Using system dynamics in practice a case study from emergency health services
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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

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Using system dynamics in practice a case study from emergency health services

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

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


The city of nottingham

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


Health services in nottingham

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


Using system dynamics in practice a case study from emergency health services

Nottingham Health Authority


Queens medical centre nottingham

Queens Medical Centre, Nottingham


Background to the project

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


Membership of steering group

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


The southampton research team

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


Strands of the research

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


System dynamics

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


Qualitative models influence diagrams

Qualitative models: influence diagrams

+

Student numbers

Staff stress levels

Research papers published

  • Link system constructs (real or abstract)

  • Identify feedback loops

  • Balancing loopshave odd number of “–” signs

  • Reinforcing loops orvicious circleshave even number of “–” signs


Feedback loop

Feedback loop

+

Student numbers

Staff stress levels

+

Research papers published

Student recruitment

+

+

Reputation of university


A balancing loop

A balancing loop

+

Student numbers

Staff stress levels

+

Research papers published

Student recruitment

+

+

Reputation of university


Behaviour over time

Number of students

time

Behaviour over time


A balancing loop1

A balancing loop

Waiting lists

Hospital beds available

GP referral rate


A vicious circle

Waiting lists

Hospital beds available

+

+

GP referral rate

A vicious circle

+

Extra Govt money

+

Patient demand

+


Pros cons of qualitative models

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


Quantitative models

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)


Quantitative models stocks and flows

Levels (stocks)

Rates (valves): control flow

Quantitative models: stocks and flows


The underlying maths

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


Stella software

Stella software


Why system dynamics

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


Modelling phases

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


Stakeholder interviews

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


Using system dynamics in practice a case study from emergency health services

WIC

NEMS

Healthcall

NHSD

Patient pathways through the emergency care – on demand system

Map version 2: for modelling

Arnomedic

GP OOH

GP in-hours

Social Services: EDT, SAO’s, Hospital SW’s

Home care & ongoing casework

EMAS

A & E

DPM

Elective admissions

D55: CCU

Home

D57

OP clinics: direct to wards (QMC and City)

Specialty wards QMC

Further care and intermediate care

Paediatrics

GP adm

D56

Home

Specialty wards City

Assessment unit

Patience wards

CMHT

Further care and intermediate care

Coronary care, Burns & plastics, Stroke unit City

Elective admissions

Dialysis / oncology / COPD patients etc

Patient flow map


Data for the stella model

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


Model validation baseline run

Model validation – baseline run


Using the stella model

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


Experimental scenarios

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


Using system dynamics in practice a case study from emergency health services

Scenario Areas


Trust me i m a computer

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


Main results from stella model

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


Patient preference study

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


Using system dynamics in practice a case study from emergency health services

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 time

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

Attributes to be compared


Using system dynamics in practice a case study from emergency health services

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


Main findings

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


Influence diagrams

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?


Using system dynamics in practice a case study from emergency health services

Patients choosing to go to Walk-in Centre

+

Additional resources placed in A&E to provide better service

+

Long waiting times in A&E

+

+

Self-referrals to A&E

Creating demand? - a feedback loop


Using system dynamics in practice a case study from emergency health services

Patients choosing to go to Walk-in Centre

+

Additional resources placed in A&E to provide better service

+

Long waiting times in A&E

+

+

+

Self-referrals to A&E

Creating demand? - a feedback loop


Implementation

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!


Pros and cons of sd

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


My personal view of using sd

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


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

References


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