Modelling Emergency and Unscheduled Care in Nottingham

Sally BrailsfordProfessor of Management Science

Cumberland Initiative Launch, May 2013

Background• Project undertaken in 2001-02, commissioned by Nottingham

City PCT• Constantly increasing pressure on system: spiralling demand,

rising emergency hospital admissions, cancelled elective operations, long A&E waits … a permanent “winter crisis”

• Steering Group set up to develop Local Services Framework for unscheduled care

• Membership from all providers: hospitals, ambulance service, in-hours and OOH primary care, NHS Direct, Walk-in Centre, social services, community mental health, etc ….

• Team from University of Southampton commissioned to provide research support, led by Professor Val Lattimer (now at UEA)

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3

Number of patient contacts per 1000 population/month with front door services in Nottingham (April 1998-March 2001)

0

5

10

15

20

25

Apr-98

Jun-9

8

Aug-98

Oct-98

Dec-98

Feb-99

Apr-99

Jun-9

9

Aug-99

Oct-99

Dec-99

Feb-00

Apr-00

Jun-0

0

Aug-00

Oct-00

Dec-00

Feb-01

Date

Cont

acts

(per

100

0/m

onth

)

NHS Direct

A&E

NEMS

999 callsWalk-in centre

4

5

Research streams• Literature review and comparison with other

Health Authorities• Stakeholder interviews and activity data collection• Descriptive study of patient pathways • Patient survey and preference study (discrete

choice experiment)• System dynamics modelling

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System Dynamics• Powerful simulation methodology with qualitative

and quantitative aspects• Developed at MIT in the 1960’s by Jay Forrester• Fundamental principle is that system structure

determines behaviour: i.e. the way that the individual components of any system relate to and affect each other determines the emergent behaviour over time of the system as a whole

• The emergent behaviour may be counterintuitive• Feedback is an important feature

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Qualitative aspects• Diagramming approach, whose aims are:

– to create and examine feedback loop structure, to identify balancing loops and vicious circles

– to provide a qualitative assessment of the relationships between system elements, information, organisational boundaries and strategies

– to analyse and understand system behaviour and to postulate design changes to improve behaviour

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ManchesterUnited league

position

Money to buyplayers

+ Manchester Cityfans happiness

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Quantitative aspects• Numerical approach, whose aims are:

– To examine the quantitative behaviour of system variables over time

– To design alternative system structure and control strategies

– To optimise the behaviour of specific system variables

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Water inbathtubWater tank Drainage

systeminflow through

taps

Outflow throughplughole

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Occupancy ofhospital beds

Referral rates Waiting lists

+

+

-

Balancing loops … and vicious circles

ManchesterUnited league

position

Ticket prices

Money to buyplayers+

+

+

11

Stocks and flowsWater inbathtubWater tank Drainage

systeminflow through

taps

Outflow throughplughole

Watertreatment

center

Sewagetreatment center

flow to house

Natural wastage

Reservoir

Rainfall

flow aftertreatment

outflow

Flow into sewage

Back to GP referral rates

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Occupancy ofhospital beds

Referral rates Waiting lists

+

+

-

The effects of political pressure

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Occupancy ofhospital beds

Referral rates Waiting lists

+

+

-

Money for extrabeds Political pressure

+

+

+

-

Unintended consequences

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Occupancy ofhospital beds

Referral rates Waiting lists

+

+

-

Money for extrabeds Political pressure

+

+

+

-

Modelling phases• Qualitative: stakeholder interviews and

development of conceptual map• Quantitative: implement map in Stella software• Populate model with 2000 – 01 data • Investigation of (24) different scenarios• Model used to explore different “futures” – new

ideas for tackling the problem, tested interactively in discussion with the Steering Group

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Elective 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

systemMap version 2: for modelling

OP clinics: direct to wards

(QMC and City)

Conceptual map:Patient flows through the

system

Slide 17Comparison of SD and DES

Using system dynamics• Doesn’t model individual patients• Doesn’t capture variability and uncertainty• Doesn’t tempt you to make the model too

complicated!• …… BUT …..• Does run very quickly• Does capture dynamic feedback effects and take a

“whole system” perspective• Can include qualitative or subjective variables

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1 Five Year outlook: assuming a) 4% growth in emergency admissions b) 3% growth in elective admissions

2 Changing “front door” demand3 Reducing emergency admissions – for

specific groups of patients 4 Early discharge5 Beds crisis & ward closures6 Streaming in the Emergency

Department

Scenarios

Headline findings• If a 4% annual increase in emergency admissions

does continue, both Acute Trusts will experience severe difficulties very soon

• Could lead to 400 cancelled elective admissions per month after 5 years if no extra resources

• GP referrals are a key factor• Preventing admission of older patients had biggest

effect• Increased use of Walk-in Centre was effective in

reducing A&E workload20

The problem of modelling the ED• National targets for 4-hour waits in the ED were

being regularly breached• Different targets for different triage categories,

depending on severity, although all patients had to meet 4-hour target

• The hospital wanted to investigate “streaming”: i.e. setting up a separate minor injury stream with dedicated staff

• Timescale of minutes, not days (let alone weeks)• Needed to develop simple Simul8 model

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Findings of the ED model• Streaming scenario showed improvements in waiting times:

especially for minor cases• Seemingly counter-intuitive findings possible because of

trade-offs between categories• Small increase in waits for medium severity patients – almost

certainly avoidable in reality• Need to use staff flexibly and responsively, driven by demand• Could have used model to develop rules for deciding when to

switch to Minors stream

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Key messages for the client

• High impact across the system of relatively small changes in one part

• GP referrals a key factor • Alternatives to admission are more effective than

discharge management in reducing occupancy• Focus on keeping less severe patients away from

the ED• Need for better outpatient services for diagnostics

and treatment23

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

• Independent Sector Treatment Centre opened in 2008

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Reflections: success factors

• Impetus came from the client – problem driven• Charismatic and enthusiastic local sponsor• Remarkable goodwill and spirit of cooperation among

Steering Group• Local politics - data collection given high priority• National politics – the right model at the right time• Simplicity and interactive nature of model • Funding to develop model and implement recommendations!

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Outcomes

• Model provided a safe “sandpit” to explore different ideas round the table: made possible by very fast run times and “buy-in” from participants

• The actual numbers were not the real issue (a key point!) - the relative impact of different changes were what mattered, and insights into the knock-on effects of decisions

• Fed into local policy framework and eventual decision to build an Independent Sector Treatment Centre at Queens Medical Centre

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Thank you for your attention

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S.C. Brailsford, V.A. Lattimer, P. Tarnaras and J.A. Turnbull (2004), Emergency and On-Demand Health Care: Modelling a Large Complex System, Journal of the Operational

Research Society, 55, 34-42.

V.A. Lattimer, S.C. Brailsford et al (2004), Reviewing emergency care systems I: insights from system dynamics modelling. Emergency Medicine Journal, 21, 685 – 691.