Development of a hand-held real-time decision support aid for critical care nursing

In the current health care environment, nurse clinicians must work "faster and smarter" making complex decisions on almost a continual basis. Evidence-based knowledge and standardized guides, such as clinical algorithms, can support clinical nursing decisions, however; effective real-time access is limited. This paper outlines research addressing this problem. In this research, current clinical knowledge is delivered to the clinician via an off-the-shelf handheld computer using wireless access to a central server and data repository. Innovative minimal-set database, data mining and knowledge discovery algorithms using a combination of case based and rule based learning with added confidence measures permitting bi-directional (forward and backwards) inferencing based on individual client data are developed and presented for the hand held device. The technology provides real-time decision support for the multiple cases and sequential decisions characterizing present critical care nursing practice. Nurses will be able to consider a full range of alternative explanations, determine additional data needs, find, isolate and examine patient case outliers for additional diagnostic data or verify the appropriateness of a selected strategy. Once fully developed the system will have the capacity to maintain a history of a series of decisions and outcomes thereby over time improving the case base and rule bases used for decision support. Outcomes of the real time decision support aid include more timely health care, less biased decisions, and improved patient outcomes.