Developing Policy: Simulating a Year of Care for People with Long Term Conditions

1. Developing Policy: Simulating a Year of Care for People with Long Term Conditions Claire Cordeaux Executive Director, Healthcare

2. We know the problem… • 15m people with Long Term Conditions • Increasing each year with ageing population • Responsible for 70% of NHS costs • Significant cause of ED attendance and urgent admission

3. Driving Policy through Funding Instruments • A year of care capitation fund for a person living with multiple conditions • Incentivizing providers and commissioners to work effectively together • Aligning funding flows and patient need for support • Improving outcomes and efficiency • Reducing emergency care activity

4. The underlying research base Scottish School of Primary Care • More people have 2 or more long term conditions

5. Long Term Conditions • Only 18% of patients with COPD have just COPD • Only 14% of patients with diabetes have just diabetes • Only 5% of patients with dementia have just dementia Scottish School of Primary Care

6. Silo treatment vs. whole person Sir John Oldham, DH

7. What if? • We plan care for people rather than disease? • Are there common patterns of service use? • Can we differentiate groups of patients by need and costs to create an annual tariff? • Can we work within that tariff to reduce emergencies and manage care out of hospital?

8. Where does simulation help? • Modelling uncertainty • Testing assumptions and their consistency when no historic data • Considering variability • Driving thinking • Sharing models

9. Our task • Create a simulation model • 7 pilot sites • 1 national model to be used locally Looking for common parameters

10. Starting to simulate a new approach Exacerbation

11. Planned Urgent Pathways Population DemographyPrevalence Prevalence/ Influencing factors Service points, flows& waits Demographic weighting Whole systemmodel Referral patterns Capacity Duration Population Maternity Constrained resources Simulationresults Mental Health Servicemodels Scenarios Social Care Using Scenario Generator Urgent

12. Patient identification and assessment

13. Services by level of need

14. Validation 3 sites to date

15. Some findings

16. But…… • No real correlation between risk score and level of need

17. And……….. A crisis curve? Dr. Abraham George

18. Meanwhile End of Life – modelling death Highest number of deaths = organ failure (32%) Same group of patients with long term conditions

19. So where are we now? .. Year 2 • A new model • Predict demand by • risk score • numbers of long term conditions

20. So where are we now ctd? .. Year 2 • Consistent definitions of services being accessed • Able to calculate probability of access to services by long term condition groups Death % % % % % New LTC Group %

21. So where are we now ctd? .. Year 2 • Include changing state of patients • Numbers of long term conditions • Low to very high

22. What the simulation does… • Informs question development and data collection • Allows experimentation and hypothesis testing where no historic data available • Enables research evidence to be applied to policy and practice development • Shares national assumptions meaningfully at local level • Reduces risks in policy development by generating evidence for decisions

23. Questions? Claire.c@simul8.com Executive Director, Healthcare, SIMUL8 Corporation