1. Modelling health systems: How health data and simulation can help inform the redesign of our NHS services
Collaboration for Leadership in Applied Health Research and Care (Wessex)

2. Health Systems Analytics
Collaboration for Leadership in Applied Health Research and Care (Wessex)

3. Health Systems Analytics
Visualise and diagnose the problem
Upfront testing of a change to a process or pathway
Strategic reconfiguration of health services Vs
Analytics encourages systemic solutions to systemic problems

4. Applied Analytics
Being realistic about the impact of hospital reconfiguration intervention on ED overcrowding

5. Reorganising a medical pathway

6. A simplified acute medical pathway
Wards grouped by
Medicine for old people (MOP)
Cardiology
Gastro
General Medicine
Respiratory
Might also happen
if the system is
under pressure

7. ‘What if’ – stream by pLoS
We can push cases with a pLoS < 72 hours to a short stay ward
We can vary the number of beds in short stay and specialities
We vary the accuracy/error rate
We can change the case mix in wards
Adjust the % of cases that move between speciality wards

8. What is the model measuring?
Waiting times.
Average ED trolley waiting time
Average transfer delay from AMU
Usage of space.
Average no. patients on a trolley
Average no. of medical bed shortages
Occupancy of wards

9. Using routine data for analytics
Our simulated patient population
Emergency department
The variation in emergency demand that we expect hourly, daily, monthly, annually etc along with mode of arrival to hospital (e.g. unscheduled versus GP referred). +
AEC
The variation in ambulatory emergency demand that we expect and likelihood of admission to hospital.
PAS
The profile of a patients journey’s through a hospital. For example, transfers between wards
Our simulated hospital

10. Results (60 bedded short stay)
It was estimated that the hospital needed to achieve a 65% rate to reduce ED overcrowding
We don’t know in advance how good the hospital will be at streaming patients so we do what is called a sensitivity analysis.

11. Conclusions Usage of the short stay ward must be tightly controlled.
Outcomes are very sensitive to the accuracy of short stay streaming
Predictions of LoS needs to be >=70% accurate otherwise benefits will not be realised.
Monitor and control
Monitor prediction accuracy within the short stay ward
Monitor the impact on outliers and between ward transfers
Monitor the impact on elective cancellation rates

12. NIHR CLAHRC Wessex in partnership with
University Hospital Southampton NHS Foundation Trust
Portsmouth Hospitals NHS Trust
Hampshire Hospitals NHS Foundation Trust
The Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust
Poole Hospital NHS Foundation Trust
Isle of Wight NHS Trust
Dorset County Hospital NHS Foundation Trust
Salisbury NHS Foundation Trust
Solent NHS Trust
Southern Health NHS Foundation Trust
NHS England South Wessex area team
NHS Dorset CCG
NHS West Hampshire CCG
NHS Southampton City CCG
NHS Portsmouth CCG
NHS North East Hampshire and Farnham CCG
NHS North Hampshire CCG
NHS South Eastern Hampshire CCG
NHS Fareham and Gosport CCG
NHS Isle of Wight CCG
Health Education Wessex

13. Extra Slides
Collaboration for Leadership in Applied Health Research and Care (Wessex)

14. Visualise and diagnose
Identify key areas to target interventions
Achieve a shared vision of the problem
Agree actions and a way forward

15. Upfront testing of change
Ask ‘what if’ before you make any risky changes
Predict the economic, quality and patient impacts
Test out the wider system impact of disinvestment decisions
Identify unintended knock-on consequences of change

16. Strategic reconfiguration
Where should services be located to equitably meet patient need?
How many locations should you commission?
How much capacity should each site have?