Supporting drug prescription through autocompletion

Area of Science:

• Health Informatics
• Clinical Information Systems
• Medical Terminology

Background:

• Computerized prescribing is crucial for modern healthcare, aiding drug delivery and care scheduling.
• It aims to reduce medication errors and enhance medication logistics.
• Variability in clinician terminology and spelling challenges complicate concept selection in large vocabularies.

Purpose of the Study:

• To develop a tool that facilitates the computerized prescription process.
• To address the challenge of time-consuming term searches in large medical vocabularies.
• To improve the unambiguous selection of medical concepts during prescribing.

Main Methods:

• Developed a tool that proposes likely medical terms based on initial user input (first letters).
• Implemented a ranking mechanism to help users select the most appropriate term.
• Conducted experimental evaluations to assess the tool's performance.

Main Results:

• The tool successfully proposes relevant medical terms based on partial input.
• Ranking of results aids in efficient and accurate term selection.
• Experimental evaluation demonstrated promising results in easing terminology manipulation.

Conclusions:

• The developed tool significantly simplifies the process of selecting medical terms in computerized prescribing.
• It offers a practical solution to improve efficiency and reduce potential errors in clinical information systems.
• The tool enhances user experience by minimizing time spent on terminology searches.