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Related Experiment Videos

Taxonomy development and knowledge representation of nurses' personal cognitive artifacts.

Sharon McLane¹, James P Turley

¹University of Texas School of Health Information Sciences, Houston, TX, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 31, 2010

Summary

Nurses create patient data summaries (knowledge representations) crucial for clinical decision-making and practice. This study developed a taxonomy of these representations to guide informatics in creating better health IT tools.

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Area of Science:

Nursing Informatics
Cognitive Science
Human-Computer Interaction

Background:

Nurses generate patient clinical data summaries (knowledge representations) each shift.
These representations are vital for working memory, task prioritization, critical thinking, and decision-making.
Understanding these cognitive artifacts is essential for developing effective health information technology.

Purpose of the Study:

To elicit and describe the knowledge representations and structures essential for clinical nursing practice.
To develop a taxonomy classifying these necessary nursing knowledge representations.
To explore the application of this methodology to cognitive artifacts in other disciplines.

Main Methods:

Qualitative methods were employed to elicit knowledge representations from practicing nurses.
Analysis of elicited data to identify common structures and themes.
Development of a hierarchical taxonomy based on identified knowledge representation types.

Main Results:

A taxonomy of nursing knowledge representations was developed, detailing essential components for practice.
The study identified the critical role of these representations in supporting nurses' cognitive processes.
Methodology for eliciting and classifying cognitive artifacts was established.

Conclusions:

The developed taxonomy provides a framework for understanding and representing nursing knowledge.
Informing informatics professionals on how to design information systems that support nurses' cognitive work.
Suggests a transferable process for converting cognitive artifacts into functional information systems across disciplines.