Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support

Area of Science:

• Health Informatics
• Clinical Decision Support
• Data Science in Healthcare

Background:

• Digitization of health IT enables real-time data access for healthcare organizations.
• Increased data availability fuels algorithmic detection of health events.
• Real-time data access presents unique challenges beyond mere accessibility.

Purpose of the Study:

• To highlight challenges in real-time processing of healthcare system data.
• To discuss accurate interpretation of real-time data analysis results.
• To identify obstacles in leveraging real-time data for safety event detection.

Main Methods:

• Expert consensus from informatics and clinical professionals.
• Collated experiences from multiple real-time event detection projects.
• Categorized challenges with specific examples for context.

Main Results:

• Identified 8 major challenge categories.
• Detailed 13 specific challenges and 9 examples.
• Examples focused on detecting discrepancies in medication orders, administration records, and infusion pump data.

Conclusions:

• Real-time data for safety event detection is powerful but challenging.
• Key challenges include data quality and technical complexity.
• Acknowledging and addressing these challenges is vital for effective real-time clinical decision support.