Lessons learned in improving the adoption of a real-time NLP decision support system

While most research in the NLP domain focuses on information accuracy, the adoption of NLP applications in healthcare extends beyond technical innovations. This study investigates the adoption issues of an NLP application in three different field sites. Using both quantitative log analysis and quali...

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Hauptverfasser: Yang Huang, Zisook, D., Yunan Chen, Selter, M., Minardi, P., Mattison, J.
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Zisook, D.
Yunan Chen
Selter, M.
Minardi, P.
Mattison, J.
description While most research in the NLP domain focuses on information accuracy, the adoption of NLP applications in healthcare extends beyond technical innovations. This study investigates the adoption issues of an NLP application in three different field sites. Using both quantitative log analysis and qualitative user interviews, we identified four main factors that affect NLP adoption: organizational culture and support, system usability, information quality and system reliability. These factors must be considered to ensure successful adoption of NLP applications that provide real-time decision support in a clinical care setting.
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Accuracy
Decision Support
Encoding
History
Medical services
Natural language processing
NLP
Real time systems
Real-time
Time factors
User Adoption
title Lessons learned in improving the adoption of a real-time NLP decision support system
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