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: 10504867
• 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/10504867
• 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.

项目总结 临床护理过程中的认知负荷受到许多因素的影响，包括专业， 实践设置、患者复杂性、电子健康记录(EHR)的使用以及临床医生的专业知识。在美国 在美国，临床护理主要是使用EHR记录的：记录负担、可用性差和 不必要的导航会增加认知负荷。认知负荷的这种增加，反过来， 导致工作时间更长、对工作不满意、患者结局不佳(例如，错误)、精疲力竭和糟糕 临床医生的结果。许多先前的研究表征了临床医生的认知负荷或其对差错的影响 依靠回顾方法，包括自我报告、时间运动研究和焦点小组。同样， 职业倦怠也完全是通过调查来衡量的。基于电子病历的审计日志显示了相当大的 承诺成为跟踪和测量临床活动的可行资源，而无需增量调查 给临床医生带来负担。我们的研究团队已经证明，基于审计日志的工作负载测量可以 用于评估认知负荷、职业倦怠和错误。基于这一前景看好的试点工作，主要重点是 用于表征认知负荷的IGNITE(集成实时临床活动和行为反应 和错误)研究的目的是利用基于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)临床医生职业倦怠的中介作用 解释认知负荷和错误之间的关系。我们将通过大规模的 在与华盛顿大学/北京赛马会相关的三个大型学术医疗中心进行的多点研究 医疗保健、斯坦福大学和科罗拉多大学。对于第一个目标，我们将使用基于电子病历的审计 在三个站点的非手术、住院设置中收集的日志，时间跨度为3年(2019年1月1日至 12/31/2021)制定认知负荷的测量方法--包括内在和外在的--并评估 客观测量错误患者错误的认知负荷(使用收回和重新排序警报)。对于 第二个目标是，我们将前瞻性地从医学中心收集300名受训人员(住院医生、研究员)的数据 以及来自每个研究地点的5个月的儿科。每月的职业倦怠调查，以及认知负荷 在研究期间，将使用基于电子病历的审核日志中的措施和错误患者错误 确定认知负荷、职业倦怠和错误之间的中介关系。此外，对于这两个 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工作倦怠 基于EHR的活动序列。这项研究的洞察力将有助于设计符合 与医生的背景工作实践相结合，设计临床试验以评估此类干预措施，以及 在为医生的安全和福祉制定知情的政策指南方面。