How can academic research and modelling add value to NHS decision makers?

1. How can academic research and modelling add value to NHS decision makers? Mr Andrew Fordyce FRCS, Dr Mike D Williams. Dr Mike Allen

2. How the partnership story began • •24/7 system reliability • •Built academic – clinical partnership • •Need to save money, business question “if we make changes aimed to reduce LoS, can we close some beds?”

3. Setting the context • “People working in healthcare increasingly have to do more with less. ...working under conditions they would rather avoid in which the safety margin for those they are caring for has been greatly diminished.” • Runciman B, Merry A, Walton M., 2007 Safety and Ethics in Healthcare, Ashgate, Aldershot. • Decision makers need assistance in making hard choices in the face of many competing demands

4. Research approach and methods • Taking a systems thinking perspective – complex socio-technical system •Qualitative – interviews and observations in primary care, ambulance trust and acute hospital – patient pathway •Quantitative – analysis of hospital PAS data and creation of discrete event simulation model to assess bed occupancy

5. Key findings for decision makers When looking at the flow of urgent patients we provided evidence as to some of the reasons why there are daily peaks and variation in demand at the hospital and the problems created •GP working practices •Ambulance prioritising 999 • Staffing and productivity of clinical micro systems (clerking) • Impact on wider hospital – discrete event simulation of demand patterns

6. GP working practices • Practices facing high demand – prioritise surgery based appointments – no willingness to change • Batch ‘visits’ (create higher number of referral to hospital) as they are not an ‘efficient’ use of doctor time • No standard method of communication to the hospital • Request ambulance – 8mins (999) of 4 hours

7. 8 minutes or 8 hours to treatment •GPs visit sickest patients 1 - 3pm – then phone for ambulance (HCP calls) •Ambulance prioritise 999 response < 8mins therefore GP call as ‘urgent’ <4 hrs •Patient arrives at hospital late afternoon / evening •Patient’s need subordinated to local optimisation of parts of system “Visits are a very inefficient use of GP time.” “Achieving the 999 target is our priority.”

8. This area for large pictures/charts/tables,etc with one line captioning. Arrival and discharge patterns by hour of day – change demand pattern or design services to cope

9. Helping managers understand normal variation around the mean Panic – admissions have risen by 7% no – it is 12%, some say 15% Acute emergency admissions have been rising at ~1.6% per annum

10. A question How many emergency medical patients does an F1 doctor process (clerk) in A&E on average during an 8 hour, 9 – 5pm shift?

11. Clerking Capacity – staffing to meet demand? Weekday Weekend This area for pictures/charts/tables,etc Note: Clerking capacity is estimated based on planned rota of staff assuming an average of 1hr per patient

12. Inefficient clinical micro systems “...someone will have taken the notes to reception to be photocopied...” “As an F1, it happens to us all, from nine to five you might see four patients. There is a general feeling that if you can see four full patients from scratch and do everything, that’s not bad for an F1 doctor in an eight hour shift. If you actually looked at the amount of time doing medicine it is probably less than a quarter of the time because of the amount of time, you know, you have to spend running around and chasing up on different issues.” “When you take bloods they get left in a pot in A&E, then a porter circulates maybe once every half an hour or forty minutes, so that is half an hour to forty minutes for your blood test sat there not being examined and then they go to the lab to be looked at.”

13. Modelling bed occupancy – key themes • •Understanding & modelling demand variability at whole hospital and specialty level • Doctors would like bed pools sufficiently large to cope with demand variability for their own specialty • •What are bed requirements given expected changes in system • Increasing emergency admissions (~2% per annum) • Service Improvement Programmes to reduce length of stay • Could bed reductions be achieved based on assumptions being made?

14. Variability in 2012 emergency admissions 15%CV 45% CV

16. Medical & surgical patients* – midnight count(*Patients categorised by consultant at discharge) Un-escalated bed stock = 328 (inc EAU & ICU) Escalated bed stock = 351

17. Medical patients – midnight count Escalated bed stock = 236 beds Un-escalated bed stock including EAU = 208 beds

18. Model Logic Arrivals, routing and lengths of stay are dependent upon specialty & whether elective or emergency admission. # Arrivals adjusted by average for weekday, Placing patient on ward: Preferred ward(s) for specialty Escalate preferred ward(s) Ward of same division (medical/surgical) Escalate ward of same division Ward of different division Escalate ward of different division Overflow  Cancel 1 elective procedure for each midnight overflow patient • Outliers are not repatriated • Overflow patients are repatriated once/day • Outlier 1 = Non-preferred ward for specialty • Outlier 2 = Ward of different division

19. Example scenario This area for large pictures/charts/tables,etc with one line captioning.

20. Model conclusions • •Expected LoS reductions (in SIPS) will not allow for closure of beds • In order to close beds LoS reductions significantly greater than anticipated would be required • •The model was used to explore a range of scenarios, such as • Altering medical/surgical bed balance • Various bed numbers and LoS reduction combinations • Smoothing elective flow over 6-7 days (in place of 5 days) • Differing assumptions on emergency admission growth

21. The Impact: • Not ready for bed closures • Speciality to dependency based model • Testing weekend working • Building a longer term partnership between NHS in South Devon and the University of Exeter Contact us: andrew.fordyce@nhs.net m.allen@exeter.ac.uk; m.d.a.williams@exeter.ac.uk