Algorithmic and user study of an autocompletion algorithm on a large medical vocabulary

Area of Science:

• Human-Computer Interaction
• Medical Informatics
• Algorithm Analysis

Background:

• Autocompletion enhances user interaction in text-entry software.
• Focus on medical professionals entering ontology concepts for structured data in medicine.

Purpose of the Study:

• Algorithmic analysis of the multi-prefix matching autocompletion algorithm.
• Evaluate its effectiveness for entering concepts from SNOMED CT.

Main Methods:

• Described and optimized the multi-prefix algorithm.
• Compared its performance against a baseline algorithm.
• Conducted a user experiment with 12 participants.

Main Results:

• Users required significantly fewer keystrokes with the multi-prefix algorithm compared to the baseline.
• Demonstrated improved efficiency in concept entry.

Conclusions:

• The multi-prefix algorithm is a strong candidate for efficient medical term retrieval.
• Supports the demand for structured data in medicine.