Trust@Health: A Trust-Based Multilayered Network for Scalable Healthcare Service Management

2025-08-16

AI Summary

This study addresses the challenges of modeling cross-level trust relationships—among physicians, departments, and hospitals—and weak decision support in healthcare systems. We propose a multi-layer trust evolution framework integrating social interaction and professional collaboration. Our approach constructs an evolutionary graph-based cross-level network model and designs a quantifiable trust propagation algorithm to assess trust across hospital-, department-, and physician-level nodes. Empirical evaluation demonstrates strong correlation between institutional trust scores and official accreditation ratings (r = 0.91); physician-level scores exhibit significant skewness, exposing potential biases in existing evaluation mechanisms. The framework enhances referral recommendation accuracy and improves interpretability in resource allocation. By enabling scalable, data-driven governance, it establishes a novel paradigm for trust-aware decision support in healthcare systems.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

We study the intricate relationships within healthcare systems, focusing on interactions among doctors, departments, and hospitals. Leveraging an evolutionary graph framework, the proposed model emphasizes both intra-layer and inter-layer trust relationships to better understand and optimize healthcare services. The trust-based network facilitates the identification of key healthcare entities by integrating their social and professional interactions, culminating in a trust-based algorithm that quantifies the importance of these entities. Validation with a real-world dataset reveals a strong correlation (0.91) between the proposed trust measures and the ratings of hospitals and departments, though doctor ratings demonstrate skewed distributions due to potential biases. By modeling these relationships and trust dynamics, the framework supports scalable healthcare infrastructure, enabling effective patient referrals, personalized recommendations, and enhanced decision-making pathways.

Problem

• Analyzing trust relationships among healthcare entities for optimization
• Identifying key entities via social and professional interaction integration
• Enhancing decision-making with scalable trust-based healthcare infrastructure

Innovation

• Evolutionary graph framework for trust relationships
• Trust-based algorithm quantifying entity importance
• Scalable infrastructure for healthcare management