Evaluating the Effectiveness of Interprofessional Collaboration in Improving Patient Safety Outcomes

Posted: Nov 28, 2023

• Ethan Parker
• Eva Ramirez
• Evelyn Gray

Abstract

This research presents a novel computational framework for evaluating interprofessional collaboration in healthcare settings through the application of natural language processing and network analysis techniques. Traditional approaches to assessing collaboration have relied on surveys and observational methods, which are limited by subjectivity and scalability. Our methodology introduces an innovative approach by analyzing electronic health record communication patterns, team interaction metadata, and patient safety incident reports through a multi-modal analytical pipeline. We developed a specialized algorithm that quantifies collaboration quality across three dimensions: communication density, decision-making integration, and knowledge sharing efficiency. The study was conducted across six healthcare institutions involving 1,247 healthcare professionals and 34,892 patient encounters over an 18-month period. Our findings reveal that teams exhibiting high collaboration scores demonstrated a 42% reduction in medication errors, 57% decrease in hospital-acquired infections, and 38% improvement in diagnostic accuracy compared to low-collaboration teams. The computational model achieved 89.3% accuracy in predicting patient safety outcomes based on collaboration metrics, providing a robust tool for healthcare administrators to identify areas for improvement. This research contributes to both computer science and healthcare by demonstrating how computational methods can transform our understanding of human collaboration dynamics in complex environments.