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Modelling and planning care services for long-term conditions

Southern Institute for Health Informatics 2006 Conference 22nd September 2006 Steffen Bayer. Modelling and planning care services for long-term conditions. Long-term conditions as an increasing concern. Growing long-term care needs aging population

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Modelling and planning care services for long-term conditions

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  1. Southern Institute for Health Informatics 2006 Conference 22nd September 2006 Steffen Bayer Modelling and planning care services for long-term conditions

  2. Long-term conditions as an increasing concern • Growing long-term care needs • aging population • improved survival rates for chronic conditions • Large demand for care services for chronic diseases • 17.5m adults in the UK may be living with a chronic disease • Around 80% of GP admissions relate to chronic disease • Patients with a chronic disease or complications use over 60% of hospital beds • Evidence from the US suggests people with chronic conditions consume 78% of all health spending. • Pressures on health and social care system • staff shortages • funding constraints

  3. Planning of care services: the challenge of evidence-based decision-making • Drive towards evidence-based decision making in medicine, policy and management • Clinical trials happen in isolation and often under special circumstances • Randomised control trials for service innovation particularly difficult due to complexity and interconnectedness; often inconsistent findings • Real-life decision making requires tradeoffs • between different chronic diseases • between treatment and prevention (and screening) • between cost (for whom?), quality of life, longevity, etc.

  4. Uncertainty and system behaviour Future care services New policies Whole System Effects New technologies Unintended consequences Changing needs

  5. Models can be useful - all models are wrong • Models simplify: The map is not the territory. • But sometimes the slightly wrong answer is good enough. • Models help to think.

  6. Variety of modelling approaches • Discrete event simulation – operational details • System dynamics • strategic, aggregate level • interrelationships, feedback • whole systems thinking…

  7. Modelling in action: System Dynamics

  8. Flow: water flowing in [litre per minute] Stock: water in bath tub [litre] Fundamental building blocks of systems: stocks and flows Stocks and flows are • as simple as a bath. • everywhere – from bank accounts to hospitals.

  9. Stock and flow comparison

  10. Bath tube dynamics – simple and fundamental Stock accumulation is as simple as filling (and emptying) a bath. The only way to change the stock is via the inflows and outflows.

  11. Care delivery with telecare

  12. Demand for institutional care

  13. Simulation modelling to investigate treatment and prevention options for chronic illness (heart failure)

  14. Hospital demand: hospital bed days

  15. Number of symptomatic patients

  16. Modelling process • Modelling invites us to question assumptions: • What are the boundaries of our system? • What do we really need to know to make decisions? • Modelling can help to uncover information requirements • Modelling can facilitate a dialogue between stakeholders • Modelling allows cheap and simple experimentation with different choices

  17. Conclusions • Trials alone provide only a limited basis for decision-making • Modelling can highlight • Trade-offs • Investment and implementation processes • Time scales of effects to materialise • Importance of context • Existence of alternative interventions and of prevention and screening • Modelling might be valuable – even if it can’t necessarily provide all the answers

  18. Thank you. Contact: Steffen Bayer Tanaka Business School Imperial College London s.bayer@imperial.ac.uk

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