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Abstract

Learning Objectives: Current scoring systems for use in critical illness do not take into account levels of simultaneous physiological or pharmacological support. Yet, clinicians at the bedside subconsciously interpret cardiovascular parameters in the context of this support. In the acquisition of subconscious expertise it is established that it is better to observe an expert solve a problem in real time than to ask them to describe what they do in the abstract. Hypothesis: Using knowledge capture techniques it is possible to design a scale of the overall state of a critically ill patient underpinned by a sophisticated physiological rule base. Methods: Data sets were prepared from the Electronic Records of 10 patients with 2761 time points of routinely collected physiological and pharmacological parameters. A clinician scored each time point as stable (A) through to unstable (E) whilst simultaneously describing a rule set of ranges (A-E) of derangement for each parameter. The same time points were annotated automatically using the rule set described in the abstract and inconsistencies between the two sets of annotations compared in a confusion matrix. Each disagreement was analyzed and changes made to the rule base where appropriate to better capture clinical expertise. The process was repeated with two other clinicians, their clinical annotations being tested against the previous clinician’s rule set, resulting in further refinements. Results: Agreement between clinician 1’s final rule set and annotations post refinement was 96.7%. Agreement between clinician 1’s final rule set clinician 2’s initial annotations and was 10.7% (97.6% after refinement). Initial agreement between clinician 2’s final rule set and clinician 3’s initial annotations was 90.6% (98.1% after refinement - the final A to E rule set for the new score).Conclusions: There was a higher agreement between the final rule set of the first clinician and the initial annotations of each subsequent clinician as the refinement proceeded. It is possible to design a sophisticated rule base underpinning a physiological score using knowledge capture techniques.