Integrating real-time clinical activity and behavioral responses for characterizing cognitive load and errors (IGNITE)

整合实时临床活动和行为反应来表征认知负荷和错误（IGNITE）

• 批准号: 10707148
• 负责人: Thomas George Kannampallil
• 金额: $ 40万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707148
• 关键词: Integrating; real; time; clinical; activity

PROJECT SUMMARY Cognitive load during the delivery of clinical care is affected by a number of factors including specialty, practice setting, patient complexity, electronic health record (EHR) use, and clinician expertise. In the United States, clinical care is primarily documented using EHRs: documentation burden, poor usability, and unnecessary navigation contribute to increased cognitive load. Such increases in cognitive load, in turn, contribute to more work hours, dissatisfaction with work, poor patient outcomes (e.g., errors), burnout and poor clinician outcomes. Much of the prior research characterizing clinician cognitive load or its impact on errors has relied on retrospective approaches including self-reports, time-motion studies, and focus groups. Similarly, burnout also has been measured exclusively using surveys. EHR-based audit logs have shown considerable promise as a viable resource for tracking and measuring clinical activities without the incremental survey burden on clinicians. Our research team has demonstrated that workload measures based on audit logs can be used to assess cognitive load, burnout, and errors. Based on this promising pilot work, the primary focus of the IGNITE (Integrating real-time clinical activity and behavioral responses for characterizing cognitive load and errors) study is to utilize EHR-based audit logs and decision support tools to objectively determine the direct relationships between (a) cognitive load and errors, and (b) the mediating role of clinician burnout in explaining the relationship between cognitive load and errors. We will accomplish this through a large-scale multi-site study conducted at three large academic medical centers associated with Washington University/BJC HealthCare, Stanford University, and University of Colorado. For the first aim, we will utilize EHR-based audit logs collected across non-surgical, inpatient settings across three sites over a 3-year period (1/1/2019 to 12/31/2021) to develop measures of cognitive load—both intrinsic and extraneous—and assess the effect of cognitive load on objectively measured wrong-patient errors (using the retract-and-reorder alerts). For the second aim, we will prospectively collect data on a cohort of 300 trainees (residents, fellows) from Medicine and Pediatrics from each study site over a 5-month period. Monthly burnout surveys, along with cognitive load measures from the EHR-based audit logs, and wrong-patient errors during the study period will be used to determine the mediating relationships between cognitive load, burnout and errors. In addition, for both of the proposed aims, we will develop advanced machine learning algorithms to predict errors and burnout from EHR-based activity sequences. Insights from this study will help in designing targeted interventions aligned with the contextual work practices of physicians, designing clinical trials for evaluating such interventions, and in developing informed policy guidelines for the safety and well-being of physicians.

项目概要 临床护理期间的认知负荷受到多种因素的影响，包括专业、 美国的实践环境、患者复杂性、电子健康记录 (EHR) 使用和临床医生专业知识。 各州，临床护理主要使用 EHR 进行记录：记录负担、可用性差以及 不必要的导航会增加认知负荷，反过来， 导致更多的工作时间、对工作的不满、患者结果不佳（例如错误）、倦怠和不良 许多先前的研究描述了临床医生的认知负荷或其对错误的影响。 依赖于回顾性方法，包括自我报告、时间运动研究和焦点小组。 职业倦怠还专门使用基于电子病历的审计日志进行了衡量。 有望成为跟踪和衡量临床活动的可行资源，无需增量调查 我们的研究团队已经证明，基于审核日志的工作量测量可以减轻登山者的负担。 基于这项有前途的试点工作，主要关注点是用于评估认知负荷、倦怠和错误。 IGNITE（整合实时临床活动和行为反应来表征认知负荷 和错误）研究是利用基于 EHR 的审计日志和决策支持工具来客观地确定 (a) 认知负荷和错误之间的直接关系，以及 (b) 临床医生倦怠的中介作用 我们将通过大规模的研究来解释认知负荷和错误之间的关系。 在与华盛顿大学/BJC 相关的三个大型学术医疗中心进行的多地点研究 HealthCare、斯坦福大学和科罗拉多大学 对于第一个目标，我们将利用基于 EHR 的审计。 3 年期间（2019 年 1 月 1 日至 2019 年 1 月 1 日）跨三个地点的非手术住院环境收集的日志 2021 年 12 月 31 日）制定认知负荷测量方法（内在的和外在的）并评估认知负荷的影响 客观测量的错误患者错误的认知负荷（使用撤回和重新排序警报）。 第二个目标，我们将前瞻性地收集 300 名医学实习生（住院医师、研究员）的数据 每个研究中心 5 个月内的儿科调查以及认知负荷。 基于 EHR 的审计日志中的措施以及研究期间的错误患者错误将用于 此外，确定认知负荷、倦怠和错误之间的中介关系。 提出的目标，我们将开发先进的机器学习算法来预测错误和倦怠 基于 EHR 的活动序列将有助于设计有针对性的干预措施。 根据医生的具体工作实践，设计评估此类干预措施的临床试验，以及 为医生的安全和福祉制定明智的政策指南。