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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.

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Presentation on theme: "Using System Dynamics in practice: a case study from emergency health services Sally Brailsford 1, Valerie Lattimer 2, PanayiotisTarnaras 1 and Joanne."— Presentation transcript:

1 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 UBC Centre for Health Care Management, 8 Dec 2006

2 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 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 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 5 Nottingham Health Authority

6 6 Queens Medical Centre, Nottingham

7 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 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

9 9 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

10 10 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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16 16 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

17 17 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 –

18 18 Feedback loop Student numbers Staff stress levels + Research papers published – Student recruitment Reputation of university + + +

19 19 A balancing loop Student numbers Staff stress levels + Research papers published – Student recruitment Reputation of university + + + –

20 20 Behaviour over time time Number of students

21 21 – Waiting lists Hospital beds available GP referral rate – – A balancing loop –

22 22 A vicious circle + – Waiting lists Hospital beds available GP referral rate – – + Extra Govt money + Patient demand + +

23 23 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

24 24 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)

25 25 Quantitative models: stocks and flows Rates (valves): control flow Levels (stocks)

26 26 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

27 27 Stella software

28 28 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

29 29 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

30 30 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

31 31 Patient flow map

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33 33 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

34 34 Model validation – baseline run

35 35 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 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

37 37 1Increased admissions: a) 4% growth in emergency admissions b) 3% growth in elective admissions 2Changing “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 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

39 39 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

40 40 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

41 AttributeLevelLevel description Contacting the service 1By telephone, 2 or more calls 2By telephone, 1 call 3In person Where advice / treatment takes place 1Travel 15 miles 2Travel 5 miles 3At home, no travel Time waiting for advice / treatment after initial contact 14 hrs 30 minutes 22 hrs 30 minutes 330 minutes Whether kept informed of expected waiting time 1No information 2Some information 3Full information Who advices / treats 1Paramedic 2Specialist nurse 3Doctor Quality of contact time 1Not enough time to deal with problem, interruptions 2Enough time to deal with problem, interruptions 3Enough time to deal with problem, no interruptions Attributes to be compared

42 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 AService B Making contact Single telephone callIn person Where advisedAt home, no travellingNearest NHS facility 15 miles Waiting time between initial contact and advice 2½ hours4½ hours Informed of expected wait No information Who advicesSpecialist nurseDoctor Quality of contactEnough time, no interruptions Not enough time, interruptions Tick one box only

43 43 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 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?

45 45 Creating demand? - a feedback loop Patients 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 + +

46 46 Creating demand? - a feedback loop Patients 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 + + +

47 47 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

48 48 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

49 49 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

50 50 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

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