Description
We present a strategy for modeling proposed algorithms on recent patient data to optimize the development of a real-time pediatric sepsis detection tool. These tools test “fast-track” pathways whereby patients who already have sufficient charted information to trigger a sepsis huddle may skip prompted nurse assessments. By modeling the behavior of the tool on real historical patient encounters we aim to identify potential pitfalls before the resource-intensive phases of building and silent monitoring.
Describe the new knowledge and additional skills the participant will gain after attending your presentation.: The attendee should be able to assess the role of retrospective data analysis in the formulation of new clinical tools, and describe the strengths and limitations of retrospective analysis.
Authors:
Amelia Drace (Presenter)
Oregon Health & Science University
Benjamin Orwoll, Oregon Health & Science University
Matthew Storer, Oregon Health & Science University
David Dorr, Oregon Health & Science University
Presentation Materials: