A Study of Healthcare Team Communication Networks using Visual Analytics

Area of Science:

• Health Informatics
• Network Science
• Computational Medicine

Background:

• Healthcare professional cooperation is vital for patient survival.
• Complex Multiteam Systems (MTS) in healthcare present challenges in understanding cooperation dynamics.
• Electronic Health Records (EHR) contain valuable data on interprofessional communication.

Purpose of the Study:

• To develop and apply a visual analytics workflow to analyze MTS communication networks.
• To identify associations between communication network characteristics and cancer patient survival outcomes.
• To leverage EHR access logs for understanding healthcare collaboration.

Main Methods:

• Construction of MTS communication networks from real-world cancer patient EHR access logs.
• A visual analytics workflow involving neural network learning, dimensionality reduction, and interactive interpretation.
• Classification of data based on input/output attributes and simplification of results.

Main Results:

• Extracted associations between semantic characteristics of MTS communication networks and patient survival.
• Demonstrated the utility of the visual analytics workflow through two case studies.
• Gained insights from expert interviews regarding the findings.

Conclusions:

• The visual analytics workflow effectively reveals crucial factors in HCP cooperation impacting patient survival.
• Analyzing EHR access logs provides a novel approach to understanding complex healthcare communication networks.
• This methodology can inform strategies to optimize team collaboration for improved patient outcomes.