how can academic research and modelling add value to nhs decision makers? mr andrew fordyce frcs, dr...
TRANSCRIPT
How can academic research and modelling add value to NHS decision makers?Mr Andrew Fordyce FRCS, Dr Mike D Williams. Dr Mike Allen
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?”
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
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
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
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
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.”
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
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
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?
Clerking Capacity – staffing to meet demand?
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Note: Clerking capacity is estimated based on planned rota of staff assuming an average of 1hr per patient
Weekday Weekend
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.”
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?
Variability in 2012 emergency admissions
15%CV
121086420
70
60
50
40
30
20
10
0
Admissions per day
Frequency
Mean 4.885StDev 2.196N 365
Normal Trauma admissions
45% CV
-60
-40
-20
0
20
40
60
80
Net
cha
nge
in n
umbe
r of p
atien
ts
Net change in hospital occupancy over one year
Common range of change (2 SD) = ±30 patients per day
Medical & surgical patients* – midnight count(*Patients categorised by consultant at discharge)
Un-escalated bed stock = 328 (inc EAU & ICU)
Escalated bed stock = 351
Medical patients – midnight countUn-escalated bed stock including EAU = 208 beds
Escalated bed stock = 236 beds
Model Logic
Placing patient on ward: 1. Preferred ward(s) for specialty2. Escalate preferred ward(s)3. Ward of same division (medical/surgical)4. Escalate ward of same division5. Ward of different division6. Escalate ward of different division7. Overflow Cancel 1 elective procedure for each
midnight overflow patient
Emergency
Elective
EAU
Wards
ICU
Exit
Arrivals, routing and lengths of stay are dependent upon specialty & whether elective or emergency admission.
# Arrivals adjusted by average for weekday,
• Outliers are not repatriated• Overflow patients are repatriated once/day
• Outlier 1 = Non-preferred ward for specialty• Outlier 2 = Ward of different division
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
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
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