A User-Centered Evaluation of a COVID-19 Intelligent Query System (COVID-IQS)

Area of Science:

• Information Science
• Medical Informatics
• Bibliometrics

Background:

• The COVID-19 pandemic necessitates efficient access to scientific literature.
• Existing search tools may not adequately meet the specific needs of researchers.
• Developing specialized systems can enhance knowledge discovery and hypothesis generation.

Purpose of the Study:

• To share the experience of developing an intelligent query system for COVID-19 literature.
• To identify and categorize usability issues through user-centered evaluation.
• To provide lessons learned for building similar literature search engines.

Main Methods:

• Development of an intelligent query system focused on COVID-19 literature.
• User-centered evaluation involving 12 researchers.
• Categorization of identified usability issues into four main areas.

Main Results:

• Usability issues were identified in distinct user needs, functionality errors, information display, and implementation.
• Two key lessons for developing COVID-19 literature search engines were derived.
• The system, COVID-IQS, demonstrated potential for supporting knowledge discovery.

Conclusions:

• User-centered evaluation is crucial for refining scientific literature search systems.
• The developed COVID-IQS can enhance institutional knowledge discovery and hypothesis generation.
• Future iterations will focus on accommodating diverse user roles and collaborative features.