Review of the Cardiff Masters EU Projects with Aneurin Bevan University Health Board

1. Review of the Cardiff Masters EU Projects with Aneurin Bevan University Health Board Danny Antebi, Paul Harper, Julie Vile & Janet Williams Masters students: Elizabeth Allkins, Yiwen Fu

2. Overview • Background on links between Cardiff University & ABUHB • Brief overview of the two Unscheduled Care Masters Projects • Project feedback to the Health Board • Outcomes and sustainability of the work • Questions

3. ABUHB – Aneurin Bevan University Health Board • 14 hospitals in total, 2 major A&E • Serves an estimated population of over 639,000, approximately 21% of the total population of Wales • Employing over 13,000 staff, two thirds of which are involved in direct patient care

4. ABCi – Aneurin Bevan Continuous Improvement

5. The Modelling Unit • John Frankish – Service Lead/Improvement Coach • Dr Tracey England – Mathematical Modeller • Dr Penny Holborn - Mathematical Modeller • Dr Izabela Komenda – Mathematical Modeller • Dr Julie Vile - Mathematical Modeller • Terry Watkins – Improvement Coach • Mr Hiro Tanaka – Clinical Liaison/Support • Steve Elliott – Financial Support

6. Functions Range from informal advice to analytical support Variety of mathematical techniques permit best option approach for any project: • Simulation • Statistics • Forecasting • Demand and capacity planning • Optimisation • Scheduling • Queueing theory • Training sessions

7. Focus - Current Projects • Unscheduled care: • Modelling demand and capacity for OoH services • Improving the accuracy of predictions for RGH ED • Aligning staffing profiles to peaks and troughs in demands for RGH ED • Improving patient flow • Evaluating the effect of individuals presenting in ED under the influence of alcohol • Mental health: • CHMTs skill mix and staffing levels • Developing caseload management tool • Analysis of CHC packages over a 5 year period • Pathology: The effects of changing the shift patterns in the laboratory at the RGH • Primary care: Evaluating the introduction of a Patient Access system for GPs scheduled care • Scheduled care: Development of a fracture neck of femur database tool • Informatics: Digitalisation of Health Records

8. Masters Projects • Short-term projects (3 months) Outputs: • Presentation • Executive summary • 20,000 word dissertation!

9. Unscheduled Care PROCESSES ARE TOO SLOW IN HOSPITAL TOO MUCH DEMAND LACK OF CAPACITY TO TAKE PATIENTS OUT OF SYSTEM • Alternative pathway for elderly/ frail patients • Co-locate MIU • Better computational facilities • Admission avoidance strategies • Better community model • Role of WAST • Consultant at front end • Discharge patients earlier • Bring in elective patients later • 24/7 working • Patient boarding

10. Modelling patient flow in ED to better understand demand management strategies. Elizabeth Allkins Sponsor Supervisor – Danny Antebi University Supervisors – Dr Julie Vile and Dr Janet Williams

11. Aims • Gain insight into the functioning of the Emergency Department in the Royal Gwent Hospital • Explore the effect on the system of actions to redistribute demand, reduce overcrowding and long waiting times

12. Background

13. 4 hr Breaches & Death Rate

14. Two complementary approaches • Reduce attendance at ED • Improve flow through ED

15. Stats analysis: Length of Stay

16. DES Simulation model Input Parameters – easily changed via spreadsheet Resources • Staff • Nurses • Doctors • Call handler • Receptionist • Beds • Beds for Majors and wards • Rooms for Minors • Xray machine Validation and Verification processes completed

17. Final model

18. What-if scenarios • CDU • Use as a ‘fast-track’ stream • Use as an additional ward with 12 extra beds.

19. What-if scenarios • WAST pre-hospital streaming • Streaming ambulance patients direct to the MAU • GP trial • Streaming GP referrals to a bed in the MAU • Reduction in WAST conveyance rates • Reduction of 10% reduced waiting times

20. Conclusions • Detailed analysis of ED data • Cost saving of CDU • Reduction in Majors LOS • Simulation • Demonstrated the power of modelling • Explored scenarios to improve waiting times • Built a solid foundation for future research

21. Assessing the impact of an ageingpopulation and the effects of frailty programmes on RGH Yiwen Fu Sponsor Supervisor – Danny Antebi University Supervisors – Dr Julie Vile & Prof. Paul Harper

22. Aim and background • Aim • Evaluation: Impact of aging population on RGH • Evaluation: Indirect effectiveness of Newport frailty team on ED • Simulation models and scenarios: Estimate impact on patient flow in ED resulting from realisation of operational LoS and attendance targets • Background • Pressure from ageing population in ED • Newport frailty team: Established at 11th April 2011; Further developed at 1st Sept 2011 • Main patients source: GP and secondary care

23. Attendance

24. Arrival modes • 2/3 of patients 65+ arrive by ambulance • 2/3 of those under 65 come in a private vehicle Ambulance 19% Ambulance 65%

25. Newport population projection

26. The Frailty programme • Joint service provision across ABHB and the 5 local authorities • Set up in April 2011 • Provide intermediate care services • Keep people happily independent in their own home • Services within Community setting • Services within the hospital • Aims • Reduce bed days • Avoid ED and MAU admissions • Reduce need for Continuing Health Care Packages

27. Impact on ED/MAU attendance ratios • The baseline population is increasing and ageing. • Significant reductions in number of 65+ attending MAU (1st contact): • 1,995 (2011/2012) -> 1,884 (2012/2013). • Number of patients under 65 remained relatively stable over same period. • Early days – some natural variability in system, numbers increased in year frailty team was initiated. Need to re-evaluate in the future! • Lack of evidence to support a reduction in ED attendances: • Numbers and ratios fell for ALL patients except those aged 65-74 between the ‘during’ and ‘after’ period. • Possibly some evidence of a small impact - between 2012-2013 the ratios of patients aged 75+ fell at a more dramatic rate than those under 65. • Need to re-evaluate in the future! • Marginal (but not significant) reductions in the assessed-in rate

28. Newport LoS analysis

29. Simulation model

30. Results of simulation • Scenarios results are compared with baseline results • Relative large impacts on Major unit • Scenario 1 • Reduction on service time (65+ only) • Reduction rate is linearly related to performances • Gradually improve all performances • Scenario 2 • Reduction on attendances (65+ only) • Reduction rate is curvedly related to performances • Mainly improve queuing size and queuing time

31. Simulation results

32. Conclusions • Small cohort of population but uses a non-equivalently large proportion of resources • Impact of Newport frailty team – need to evaluate in future • Better care management would benefit hospital and patients • Introduce a frailty consultant to ED? • Further research • Impact of Newport frailty team on MAU/quality of life • Apply same techniques to other Local Authorities • Need IT software to capture the relevant data • More detailed simulation model

33. Feedback to Heath Board Aneurin Bevan Continuous Improvement and Cardiff University Event Developing Mathematical Models in Healthcare Wednesday 11 September 2013 Malpas Court, Whittle Drive, Malpas, Newport, NP20 6NS

34. Programme

35. Outcomes and sustainability • Extract from email to students from Dr Julie Vile: “The buzz you created at the event was something rarely seen at the NHS and I've hardly been able to get any work done this morning, due to the large number of people coming in the office praising your work which has really helped to raise the profile of the Modelling Unit within our health board.”

36. Outcomes and sustainability • Quote from Dr Danny Antebi Director of ABCi (Aneurin Bevan Continuous Improvement): “Having seen some of the projects the mathematicians have been working on, senior managers in Aneurin Bevan University Health Board are becoming more and more convinced of the value of this approach. In my view we can’t manage increasingly complex systems, be they in health or otherwise, without modelling as an integral part of our design and analysis.”

37. Outcomes and sustainability Modelling patient flow in ED to better understand demand management strategies – Elizabeth Allkins • Built a solid foundation for future research and the model is to be used to explore future scenarios Assessing the impact of an ageing population and the effects of frailty programmes on RGH – Yiwen Fu • Recommended that a frailty team be placed within ED and a pilot is now underway

38. Questions? • www.cf.ac.uk/maths/research/researchgroups/ opresearch/healthcare • http://www.wales.nhs.uk/sitesplus/866/page/69767 • Follow us on twitter @abciab • Email: Julie.Vile@wales.nhs.uk

39. Other Masters Projects Compliance with National Guidelines for stroke in radiology – Hannah Williams • The model identified a significant increase in compliance with revised guidelines and proposed changes to the initial plan for extended working hours Modelling the provision of phototherapy services for dermatology clinics – Harriet Jones • The tool is to be further explored and recommendations on the location of future psoriasis centres are to be considered in South Wales Plan Simulating the automated clinical biochemistry track system at RGH – Bradley Hardy • Identified reductions in cost and staff workload by removing/replacing analysers which are being considered for implementation