Clinical decision-support systems for intensive care units using case-based reasoning.
The artificial intelligence approach used in this work focusses on case-based reasoning techniques for the estimation of medical outcomes and resource utilization. The systems were designed with a view to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy. The initial prototype provided information on the closest-matching patient cases to the newest patient admission in an adult intensive care unit (ICU). The system was subsequently re-designed for use in a neonatal ICU. The results of a short clinical pilot evaluation performed in both adult and neonatal units are reported and have led to substantial improvement of the prototype. Future work will include longer-term clinical trials for both adult and neonatal ICUs, once all the software changes have been made to both prototypes in response to the comments of the users made during the preliminary evaluations. To date, the results are very encouraging and physician interest in the potential clinical usefulness of these two systems remains high, and particularly so in the new testing environment in Ottawa.
School of Information Technology and Engineering, University of Ottawa, 161 Louis-Pasteur, Ottawa, Ontario, Canada K1N 6N5. moniquefrize@pigeon.carleton.ca
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D000328:Adult; D020000:Decision Support Systems, Clinical; D005103:Expert Systems; D006801:Humans; D007231:Infant, Newborn; D007362:Intensive Care Units; D007363:Intensive Care Units, Neonatal