using system dynamics in practice: a case study from emergency health services
DESCRIPTION
Using System Dynamics in practice: a case study from emergency health services. Sally Brailsford 1 , Valerie Lattimer 2 , PanayiotisTarnaras 1 and Joanne Turnbull 2. 1 School of Management 2 School of Nursing and Midwifery University of Southampton, UK - PowerPoint PPT PresentationTRANSCRIPT
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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 MidwiferyUniversity of Southampton, UK
UBC Centre for Health Care Management, 8 Dec 2006
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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
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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
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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
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Nottingham Health Authority
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Queens Medical Centre, Nottingham
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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
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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
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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
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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
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Num ber of patient contacts per 1000 population/m onth w ith front door services in Nottingham (April 1998-M arch 2001)
0
5
10
15
20
25
Date
Con
tact
s (p
er 1
000/
mon
th)
NHS Direct
A&E
NEMS
999 calls
Walk-in centre
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Number of monthly A&E attendances by method of referral
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Date
Num
ber
of a
tten
dan
ces
Self
GP in hours/OOHs
Other hospital
A&E team
Social services
Ambulance/999
Other
NHS D
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M e a n A & E a tte n d a n c e b y h o u r o f d a y a n d d a y o f w e e k (A p r il 2 0 0 0 -M a rc h 2 0 0 1 )
0
5
10
15
20
25
3001
:00
02:0
0
03:0
004
:00
05:0
0
06:0
0
07:0
008
:00
09:0
0
10:0
011
:00
12:0
0
13:0
0
14:0
015
:00
16:0
0
17:0
018
:00
19:0
0
20:0
0
21:0
022
:00
23:0
0
24:0
0
Date
Mea
n at
tend
ance
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
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Adult A&E attendances by triage category
0
5001000
15002000
25003000
35004000
4500
Date
Nu
mb
er
of
att
en
da
nce
s
1
2
3
4
5
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Emergency and elective admissions rates (per 1000 population/month) at NCH and QM C
Emergency and elective admissions by day of the week
0
1
2
3
4
5
6
7
Date
Rate
per
100
0 oe
r mon
th
Emergency City
Emergency QMC
Elective City
Elective QMC
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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
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Qualitative models: influence diagrams
• 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
Student numbers
Staff stress levels
+
Research papers published
–
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Feedback loopStudent numbers
Staff stress levels
+Research papers published
–
Student recruitment
Reputation of university
+
++
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A balancing loopStudent numbers
Staff stress levels
+Research papers published
–
Student recruitment
Reputation of university
+
++
–
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Behaviour over time
time
Number of students
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–
Waiting lists
Hospital beds available
GP referral rate
–
–A balancing loop
–
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A vicious circle
+–
Waiting lists
Hospital beds available
GP referral rate
–
–
+Extra Govt money
+Patient demand
+
+
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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
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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)
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Quantitative models: stocks and flows
Rates (valves): control flow
Levels (stocks)
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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
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Stella software
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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
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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
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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
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31Elective admissions
WIC
NEMS
Healthcall
Arnomedic
GP in-hours
EMAS
GP adm
Social Services: EDT, SAO’s, Hospital SW’s
CMHT
D57
D56
Specialty wards QMC
Specialty wards City
Elective admissions
Home
Home
Further care and intermediate care
Assessment unit
D55: CCU
Home care & ongoing casework
Dialysis / oncology / COPD patients etc
Further care and intermediate care
Patience wards
GP OOH
Coronary care, Burns & plastics, Stroke unit
City
A & E
NHSD
DPM
Paediatrics
Patient pathways through the emergency care – on demand system
Map version 2: for modelling
OP clinics: direct to wards (QMC and City)
Patient flow map
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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
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Daily Bed Occupancy Rates, Nottingham City Hospital
0.00
20.00
40.00
60.00
80.00
100.00
120.00
1 12
23
34
45
56
67
78
89
10
0
111
12
2
13
3
14
4
15
5
16
6
17
7
18
8
19
9
21
0
22
1
23
2
24
3
25
4
26
5
27
6
28
7
29
8
30
9
32
0
33
1
34
2
35
3
36
4
Pe
rce
nta
ge
of
be
ds
oc
cu
pie
d
Data
Model
Model validation – baseline run
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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
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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
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1 Increased admissions:
a) 4% growth in emergency admissions
b) 3% growth in elective admissions
2 Changing “front door” demand
3 Reducing emergency admissions – for specific groups of patients
4 Early discharge
5 Beds crisis & ward closures (MRSA)
6 Streaming in A&E (the ER)
Scenario Areas
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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
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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
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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
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Attribute Level Level description
Contacting the service 1 By telephone, 2 or more calls
2 By telephone, 1 call
3 In person
Where advice / treatment takes place
1 Travel 15 miles
2 Travel 5 miles
3 At home, no travel
Time waiting for advice / treatment after initial contact
1 4 hrs 30 minutes
2 2 hrs 30 minutes
3 30 minutes
Whether kept informed of expected waiting time
1 No information
2 Some information
3 Full information
Who advices / treats
1 Paramedic
2 Specialist nurse
3 Doctor
Quality of contact time1 Not enough time to deal with problem,
interruptions2 Enough time to deal with problem, interruptions3 Enough time to deal with problem, no interruptions
Attributes to be compared
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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-of-hours 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
Waiting time between initial contact and advice
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
Tick one box only
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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
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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 “queue-
jump” waiting lists for investigations– Patient behaviour due to long expected waits– Other behavioural effects: stimulating demand by
providing improved service?
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Creating demand? - a feedback loopPatients choosing to go to Walk-in Centre +
+
Long waiting times in A&E
Self-referrals to A&E
Additional resources placed in A&E to provide better service
+
+
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Creating demand? - a feedback loopPatients choosing to go to Walk-in Centre +
+
Long waiting times in A&E
Self-referrals to A&E
Additional resources placed in A&E to provide better service
+
++
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• 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!
Implementation
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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
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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
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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