As part of our work on managing long-term conditions, the King's Fund, together with New York University and Health Dialog, has developed an algorithm that links inpatient data with other routine data on utilisation of care in order to predict future risk of emergency admission. The 'Combined Predictive Model' final report and technical documentation, which is intended for use at primary care trust (PCT) level, is available to download freely from this website.

The Combined Predictive Model integrates accident and emergency, inpatient, outpatient and GP data sources to predict risk of admission to hospital across an entire patient population. It builds on the work undertaken to develop the Patients at Risk of Re-hospitalisation (PARR) models but, because it uses additional primary and secondary care data sources, the combined model is able to identify individuals along the whole continuum of risk as opposed to just those who have already experienced a recent hospital admission.

Additionally, the Combined Predictive Model identifies some patients at very high risk of future admission who are not identified by PARR. It facilitates early identification of people before their conditions deteriorate, which in turn allows differing levels of intervention intensity to be matched to different segments of overall risk. The fact that the Combined Predictive Model is built upon an integrated primary and secondary care data set allows for the development of clinical profiles which provide powerful insights into the types of patients being identified in different risk segments. This facilitates the design of highly targeted interventions proportional to risk.

The model has been developed and tested against datasets from two PCTs. Full results from this process are available in the form of a downloadable report.

• More on the predictive risk project
• More information on the PARR tool
• King's Fund work on managing long-term conditions

• The use of patient information in the long-term conditions programme (45kb)