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

SCH：智能 EHR 数据分析可增强癌症护理多团队系统

• 批准号: 10437166
• 负责人: Kwan-Liu Ma
• 金额: $ 26.41万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10437166
• 关键词: Academy; Address; Big Data; Breast; Cancer Patient; Caring; Cause of Death; Center for Translational Science Activities; Cessation of life; Clinical; Clinical Sciences; Collaborations; Colorectal; Communication; Communication Research; Communications Media; Complex; Computer software; Coupling; Data; Data Analytics; Data Science; Discipline; Effectiveness; Electronic Health Record; Future; Growth; Health Professional; Healthcare; Individual; Libraries; Location; Machine Learning; Malignant Neoplasms; Mediating; Medical Care Team; Medicine; Mentors; Methods; National Cancer Institute; Network-based; Non-Small-Cell Lung Carcinoma; Pathway Analysis; Patient-Focused Outcomes; Patients; Performance; Persons; Problem Solving; Process; Reporting; Research; Research Activity; Role; Structure; System; Systems Theory; Technology; Time; Validation; Visual; Visualization; Work; analytical method; analytical tool; base; cancer care; cancer diagnosis; cancer therapy; design; digital; disease diagnosis; evidence base; flexibility; health care delivery; health care settings; improved; information processing; interdisciplinary collaboration; machine learning method; minority student; mortality; novel; open source; pedagogy; programs; support tools; undergraduate research; undergraduate student; virtual healthcare

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.

癌症在美国的死亡率第二个主要原因继续排名，预计2020年有180万例新的癌症病例，癌症死亡606,520例。美国国家医学院报告强调了18个或更多不同的临床学科或角色可能与患者的综合癌症护理有关。因此，国家癌症研究所优先考虑改善基于团队的癌症护理的必要性。多团队系统（MTS）的观点提供了一个理论框架，以检查多个医疗保健专业人员（HCP）之间相互依存的工作。为了实现更好的系统性能，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（EHRS）在连接虚拟护理团队的核心作用，即为同一患者提供护理，在不同时间和位置工作的HCP组，并通过技术介导的沟通来支持他们的工作。通过EH RS，虚拟护理团队开发了一个用于编码，存储和检索患者信息的分布式通信系统，可以将其作为通信网络进行检查，该通信网络具有单独的HCP，代表信息代理，以及其中指示信息流的通信链接。了解这种沟通的网络形式和影响其有效性的因素对于虚拟护理团队的实践至关重要。但是，随着EHR数据的爆炸性增长，这些复杂的团队合作互动的“数字痕迹”提供了前所未有的大小和复杂性的数据，可以在其自然医疗保健环境中研究MTSS。我们建议开发用于基于ML的EHR数据网络分析的方法，并对分析结果进行视觉解释，以理解和促进癌症护理团队系统中更有效的沟通和团队合作，重点是研究癌症护理MTS的乳腺癌，结直肠癌和非小细胞细胞肺癌患者。我们的项目独特地解决了变革性数据科学研究主题。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多样化的基础。我们希望该项目将对视觉分析技术的进步以及可解释的AL和机器学习做出基本贡献，以优化医疗保健过程，特别是对未来基于团队的癌症护理产生影响。