建模与解释癌症护理结果预测中的团队协作动态
Modeling and Interpreting Teamwork Dynamics in Cancer Care Outcome Prediction
AI总结：
利用电子健康记录中的协作网络和机器学习方法，研究医疗专业人员团队协作动态对癌症患者生存预测的影响，并解释关键网络特征。
摘要：
癌症护理需要纵向方法，根据每个患者的需求随时间规划和实施治疗。虽然先前研究深入探讨了临床和人口统计学因素（如合并症和年龄）如何指导治疗规划，但对护理实施阶段的关注却少得多。然而，规划和实施都是基于团队的过程，依赖于多个医疗专业人员之间的协调努力。因此，这些协作实践中蕴含的人为因素对于优化患者结果至关重要。尽管重要性显著，但现有关于癌症护理中人为因素的文献有限，很少有研究调查护理团队内的协作如何在治疗过程中演变。为填补这一空白，本研究探讨通过电子健康记录系统捕获的医疗专业人员协作如何影响癌症患者结果，特别强调团队协作动态。我们将电子健康记录介导的医疗专业人员交互表示为网络，并应用机器学习方法识别这些协作结构中嵌入的患者生存预测信号。我们进一步通过指出与特定结果相关的网络特征和动态模式来解释模型预测。我们通过稳健性分析评估模型，确保发现稳定且不受训练中随机变异驱动。此外，我们的见解与医学文献中提出的假设一致，我们的结果为这些主张提供了基于经验数据的证据。总体而言，我们的工作提供了一个实用流程，利用协作的数字痕迹来评估和加强纵向团队医疗，为医疗实施中的数据驱动干预提供可操作的见解。
英文摘要：
Cancer care requires a longitudinal approach in which treatments are planned and delivered over time according to the needs of each individual patient. While prior research has thoroughly explored how clinical and demographic factors, such as comorbidities and age, inform treatment planning, far less attention has been devoted to the delivery phase of care. Yet planning and delivery are both team-based processes that depend on coordinated efforts among multiple healthcare professionals (HCPs). As such, the human factors embedded in these collaborative practices are crucial to optimizing patient outcomes. Despite this importance, the existing literature on human factors in cancer care is limited, and very few studies have investigated how collaboration within care teams evolves over the course of treatment. To fill this gap, this work examine how HCPs' collaboration, captured through electronic health record (EHR) systems, affects cancer patient outcomes, with particular emphasis on teamwork dynamics. We represent EHR-mediated HCP interactions as networks and apply machine learning methods to identify predictive signals of patient survival embedded in these collaborative structures. We further interpret model predictions by pinpointing network characteristics and dynamic patterns associated with particular outcomes. We evaluate our model through robustness analyses to ensure that the findings are stable and not driven by stochastic variation in training. Additionally, our insights align with hypotheses proposed in the medical literature, and our results provide the empirical, data-driven evidence supporting these claims. Overall, our work contributes a practical workflow for leveraging digital traces of collaboration to evaluate and strengthen longitudinal team-based healthcare, offering actionable insights to guide data-informed interventions in healthcare delivery.
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