Case study 8

Modelling and simulation saves lives

Predicting outcomes in London hospitals

Healthcare systems are typically characterised by complexity, variability, uncertainty and use of scarce resources.

The modelling of such systems requires the development of mathematical and simulation models that are able to account for these conditions, along with complex and large-scale patient pathways, at both operational and strategic levels.

Hospitals have traditionally measured demand by the length of waiting lists or numbers of referrals, but this does not provide health professionals with specific information about patients’ treatment needs and resource requirements. However, the application of mathematical models has recently enabled the modelling of real life scenarios, such as complex and unpredictable patient flows. Managers are now able to change their processes on screen before they do it for real, avoiding unnecessary risk to patients.[1]

Models were used in the design of a major new trauma centre at St George’s Hospital in London. The models informed the required patient-flows and resourcing levels for the centre and were used to create service level agreements with commissioners. This enabled patient waiting time to be reduced and better levels of patient care to be delivered, leading to an observed increase in survival rates.

Models were similarly utilised by a new hyper-acute stroke unit covering the South London area.

The outcomes were again lifesaving, significantly lowering risk-adjusted mortality for stroke patients, as verified by data comparing mortality rates pre and post the introduction of the new unit.

Success factors

The successful application of the models may be attributed to:

working with stakeholders to derive realistic system models

taking a system-wide view of patient flow and resource needs

creating virtual design alternatives to explore process options

evaluating new process risks utilising simulation models

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Notes

[1] Mathematics and Healthcare: Saving Lives and Reducing Costs, 2013. impact.ref.ac.uk/CaseStudies/ CaseStudy.aspx?Id=3387


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