Using mathematical models to improve the utility of quantitative ICU data

Abstract

Intensive care medicine is one of the areas of medicine most closely linked to applied physiology. Furthermore, it has a long tradition of being the forefront of advanced physiologic measurement technologies. The associated volume of quantitative data about a patient's physiologic status, therapy, together with the output of off-line analyses, creates an information overload that profoundly reduces efficient and effective information processing. To a certain extent, this disconnection is a reason for the slow progress in utilizing such information across patients and hospital systems to improve patient care, perhaps most prominently evidenced by the failure of the physiologically valuable information provided by pulmonary artery catheterization to improve outcome in the critical care setting [1, 2]. In fact, for newer and more advanced monitoring equipment, evaluations of utility and ability to fit into proven treatment protocols is often lacking. Although the difficulty in translating the increased amount of available patient-specific information into patient benefit may in part be due to the lack of adequate therapeutic options, where clear benefit is known, actual translation of this information into practice is a primary barrier to improving patient care. © 2007 Springer Science + Business Media Inc.