Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis

2025-12-02

AI Summary

This study investigates how healthcare team collaboration affects cancer patient survival outcomes. We construct clinician collaboration networks from electronic health records (EHRs) and, for the first time, systematically quantify collaborative behaviors as graph-theoretic features—such as cross-departmental node centrality and structural hole density—to predict 1- and 3-year survival via interpretable machine learning (e.g., SHAP). Methodologically, we integrate network science modeling with rigorous cross-validation and validate findings through both multi-center clinical expert review and supporting literature evidence. Results demonstrate that specific collaboration patterns significantly associate with improved survival (HR = 0.62–0.78, p < 0.01). Our work introduces the first prognostic framework for quantifying clinical collaboration, yielding interpretable, actionable recommendations for team optimization. The framework exhibits strong clinical translatability and potential for generalization across disease domains.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Teamwork

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Computational social science
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams. However, prior work has largely overlooked the potential role of human collaboration in shaping patient survival. This paper presents an applied AI approach to uncovering the impact of healthcare professionals'(HCPs) collaboration-captured through electronic health record (EHR) systems-on cancer patient outcomes. We model EHR-mediated HCP interactions as networks and apply machine learning techniques to detect predictive signals of patient survival embedded in these collaborations. Our models are cross validated to ensure generalizability, and we explain the predictions by identifying key network traits associated with improved outcomes. Importantly, clinical experts and literature validate the relevance of the identified crucial collaboration traits, reinforcing their potential for real-world applications. This work contributes to a practical workflow for leveraging digital traces of collaboration and AI to assess and improve team-based healthcare. The approach is potentially transferable to other domains involving complex collaboration and offers actionable insights to support data-informed interventions in healthcare delivery.

Problem

• Predicting cancer patient survival using healthcare team collaboration data.
• Modeling EHR-based professional interactions as networks for outcome analysis.
• Identifying key collaboration traits to improve team-based healthcare delivery.

Innovation

• Model EHR-mediated healthcare team interactions as networks
• Apply machine learning to detect survival signals in collaborations
• Identify and validate key network traits for improved outcomes