Modelling Emergency and Unscheduled Care in Nottingham

1. Modelling Emergency and Unscheduled Care in Nottingham Sally Brailsford Professor of Management Science Cumberland Initiative Launch, May 2013

2. 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)

6. 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

7. 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

8. 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

9. 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

10. Balancing loops … and vicious circles

11. Stocks and flows

12. Back to GP referral rates

13. The effects of political pressure

14. Unintended consequences

15. 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

16. 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 Conceptual map: Patient flows through the system

17. Comparison of SD and DES

18. 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

19. Scenarios

20. 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 workload

21. 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

22. 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

23. 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 treatment

24. 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

25. 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!

26. 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

27. Thank you for your attention S.C. Brailsford, V.A. Lattimer, P. Tarnarasand 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.