membership of steering group
•
•
•
•
•
•
•
•
•
Clinicians and managers from hospitals (plus A&E)
In-hours and out-of-hours GP services
Ambulance Service
Social Services
Mental Health Services
NHS Direct (integrated with out-of-hours GP service)
NHS Walk-in Centre
Patient representative groups
Community Health Council representatives
8
The Southampton research team
• Val Lattimer, MRC Research Fellow, School of Nursing
and Midwifery
• Helen Smith, Reader in Primary Medical Care, Health
Care Research Unit
• Karen Gerard, health economist, HCRU
• Steve George, Reader in Public Health Medicine, HCRU
• Mike Clancy, A&E Consultant, Southampton University
Hospitals Trust
• Me
• Panayiotis Tarnaras and Jo Turnbull, RA’s
9
Strands of the research
•
•
•
•
•
•
Literature review and comparison with
other Health Authorities
Stakeholder interviews
Activity data collection
System dynamics modelling
Descriptive study of patient pathways
Patient survey and preference study
10
Number of patient contacts per 1000
population/month with front door services in
Nottingham (April 1998-March 2001)
25
N H S D ire c t
15
A&E
NE M S
9 9 9 c a lls
10
W a lk -in c e n t re
5
01
Fe
-0
ec
b-
0
0
-0
D
ct
O
-0
0
00
n-
ug
A
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0
Ju
-9
pr
00
A
Fe
ec
b-
9
9
-9
D
ct
O
-9
9
99
n-
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A
Ju
-9
9
-9
pr
99
A
Fe
ec
b-
8
8
-9
D
ct
O
-9
8
98
ug
A
n-
Ju
pr
-9
8
0
A
Conta cts (pe r 1000/m onth)
20
D a te
11
Number of monthly A&E attendances by
method of referral
10000
9000
S e lf
7000
G P in h o u rs / O O H s
6000
O t h e r h o s p it a l
A & E team
5000
S o c ia l s e rvic e s
4000
A m b u la n c e / 9 9 9
3000
O ther
NHS D
2000
1000
D a te
01
n-
0
Ja
-0
ct
O
l-0
0
0
Ju
A
pr
-0
00
n-
9
Ja
-9
ct
O
l-9
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A
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-9
99
n-
8
Ja
ct
-9
8
O
l-9
Ju
pr
-9
8
0
A
Num be r of a tte nda nce s
8000
12
Mean A&E attendance by hour of day and day of week
(April 2000-March 2001)
30
25
Tu e s d a y
W ednes day
15
Th u rs d a y
F rid a y
S a t u rd a y
10
S unday
5
24:00
23:00
22:00
21:00
20:00
19:00
18:00
17:00
16:00
15:00
14:00
13:00
12:00
11:00
10:00
09:00
08:00
07:00
06:00
05:00
04:00
03:00
02:00
0
01:00
Me a n a tte nda nce
M onday
20
D a te
13
pr
Ju -98
n
A -98
ug
O -98
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D t -9 8
ec
F e -98
b
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ug
O -00
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D t -0 0
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b01
A
Num be r of a tte nda nce s
Adult A&E attendances by triage category
4500
4000
3500
1
3000
2500
2
2000
3
1500
4
1000
5
500
0
D a te
14
Emergency and elective admissions rates (per 1000
population/month) at NCH and QMC
7
Ra te pe r 1000 oe r m onth
6
5
E m e rg e n c y C it y
E m e rg e n c y Q M C
4
E le c t ive C it y
3
E le c t ive Q M C
2
1
Emergency and elective admissions by day of the week
-0
1
-0
Fe
b
-0
0
0
De
c
ct
O
-0
0
00
-0
n-
ug
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15
System Dynamics
• Based on Jay Forrester’s Industrial Dynamics
(1969)
• Aim: to analyse complex interacting systems
• Principle: “structure determines behaviour”
• Qualitative aspect: causal loop (influence)
diagrams, to gain understanding of system
behaviour
• Quantitative aspect: stock - flow models
16
Qualitative models: influence diagrams
+
Student
numbers
–
Staff stress
levels
Research papers
published
• Link system constructs (real or abstract)
• Identify feedback loops
• Balancing loops have odd number of “–” signs
• Reinforcing loops or vicious circles have even
number of “–” signs
17
Feedback loop
+
Student
numbers
+
–
Staff stress
levels
Research papers
published
Student
recruitment
+
Reputation
of university
+
18
A balancing loop
+
Student
numbers
+
Student
recruitment
–
–
Staff stress
levels
Research papers
published
+
Reputation
of university
+
19
Behaviour over time
Number of students
time
20
A balancing loop
Waiting lists
–
–
–
Hospital
beds
available
–
GP referral
rate
21
A vicious circle
+
Waiting lists
Extra Govt
money
+
+
Patient
demand
–
–
+
+
Hospital
beds
available
–
GP referral
rate
22
Pros & cons of qualitative models
• Can explore unanticipated side-effects,
and identify performance indicators to
flag up when these side-effects begin to
be felt
• Cannot tell which loops will dominate
without quantifying effects – can be
difficult and subjective
23
Quantitative models
• Need to quantify model parameters to
tell which loops dominate, and when
• Can suggest useful performance
indicators even if numerical data is not
available (e.g. “staff stress levels”)
• Software: Vensim, Stella (ithink)
24
Quantitative models: stocks and flows
Levels (stocks)
Rates (valves): control flow
25
The underlying maths
• Stock-flow equations: ordinary differential
equations, discretised as difference
equations with finite timestep dt
• Various solution methods used, in
different software packages
• Deterministic - “simulation” is not
stochastic
26
Stella software
27
Why System Dynamics?
•
•
•
•
•
•
Huge, diverse, complex system
Many stakeholders with opposing viewpoints
Long timescale (5 years)
Hundreds of thousands of “entities”
Waiting times less important than process flows
Lack of accurate data in sufficient detail from
some providers
• Gaining insights more important than numerical
predictions
28
Modelling phases
• Qualitative: stakeholder interviews and
development of patient flow map; influence
diagramming used to focus discussion about
specific subsystems
• Quantitative: Stella model, populated with
2000 – 01 data, used to investigate (24)
different scenarios, some suggested by
Steering Group and others by us
29
Stakeholder interviews
• Outline draft of patient pathways map derived in
orientation visit (August 2001)
• 30 interviews during Sept - Oct 2001
• Respondents were asked …
– About own work area and areas of influence
– To identify where they thought bottlenecks arose
– To discuss factors which had shaped the system, and
barriers to future development (local politics!)
– To scribble on and amend the map where they thought
we had got it wrong
30
WIC
NEMS
NHSD
Patient flow map
Healthcall
Patient pathways through the emergency
care – on demand system
Arnomedic
GP
OOH
Map version 2: for modelling
GP in-hours
Social Services: EDT,
SAO’s, Hospital SW’s
EMAS
A&E
DPM
Home care & ongoing
casework
Elective admissions
D55: CCU
D57
OP clinics:
direct to
wards (QMC
and City)
Dialysis / oncology /
COPD patients etc
Specialty wards QMC
Paediatrics
GP
adm
CMHT
Home
Further care and
intermediate care
D56
Assessment
unit
Coronary care, Burns
& plastics, Stroke unit
City
Patience
wards
Specialty wards City
Home
Further care and
intermediate care
Elective admissions
31
32
Data for the Stella model
• Many problems obtaining data (!!!) especially,
but not exclusively, in primary care
• Used 2000-01 activity data for “arrivals”
• Length of stay, and patient pathways within
the hospitals, obtained from Dept of Health
Hospital Episode Statistics data, patient
surveys and from interviews with hospital staff
• Internal validation by checking flow balances
33
Model validation – baseline run
Daily Bed Occupancy Rates, Nottingham City Hospital
120.00
80.00
Data
Model
60.00
40.00
20.00
364
353
342
331
320
309
298
287
276
265
254
243
232
221
210
199
188
177
166
155
144
133
122
111
100
89
78
67
56
45
34
23
12
0.00
1
Percentage of beds occupied
100.00
34
Using the Stella model
• Regular trips to Nottingham to demonstrate
the model as it evolved
• Different people at each meeting!
• No problems accepting “continuous” patient
flows; happy with SD technicalities once
explained
• Panel found the computer model fascinating
and were keen to suggest scenarios to test
35
Experimental scenarios
• Reconfigurations of services, e.g.
– Longer opening hours for Walk-in Centre
– Minor cases sent to WiC instead of A&E
– More “step-down” beds to reduce LoS
• New services, e.g.
– (Diagnostic and) Treatment Centre
– Services targeted at specific patient groups
36
Scenario Areas
1
2
3
4
5
6
Increased admissions:
a) 4% growth in emergency admissions
b) 3% growth in elective admissions
Changing “front door” demand
Reducing emergency admissions – for
specific groups of patients
Early discharge
Beds crisis & ward closures (MRSA)
Streaming in A&E (the ER)
37
Trust me, I’m a computer
• Wide spectrum of computer literacy and
quantitative skills in the Steering Group
panel
• Stella model looked impressive because it
was complicated
• Clients warned not to over-interpret the
numbers
• Balance provided by couple of “computer
sceptics” in the Steering Group
38
Main results from Stella model
• Current rate of growth is not sustainable
without extra resources: up to 400
cancelled elective admissions per month
after 5 years
• High impact of relatively small changes
• Alternatives to admission more effective
than discharge management in reducing
occupancy
• Some benefits of moving less severe
patients away from A&E
39
Patient preference study
• Discrete choice experiment (designed and led
by health economist Karen Gerard)
• Enable trade-offs between different aspects
of service to be evaluated
• Respondents - the users of emergency
services (n = 378)
• Patients also asked what factors influenced
their choice of service on that particular day
40
Attributes to be compared
Attribute
Contacting the service
Where advice / treatment
takes place
Time waiting for advice /
treatment after initial
contact
Whether kept informed of
expected waiting time
Who advices / treats
Quality of contact time
Level
Level description
1
By telephone, 2 or more calls
2
By telephone, 1 call
3
In person
1
Travel 15 miles
2
Travel 5 miles
3
At home, no travel
1
4 hrs 30 minutes
2
2 hrs 30 minutes
3
30 minutes
1
No information
2
Some information
3
Full information
1
Paramedic
2
Specialist nurse
3
Doctor
1
Not enough time to deal with problem, interruptions
2
Enough time to deal with problem, interruptions
3
Enough time to deal with problem, no interruptions
Imagine that you are at home. You decide you are in need of urgent medical advice or
treatment. It is sometime after the GP surgery has closed. You decide to contact an out-ofhours service. Which service would you choose?
Service A
Service B
Making contact
Single telephone call
In person
Where advised
At home, no travelling
Nearest NHS facility 15
miles
2½ hours
4½ hours
Informed of expected
wait
No information
No information
Who advices
Specialist nurse
Doctor
Quality of contact
Enough time, no
interruptions
Not enough time,
interruptions
Waiting time between
initial contact and advice
Tick one box only
Main findings
• Keep people informed!! Patients prepared to wait
extra 86 minutes for better information
• Younger patients (<45) preferred doctor advice –
would trade for services located nearer home; this
was less important for older patients
• Lack of interruptions important : location less so
• Potential need to tailor services for older patients,
who are happier to accept treatment by specialist
nurses and paramedics
43
Influence diagrams
• Mainly used to focus panel discussion on
specific issues arising from interviews and
patient preference study, e.g.
– Increased re-admission rates due to premature
discharge
– Effect of GP’s sending patients to A&E to “queuejump” waiting lists for investigations
– Patient behaviour due to long expected waits
– Other behavioural effects: stimulating demand by
providing improved service?
44
Creating demand? - a feedback loop
Patients
choosing to go
to Walk-in
Centre
Additional
resources
placed in A&E
to provide
better service
+

+
Self-referrals to
A&E
+
Long waiting
times in A&E
+
45
Creating demand? - a feedback loop
Patients
choosing to go
to Walk-in
Centre
Additional
resources
placed in A&E
to provide
better service
+
+
+

Self-referrals to
A&E
+
Long waiting
times in A&E
+
46
Implementation
• Results presented to Steering Group in
May 2002
• “Stakeholder day” at Nottingham Forest
Football Club, June 2002
• Local Services
Framework
developed and
implemented by
August 2002!
47
Pros and cons of SD
• Excellent for studying interconnections between
individual departments/providers and the wider
health system
• Very powerful tool giving global view of whole
system
• Loss of individual patient information and variability
between individuals
• Cannot produce highly detailed numerical results
• Difficult to use for operational decision-making:
better for strategic policy-making
48
My personal view of using SD
• Qualitative aspects were very useful (interviews,
maps & influence diagrams)
• Stella model was compelling focus for stimulating
discussion and ideas
• Suspect that some people still fixated on the
numbers despite all the health warnings
• Some places where software was inadequate for
modelling: e.g. effects of variability, decision logic
governing flows
49
References
• S.C. Brailsford, V.A. Lattimer, P.Tarnaras and J.C.
Turnbull, “Emergency and On-Demand Health Care: Modelling
a Large Complex System”, Journal of the Operational Research
Society, 2004, 55:34-42.
• V.A. Lattimer, S.C. Brailsford et al. Reviewing emergency
care systems I: insights from system dynamics modelling.
Emerg Med J, 2004, 21:685-691
• K. Gerard, V.A. Lattimer, H. Smith, S.C. Brailsford et al.
Reviewing emergency care systems II: measuring patient
preferences using a discrete choice experiment. Emerg Med J,
2004, 21:692:697
50