Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis

TL;DR

This paper demonstrates how analyzing healthcare team collaboration through electronic health records using AI can predict cancer patient outcomes and identify key collaborative traits that improve survival rates.

Contribution

It introduces a novel AI-based method to model and analyze healthcare team interactions from EHR data, linking collaboration patterns to patient survival outcomes.

Findings

Healthcare team collaboration traits are predictive of patient survival.

Key network features correlate with improved cancer outcomes.

Validated relevance of collaboration traits through clinical expert review.

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.