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

智能癌症护理团队：加强 EHR 沟通以改善专业间团队合作

• 批准号: 10504435
• 负责人: Kwan-Liu Ma
• 金额: $ 70.68万
• 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: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504435
• 关键词: Academy; Accident and Emergency department; Address; Affect; Aftercare; Algorithms; Breast; Cancer Patient; Caregivers; Cause of Death; Cessation of life; Clinical; Cognition; Colorectal; Combined Modality Therapy; Communication; Data; Decision Making; Dimensions; Discipline; Electronic Health Record; Emergency department visit; Fast Healthcare Interoperability Resources; Focus Groups; Future; Goals; Health Professional; Healthcare; Hospitalization; Information Management; Intervention; Intervention Studies; Interview; Knowledge; Learning; Machine Learning; Malignant Neoplasms; Measures; Mediating; Medical; Medicare; Medicine; Methods; Modeling; National Cancer Institute; National Research Council; Non-Small-Cell Lung Carcinoma; Outcome; Pathway Analysis; Patient Care; Patient-Centered Care; Patient-Focused Outcomes; Patients; Performance; Policies; Population; Positioning Attribute; Quality of Care; Reporting; Research; Risk; Role; Site; Social Network; Structure; System; Systems Theory; Technology; Treatment Protocols; United States; Visual; Visualization; Work; aging population; analytical tool; base; beneficiary; cancer care; cancer diagnosis; cancer therapy; care systems; comorbidity; cost; design; digital; effectiveness evaluation; graph neural network; health care delivery; improved; information processing; innovation; interest; mortality; novel; prototype; social health determinants; theories; time use; tool

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.

抽象的 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。美国国家医学院描述了一种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。与这些结合 优先事项，我们研究的总体目标是确定IP团队工作如何影响质量成果并发展 改善癌症护理团队合作的工具。 MultiTeam System（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）之间的IP工作。我们建议利用社交网络分析和机器 学习（ML）辅助视觉分析，将我们的初步研究扩展到研究所有使用EPIC的研究地点的理论信息，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，通过EHRS的信息共享，其目的：目标1：制定癌症护理中EHR内部和群体之间的新措施。目标2：确定目标EHR通信结构与癌症护理质量结果的关联，特别是可能预防的ED访问和计划外的住院治疗。 AIM 3：开发ML辅助的视觉分析和原型工具以（a）为特征MTSS，（b）预测患有EHR通信结构与质量较差的EHR通信结构的患者。我们将在三个研究部位提取II或III期乳房，结直肠癌和非小细胞肺癌患者的EHR数据（n = 2,746）。对于每个患者，使用时间stamp的EHR访问数据数据，我们将构建一个她/他的癌症护理MT的加权通信网络，以衡量组内和间的沟通分数。我们将应用零充气的泊松模型来分析目标EHR网络结构与质量结果的关联，从而控制了健康的复杂性和健康的社会决定因素。利用AIM 2的结果，为AIM 3奠定了关键的基础，我们将与HCP，患者和护理人员进行访谈和焦点小组（n = 90），以对EHR通信结构进行更深入的了解。我们将应用并扩展图神经网络（GNN）来预测具有与差的EHR通信结构以及预测背后的推理相关的EHR通信结构的患者。此外，我们将开发一种算法，以推荐潜在的通信结构变化，从而大大降低风险。这项研究介绍了国家研究委员会的报告，强调了视觉分析在医疗保健中支持认知，决策和工作流优化的核心作用。在随后的研究中，我们将评估ML辅助视觉分析工具在改善患者预后和降低成本方面的有效性。