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

2

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

3

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

4

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

5

Nottingham Health Authority

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Queens Medical Centre, Nottingham

7

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

8

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

11

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

13

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

15

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

21

–

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

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

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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

36

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

38

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

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

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

44

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