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.

翻译：癌症治疗结果不仅受临床和人口统计学因素的影响，还受到医疗团队协作的影响。然而，以往的研究在很大程度上忽视了人类协作在影响患者生存方面的潜在作用。本文提出了一种应用人工智能方法，以揭示通过电子健康记录（EHR）系统捕获的医疗专业人员（HCP）协作对癌症患者预后的影响。我们将EHR介导的HCP交互建模为网络，并应用机器学习技术来检测这些协作中嵌入的患者生存预测信号。我们的模型经过交叉验证以确保泛化能力，并通过识别与改善预后相关的关键网络特征来解释预测结果。重要的是，临床专家和文献验证了所识别的关键协作特征的相关性，强化了其在现实应用中的潜力。这项工作有助于建立一种实用工作流程，利用协作的数字痕迹和人工智能来评估和改善基于团队的医疗保健。该方法具有潜在的可迁移性，可应用于其他涉及复杂协作的领域，并提供可行的见解以支持医疗保健服务中数据驱动的干预措施。