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A methodology for meeting context-specific information needs related to nursing orders
Sarah Collins1, Suzanne Bakken, James J Cimino
1Columbia University School of Nursing, New York, NY, USA. sac2125@columbia.edu
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|August 13, 2008
Summary
This study developed a method to identify nurses' specific information needs at the point of care. This approach enhances automated Infobuttons by anticipating clinical questions for better evidence-based practice.
Area of Science:
- Nursing Informatics
- Clinical Decision Support
Background:
- Nurses require timely, evidence-based information at the point of care.
- Automated Infobuttons are crucial for accessing this information but depend on accurate question anticipation.
- Existing systems may not fully address context-specific nursing information needs.
Purpose of the Study:
- To develop and evaluate a methodology for identifying context-specific nurse information needs related to clinical information system (CIS) nursing orders.
- To improve the effectiveness of automated Infobuttons in delivering relevant clinical evidence.
Main Methods:
- A six-process methodology was employed, deriving context-specific questions from generic nurse questions.
- Data sources included nurse focus groups, observational data, and a taxonomy.
- Questions were filtered by nursing order semantic type within logical clinical context clusters (LCCCs) identified from the nurse perspective.
Main Results:
- The study successfully generated context-specific questions tailored to nursing orders.
- Logical clinical context clusters (LCCCs) were identified as key to understanding nurse information needs.
- This approach facilitates the assessment of Internet-based resources for answering specific nurse queries.
Conclusions:
- A structured methodology can effectively identify context-specific nurse information needs within a CIS.
- Logical clinical context clusters (LCCCs) are vital for tailoring Infobutton content and improving resource accessibility.
- This work supports the development of more intelligent and responsive clinical decision support tools for nurses.
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