SMART Cancer Care Teams: Enhancing EHR Communication to Improve Interprofessional Teamwork

摘要 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。美国国家医学科学院描述了一个处于危机中的癌症护理系统，跨专业(IP)团队合作和协调在很大程度上是例外，而不是规则。 国家癌症研究所优先考虑改善基于IP团队的癌症护理的需要。与以下内容保持一致 优先事项，我们研究的总体目标是确定知识产权团队合作如何影响质量结果和开发 改善癌症护理团队合作的工具。多团队系统(MTS)视角提供了一个理论框架来检查多组医疗保健专业人员(HCP)之间的知识产权工作。我们建议利用社交网络分析和机器 学习(ML)辅助的视觉分析，将我们的初步研究扩展到检查三个都使用EPIC的研究站点的理论知情的、有针对性的电子健康记录(EHR)网络结构。我们的研究集中在团队沟通的一个可修改的维度上，即通过EHR进行信息共享，目标1：在癌症护理mTSS中开发组内和组间EHR沟通的新措施。目标2：确定有针对性的EHR沟通结构与癌症护理质量结果的关联，特别是潜在的可预防的急诊室就诊和计划外住院。目的3：开发ML辅助的视觉分析和原型工具，以(A)描述mTSS的特征，以及(B)预测与不良结果相关的EHR沟通结构的患者。我们将在三个研究地点(N=2,746)提取II或III期乳腺癌、结直肠癌和非小细胞肺癌患者的EHR数据。对于每个患者，我们将使用带有时间戳的EHR访问日志数据，构建她/他的癌症护理MT的加权沟通网络，以衡量组内和组间的沟通分数。我们将应用零膨胀泊松模型来分析目标EHR网络结构与质量结果的关联，控制医疗复杂性和健康的社会决定因素。利用目标2的结果，并为目标3奠定关键基础，我们将对HPC、患者和照顾者(N=90)进行访谈和焦点小组，以更深入地了解EHR的沟通结构。我们将应用和扩展图形神经网络(GNN)来预测具有与不良预后相关的EHR通信结构的患者，并提供预测背后的推理。此外，我们将开发一种算法来建议潜在的通信结构变化，从而显著降低风险。这项研究介绍了国家研究委员会的报告，强调了视觉分析在支持医疗保健中的认知、决策和工作流程优化方面的核心作用。在随后的研究中，我们将评估ML辅助的视觉分析工具在改善患者结果和降低成本方面的有效性。

ABSTRACT 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 described a cancer care system in crisis, with interprofessional (IP) teamwork and coordination largely the exception rather than the rule. The National Cancer Institute prioritized the need to improve IP team-based cancer care. Aligned with these priorities, the overall goal of our research is to determine how IP teamwork affects quality outcomes and develop tools to improve teamwork in cancer care. The multiteam system (MTS) perspective offers a theoretical framework to examine IP work among multiple groups of healthcare professionals (HCPs). We propose to leverage social network analysis and machine learning (ML)-assisted visual analytics to extend our preliminary studies to examining theory-informed, targeted Electronic Health Record (EHR) network structures at three study sites that all use Epic. Our research centers on one modifiable dimension of team communication, information sharing through EHRs, with these aims: Aim 1: Develop new measures of within- and between-group EHR communication in cancer care MTSs. Aim 2: Determine the associations of targeted EHR communication structures with cancer care quality outcomes, specifically potentially preventable ED visits and unplanned hospitalizations. Aim 3: Develop ML-assisted visual analytics and prototype tools to (a) characterize MTSs, and (b) predict patients with EHR communication structures associated with poor quality outcomes. We will extract EHR data of patients with stage II or III breast, colorectal, and non-small cell lung cancer at three study sites (N=2,746). For each patient, using time-stamped EHR access-log data, we will construct a weighted communication network of her/his cancer care MTS to measure within- and between-group communication scores. We will apply zero-inflated Poisson models to analyze the associations of targetd EHR network structures with quality outcomes, controlling for medical complexity and social determinants of health. Leveraging Aim 2 results, and to lay the critical groundwork for Aim 3, we will conduct interviews and focus groups with HCPs, patients, and caregivers (N=90), to gain a more in-depth understanding of EHR communication structures. We will apply and extend graph neural networks (GNN) to predict patients who have EHR communication structures associated with poor outcomes as well as provide the reasoning behind the prediction. Furthermore, we will develop an algorithm to recommend potential communication structure changes that significantly reduce risk. This study addresses the National Research Council report underscoring the central role of visual analytics to support cognition, decision-making, and workflow optimization in healthcare. In a subsequent study, we will evaluate the effectiveness of ML-assisted visual analytics tools at improving patient outcomes and reducing costs.