MedTxting: learning based and knowledge rich SMS-style medical text contraction.

Area of Science:

• Health Informatics
• Medical Communication Systems
• Digital Health

Background:

• Short Message Service (SMS) in mobile health (m-health) improves self-management and health outcomes.
• Character limits in SMS hinder comprehensive medical communication.
• Existing systems lack efficient medical text compression for SMS.

Purpose of the Study:

• To develop an innovative system (MedTxting) for compressing medical texts into SMS format.
• To enhance the efficiency of clinical workflows through optimized medical communication.
• To overcome the limitations of SMS character size for healthcare applications.

Main Methods:

• Developed MedTxting, a learning-based, knowledge-rich system for medical text compression.
• Evaluated the system using clinical questions and discharge summary narratives.
• Assessed text compression effectiveness, readability, and size reduction.

Main Results:

• MedTxting effectively compresses medical texts for SMS communication.
• The system achieves noticeable size reduction while maintaining reasonable readability.
• Demonstrated significant potential for improving clinical communication efficiency.

Conclusions:

• MedTxting offers a viable solution for overcoming SMS character limits in healthcare.
• The system facilitates real-time, cost-effective communication for patient reporting and medication consulting.
• MedTxting can enhance physician collaboration and improve point-of-care delivery.