Computer says 2.5 litres--how best to incorporate intelligent software into clinical decision making in the intensive care unit?

Abstract

What will be the role of the intensivist when computer-assisted decision support reaches maturity? Celi's group reports that Bayesian theory can predict a patient's fluid requirement on day 2 in 78% of cases, based on data collected on day 1 and the known associations between those data, based on observations in previous patients in their unit. There are both advantages and limitations to the Bayesian approach, and this test study identifies areas for improvement in future models. Although such models have the potential to improve diagnostic and therapeutic accuracy, they must be introduced judiciously and locally to maximize their effect on patient outcome. Efficacy is thus far undetermined, and these novel approaches to patient management raise new challenges, not least medicolegal ones.