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Mathematical modelling should be used in healthcare

25th May 2010

Writing in HSJ, Daloni Carlisle says mathematical modelling can help predict risk of hospital admission in a given population.

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Predictive risk modelling uses mathematical models to make predictions about events such as which people in a given population are the most likely to be admitted to hospital in the next 12 months.

It has come to the fore in recent years, particularly with the NHS challenged with reducing admissions by 5% by 2008.

Croydon public health doctor Geraint Lewis reasoned that use of routine data may help define patients most likely to require hospital admission within a year and then target nursing interventions to avoid that.

It was a fruitful combination of maths and matrons.

The Department of Health commissioned the King’s Fund, New York University and Health Dialog to develop predictive tools.

The Patients at Risk of Readmission case finding tool (PARR) tapped into inpatient data to predict the likelihood of readmission.

In 2006, the group developed the combined predictive model, which uses data from inpatient episodes, outpatients, accident and emergency and general practice to predict which people in a population are most likely to need emergency admission to hospital the following year.

Now, predictive modelling has spread across the UK with Bupa estimating 80% of PCTs use predictive modelling such as helping GPs and PCTs manage lists and the development of ‘virtual wards’ to manage patients.

However, while the concept is proving popular – and is being extended into other areas - the hard evidence for them reducing hospitalisation rates remains scant.

 

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