Title: Analyzing and Interpreting Collaborative Dynamics in Predicting Cancer Care Outcomes

Abstract:

Effective cancer management necessitates a longitudinal strategy, wherein therapeutic interventions are continuously tailored and administered over time to address the specific requirements of each patient. Although extensive research has examined the influence of clinical and demographic variables—such as age and comorbidities—on treatment planning, the actual delivery phase of care has received comparatively little scrutiny. However, both the planning and execution of care are inherently collaborative endeavors, relying on the synchronized efforts of various healthcare professionals (HCPs). Consequently, the human elements embedded within these cooperative practices are vital for enhancing patient outcomes. Despite their significance, current literature on human factors in oncology remains sparse, with minimal studies exploring how teamwork evolves throughout the treatment trajectory.

To address this deficiency, this study investigates the impact of HCP collaboration, as recorded via electronic health record (EHR) systems, on cancer patient outcomes, with a specific focus on the dynamics of teamwork. We model interactions between HCPs mediated by EHRs as networks and employ machine learning techniques to uncover predictive indicators of patient survival inherent in these collaborative frameworks. Furthermore, we enhance the interpretability of our model by identifying specific network attributes and dynamic patterns linked to distinct outcomes. We assess the reliability of our model through rigorous robustness analyses, ensuring that the results are stable and not artifacts of random variation during the training process. Moreover, our findings corroborate hypotheses found in medical literature, providing empirical, data-driven support for these established claims. Ultimately, this research offers a practical methodology for utilizing digital traces of collaboration to assess and improve longitudinal, team-based healthcare, delivering actionable insights to inform data-driven interventions in healthcare delivery.

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