NHS system will help clinicians to predict patients who are likely to need hospital admission

The NHS has launched the Combined Predictive Model, a system that will identify patients with long-term conditions who are most at risk of unplanned and unnecessary hospital admissions.

Rosie Winterton
Rosie Winterton

The system uses accident and emergency, inpatient, outpatient and GP data sources to identify future frequent users of hospital services beforehand, allowing for earlier intervention. With the right intervention targeted at the right people, the deterioration of a patient's condition could be prevented or slowed down.

In addition, as it uses a combination of primary and secondary care data sources, the model can also categorise people with long term conditions according to their risk of hospital admission, enabling organisations to implement different interventions and care pathways to meet these needs.

The Combined Model will assist community matrons and other case managers, who are responsible for planning and co-ordinating patient care and ensuring a joined up approach from integrated health and social care teams.

Health Minister Rosie Winterton said: ‘Our population is getting older and more of us are living with an illness or condition which means huge increases in demand on health and social care services.

‘Many PCTs are adopting case management approaches as a means of ensuring the most vulnerable people receive fully joined up health and social care and have person centred care planning.’

The Combined Predictive Model is available online.

Have you registered with us yet?

Register now to enjoy more articles and free email bulletins

Register

Already registered?

Sign in

Before commenting please read our rules for commenting on articles.

If you see a comment you find offensive, you can flag it as inappropriate. In the top right-hand corner of an individual comment, you will see 'flag as inappropriate'. Clicking this prompts us to review the comment. For further information see our rules for commenting on articles.

comments powered by Disqus