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The HAVEN project is funded by the Health Innovation Challenge Fund, a joint venture supported by the Wellcome Trust and the Department of Health.

Late recognition of deteriorating patients in hospitals causes treatment delays that result in increased
mortality and morbidity. Despite widespread introduction of vital sign-based “early warning scores”
deterioration of patients frequently goes unrecognised. Consequently, developing systems for early
recognition of patients at risk of severe reversible deterioration has become a key goal for the NHS.

The HAVEN Project aims to produce a hospital-wide IT system that enables a continuous risk
assessment in all hospital patients, and predicts those at risk of deterioration.

This IT system will use routinely stored electronic data (including demographics, laboratory results and
vital signs recordings) to create this continuous risk assessment. At present, these different types of
data are stored in different local databases, and are not integrated or displayed in a way that supports
decision making or calculation of patient risk. The project will implement a system to gather the
relevant data so that it can be used to gauge risk.

Risk prediction algorithms will then be developed and validated. They will use the records of patients
who were admitted to hospital and then admitted to an intensive care unit (ICU) after two or more
days in hospital. The information about these patients illustrates the pathway from the first signs of
deterioration on the ward to ICU admission.

The algorithms will be used to create a prototype allowing clinicial staff to identify, rank, review and
treat patients who, without acute medical intervention, will deteriorate and require ICU admission.
The prototype will be developed using Human Factors methods to determine the best way to present
the information to support decision making.

This project has potential to increase ICU bed capacity and save money by reducing the number of
patients admitted from the ward because their deterioration was recognised at an early stage.