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Published on: February 23, 2019
A text matching method to facilitate the validation of frequent order sets obtained through data mining
Chengjian Che1, Roberto A Rocha
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 24, 2007
Summary
A new tool was developed to compare data mining-discovered order sets with existing ones. The automated matching process showed 81% sensitivity and 84% specificity compared to manual review.
Area of Science:
- Health Informatics
- Computer Science
Background:
- Clinical order sets are crucial for standardized patient care.
- Data mining algorithms can discover novel order sets.
- Comparing discovered order sets with existing ones is essential for validation.
Purpose of the Study:
- To develop and evaluate an automated tool for matching data mining-discovered order sets with existing clinical order sets.
Main Methods:
- An order matching tool was developed utilizing Oracle Text.
- The tool incorporated both automated searching and manual review functionalities.
- Performance was assessed by comparing automated matching results against manual review outcomes.
Main Results:
- The automated matching process demonstrated a sensitivity of 81%.
- The automated matching process achieved a specificity of 84% when compared to manual review.
Conclusions:
- The developed Oracle Text-based tool provides a viable method for comparing clinical order sets.
- Automated matching offers a reasonably accurate alternative to manual review for order set comparison.
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