SCH: Smart EHR Data Analytics to Enhance Cancer Care Multiteam Systems

癌症仍然是美国第二大死亡原因，预计2020年新增癌症病例180万例，癌症死亡606,520人。美国国家医学研究院的报告强调，18个或更多不同的临床学科或角色可能涉及患者的全面癌症护理。因此，国家癌症研究所优先考虑改善以团队为基础的癌症护理的需要。多团队系统(MTS)视角提供了一个理论框架来检查多个医疗保健专业团队(HCP)之间相互依赖的工作。为了达到更好的系统性能，MTS理论认为，系统必须发展有效的沟通和协调。电子健康记录(EHR)在连接虚拟护理团队方面发挥了核心作用，即向相同患者提供护理、在不同时间和地点工作并通过技术中介沟通支持其工作的一组HCP。通过EH Rs，虚拟护理团队开发了用于编码、存储和检索患者信息的分布式通信系统，可以将其视为一个通信网络，其中单个HC代表信息代理，它们之间的通信链接指示信息流。了解这种交流的网络形式和影响其有效性的因素将对虚拟护理团队的实践至关重要。然而，随着电子病历数据的爆炸性增长，这些复杂团队互动的“数字痕迹”为在自然医疗环境中研究mTS提供了前所未有的大小和复杂性的数据。我们建议开发基于ML的EHR数据网络分析和分析结果的可视化解释方法，以了解和促进癌症护理团队系统中更有效的沟通和团队合作，重点研究乳腺癌、结直肠癌和非小细胞肺癌患者的癌症护理MTSS。我们的项目独特地解决了变革性的数据科学研究主题。我们项目中的跨学科合作将为创造性的问题解决和验证提供不同的基础。我们预计该项目将对视觉分析技术的进步以及用于优化医疗流程的可解释人工智能和机器学习做出根本性贡献，特别是对未来基于团队的癌症护理产生影响。

Cancer continues to rank as the second leading cause of mortality in the US, with 1.8 million projected new cancer cases in 2020, and 606,520 cancer deaths. The National Academy of Medicine report underscored that 18 or more different clinical disciplines or roles may be involved in patients' comprehensive cancer care. Accordingly, the National Cancer Institute prioritized the need to improve team-based cancer care. The multi-team system (MTS) perspective offers a theoretical framework to examine interdependent work among multiple teams of healthcare professionals (HCPs). To achieve better system performance, MTS theory submits that the system must develop effective communication and coordination. Electronic health records (EHRs) serve a central role in connecting virtual care teams, i.e., groups of HCPS who provide care to the same patients, work at different times and locations, and support their work with technology-mediated communication. Through EH Rs, virtual care teams develop a distributed communication system for encoding, storing, and retrieving patient information, which can be examined as a communication network with individual HCPs representing information agents and communication linkages among them indicating information flow. Understanding this network form of communication and factors influencing its effectiveness will be crucial for the practice of virtual care teams. However, with the explosive growth of EHR data, these "digital traces" of complex teamwork interactions provide data of unprecedented size and complexity to study MTSs in their natural healthcare settings. We propose to develop methods for ML-based network analysis of EHR data and visual interpretation of analysis results for understanding and promoting more effective communication and teamwork in cancer care team systems with a focus on studying cancer care MTSs of breast, colorectal, and non-small cell lung cancer patients. Our project uniquely addresses the Transformative Data Science research theme. The interdisciplinary collaboration in our project will offer a diverse basis for creative problem solving and validation. We expect this project will make fundamental contributions to the advancements of visual analytics technology and explainable Al and machine learning for optimizing healthcare processes, specifically making impact to future team-based cancer care.