It is now well established that many patients in hospital can suddenly become acutely ill but experience delayed recognition of their physiological deterioration resulting in late referral to critical care, or in some cases death. In recent years there has been significant growth in the use of scorecards to assist with the detection of increasing patient morbidity, but even though a scorecard may be well-constructed and its parameters carefully chosen, the usefulness of any scorecard is only as good as the accuracy and timeliness of the data that is used to populate it.

The scorecard in this chapter referrers to the Modified Early Warning Scorecard (MEWS) which is a paper-based clinical scorecard, intended to provide clinicians with an early warning of acute patient deterioration. While this paper based approach is a significant advance in patient care, major data capture and processing deficiencies still exist.

To overcome these limitations an electronic-Modified Early Warning Scorecard (e-MEWS) was designed and developed in collaboration with the staff at St. Luke's General Hospital, Kilkenny, Ireland. The e-MEWS is an intelligent rule-based clinical decision support system designed to automatically perform frequent wireless monitoring of a patient's vital signs, and to record and process the data to calculate and display a MEWS score and other valuable patient information.

This research demonstrates how an existing real-world paper based approach can be greatly enhanced through the application of intelligent Clinical Decision Support Systems (CDSS). In turn the adoption of wireless Body Area Network (BAN) technologies within a clinical environment highlights how Ambient Assisted Living (AAL) solutions can play a significant role in patient care delivery.