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Hospital of the Future: Integrated Patient Management in the Digital Hospital

1st October 2009 to 31st March 2013
Preventable deaths occur regularly in hospitals as a result of the failure to identify patient deterioration early enough. In August 2007, the National Patient Safety Agency (NPSA) issued a report on in-hospital deaths identifying the two most important actions which could be taken to improve patient safety: identify patients who are deteriorating and act early; improve systems to resuscitate patients when they have cardiac arrests (a training and skills development issue). 
There is increasing evidence that early detection of patient deterioration, followed by appropriate action, not only reduces preventable deaths but also reduces the numbers of cardiac arrests or unplanned admissions to the Intensive Care Unit (ICU). Reports in the literature suggest that failure to respond to patient deterioration promptly and appropriately leads to increased morbidity and mortality, increased requirement for intensive care and elevated costs. The growing evidence has led the National Institute for Health and Clinical Excellence (NICE) to issue a clinical guideline in July 2007 for the management of acutely ill patients in hospital, recommending that physiological track and trigger systems should be used to monitor all adult patients in acute hospital settings.
The likelihood of a successful outcome following patient deterioration increases if the deterioration is detected early, recognised as important, communicated to appropriate team members and care rapidly escalated. Each of the four links in this "chain of action" is characterised by variation and therefore from time to time will be prone to failure. A central objective of our Grand Challenge is to reduce the probability of failure in each of these links to an absolute minimum.
Our research over the last decade has shown that the integration of continuously-monitored parameters using data fusion can provide early warning of adverse events. The data fusion systems are based on a probabilistic model of normality previously learnt from datasets of vital signs acquired from representative groups of high-risk patients. The system alerts the nursing staff whenever the combination of vital sign parameters is indicative of physiological abnormality.
The overall aim of the Grand Challenge is to develop a novel, flexible and integrated model of in-hospital patient care, which brings together at the point of care disparate sources of information over multiple time scales. High-quality, real-time vital sign data fused with information such as the medication record (today) or genomic data (in the long term) will allow the accurate tracking of a patient's recovery after, for example, hip surgery, stroke or myocardial infarction. This will improve patient safety, reduce unplanned admissions to higher levels of care and deliver care more efficiently, and will be achieved by integrating data fusion techniques with new features of the digital hospital - the Electronic Health Record (EHR) and full connectivity using wireless broadband. It will allow resources to be targeted at the identification of early deterioration and the prevention of irreversible decline in physiological status. This will deliver not only improved patient outcomes (reduction in preventable deaths, cardiac arrests and unplanned admission to intensive care) but also essential economic benefits: reduced length of stay and fewer unplanned admissions to (more expensive) higher levels of care.
The new, information-driven, integrated model of in-hospital patient care could be deployed throughout the NHS within the next 5 to 10 years. It is planned that the ICT solutions at the core of the research programme will be developed and validated in two hospitals (John Radcliffe Hospital in Oxford and Guy's & St Thomas' in London) within a 3-year timescale.


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