Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis
It addresses the incremental problem of improving predictive analysis in healthcare by incorporating team collaboration data, potentially benefiting healthcare providers and patients through data-informed interventions.
This paper tackled the problem of predicting cancer patient outcomes by analyzing healthcare team collaboration captured in electronic health records, modeling interactions as networks and applying machine learning to detect predictive signals for survival, with cross-validation and expert validation confirming the relevance of identified collaboration traits.
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.