Building knowledge for poison control: the novel pairing of communication analysis with data mining methods.
As information systems become increasingly integrated with health care delivery, vast amounts of clinical data are stored. Knowledge discovery and data mining methods are potentially powerful for the induction of knowledge models from this data relevant to nursing outcomes. However, an important barrier to the widespread application of these methods for induction of nursing knowledge models is that important concepts relevant to nursing outcomes are often unrepresented in clinical data. For instance, communication approaches are not necessarily consciously chosen by nurses, yet they are known to impact multiple clinical outcomes including satisfaction, pain and symptom response, recovery, physiological change (e.g., blood pressure), and adherence. Decisions about communication behaviors are likely intuitive and instantaneously made in response to cues offered by the patient. For this reason, among others, important choices and actions of nurses are not routinely documented. And so for many clinical outcomes relevant to nursing, important concepts such as communication are not represented in clinical data repositories. In studying poison control center outcomes, it is important to consider not only routinely documented clinical data, but the communication processes and verbal cues of both patient and SPI. In a novel approach, our current study of poison control center outcomes pairs a qualitative study of the communication patterns of SPIs and callers to a regional poison control center, with predictive modeling of poison control center outcomes using knowledge discovery and data mining methods. This three year study, currently in progress, pairs SPI-caller communication analysis with predictive models resulting from the application of knowledge discovery and data mining methods to three years' of archived clinical data. The results will form a hybrid model and the basis for future decision support interventions that leverage knowledge about both implicit and explicit factors that contribute to poison control center outcomes.
Poynton
Mollie R
MR
College of Nursing, University of Utah, Salt Lake City, Utah, USA. mollie.poynton@nurs.utah.edu
Ellington
Lee
L
Caravati
E Martin
EM
Crouch
Barbara I
BI
Bennett
Heather
H
D003142:Communication; D020000:Decision Support Systems, Clinical; D006302:Health Services Research; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D048108:Nursing Informatics / Q000458:organization & administration; D011039:Poison Control Centers; D011041:Poisoning / Q000451:nursing*; D013637:Tape Recording