Title: Linking Healthcare Collaboration to Patient Prognosis for Predictive Modeling

Abstract: While clinical and demographic variables are well-known determinants of cancer treatment results, the role of healthcare team collaboration is equally significant yet frequently underappreciated in previous research. This study introduces an applied artificial intelligence framework to quantify how the cooperation among healthcare professionals (HCPs), as recorded in electronic health record (EHR) systems, affects survival rates for cancer patients. By representing HCP interactions mediated through EHRs as networks, we employ machine learning algorithms to extract predictive indicators of patient longevity embedded within these collaborative patterns. To guarantee the robustness and generalizability of our findings, we subjected our models to cross-validation. Furthermore, we clarify these predictions by pinpointing specific network characteristics linked to better health outcomes. The significance of these identified collaborative traits is corroborated by both existing medical literature and clinical experts, highlighting their viability for practical implementation. This research establishes a functional workflow for utilizing digital collaboration records and AI to evaluate and enhance team-oriented care. Additionally, the methodology holds potential for adaptation to other sectors characterized by intricate cooperation, providing actionable data-driven insights to optimize healthcare delivery.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC