Abstract
Corporate settings often conceal emerging psychological risks until they manifest in reduced performance, increased absenteeism, and turnover. This paper presents a collaborative evaluation framework leveraging the Multimodal Emotionally Intelligent Healthcare Network (MEIHNet) to enable proactive detection of employee distress. MEIHNet integrates physiological signals, natural language sentiment analysis, and behavioral indicators into a unified platform, facilitating real‑time monitoring and multi‑stakeholder feedback. Through collaborative evaluation with employees, managers, and occupational health professionals, the model identifies subtle emotional and cognitive shifts that precede clinical symptoms. A pilot study within a multinational firm demonstrated that MEIHNet achieved a high percentage of accuracy in anticipating elevated stress levels one week in advance, while preserving data privacy and fostering trust among stakeholders. The results suggest that collaborative evaluation via MEIHNet can transform corporate health strategies from reactive interventions to preventive care, ultimately enhancing well‑being, productivity, and organizational resilience.
Presenters
Michael OseiStudent, PhD in Evaluation, Western Michigan University, Michigan, United States Michelle Rincones Rodriguez
Student, Master of Science, London School of Economics
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2025 Special Focus—Emotional vs Artificial Intelligence: A Paradigm Shift in Healthcare?
KEYWORDS
Multimodal Emotionally Intelligent Healthcare Network, Collaborative Evaluation, Model for Collaborative
