Measuring NICU Nurse Practitioner Workload in Real-time to Improve Care Quality and Patient Safety

实时测量 NICU 护士从业人员的工作量，以提高护理质量和患者安全

基本信息

批准号：
10736277
负责人：
DANIEL Joseph FRANCE
金额：
$ 68.95万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-15 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10736277
关键词：
Academic Medical Centers Address Affect COVID-19 pandemic Caring Collaborations Communication Complex Critical Illness Data Data Element Development Diagnostic Dimensions Dose Electronic Health Record Engineering Environmental Risk Factor Evaluation Studies Event Focus Groups Foundations Funding Future Growth Health system Healthcare Hospitals Hour Human Human Engineering Iatrogenesis Incidence Interruption Intervention Interview Job Satisfaction Learning Maps Measurement Measures Mechanical ventilation Medication Errors Modeling National Institute of Child Health and Human Development Neonatal Neonatal Intensive Care Units Neonatal Nursing Nosocomial Infections Nurse Practitioners Nurses Observational Study Occupations Organ Patient Care Patient Self-Report Patient-Focused Outcomes Patients Performance Perioperative Personal Satisfaction Physiology Play Process Provider Psychological Factors Public Health Quality of Care Recommendation Research Research Personnel Resource Allocation Risk Role Safety Severities Shapes Structure Surveys System Time Universities Weight Work Workload clinical decision support clinical practice design environmental stressor experience health information technology human centered design improved innovation microsystems mortality multidisciplinary multilevel analysis neonatal care neonatal patient neonate patient safety professional atmosphere prospective safety outcomes support tools tool validation studies

项目摘要

PROJECT SUMMARY High provider workload is a threat to care quality, patient safety, and providers’ well-being and job satisfaction. Workload – which lacks a universally accepted definition - is a complex multi-dimensional construct that is affected by external task demands and environmental, organizational, and psychological factors. The importance of managing high workload is nowhere more evident than in neonatal intensive care units (NICUs). Critically ill neonates are highly vulnerable to iatrogenic events due to their immaturity and fragility, and high clinician workload has been directly associated with increased incidence of adverse neonatal safety outcomes. Despite the evidence and need, patient safety researchers have been slow to develop multi-level models, scalable workload measurement systems, or other health information technology interventions to improve workload management and patient safety. Conventional workload management tools predominantly measure and predict workload using unit-level (e.g., staffing ratios) or patient-level (e.g., acuity) data rather than data collected across the four levels of workload recommended by human factors engineers (HFEs) - unit, job, patient, and situation. As a result, current tools under-measure the workload experienced by providers and are not designed to identify mutable microsystem factors that contribute most to provider workload. A promising development in workload research is the increasing emphasis on measuring situational workload which best explains the workload experienced by clinicians due to healthcare microsystem design. Situational workload is most affected by performance obstacles (i.e., delays, interruptions, etc.) in the local work environment and can be applied at the unit, job, or patient-levels. Most importantly, it is diagnostic of underlying contributory factors and therefore actionable for improvement. To date, situational workload has been measured using subjective surveys which are work-interrupting, thus difficult to integrate into practice. Vanderbilt University Medical Center (VUMC), in collaboration Johns Hopkins University (JHU), will employ a systems engineering human-centered design process to design, develop, and validate new multi-level model of NICU nurse practitioner workload derived from readily accessible electronic health record (EHR) data. The validated model will be the foundation for a future EHR-based clinical decision support (CDS) tool that will track the real-time workload of NICU providers, predict near- term future unit workload, and guide workload reduction and balancing interventions. The project’s three Specific Aims are: Aim 1. To conduct a comprehensive HFE-based analysis of NICU provider (i.e., neonatal nurse practitioner) workload; Aim 2. To design and develop real-time multivariable workload models and Aim 3. To validate the real-time workload models at VUMC (A) and to determine the generalizability of the models at an external hospital (B).

项目概要提供者的高工作量对护理质量、患者安全以及提供者的福祉和工作构成威胁工作量满意度——缺乏普遍接受的定义——是一个复杂的多维度的问题。受外部任务需求以及环境、组织和心理影响的构建管理高工作量的重要性在新生儿重症监护中最为明显。危重新生儿由于其不成熟和不成熟而极易发生医源性事件。脆弱性和临床医生工作量大与新生儿不良事件发生率增加直接相关安全结果。尽管有证据和需求，患者安全研究人员在开发多层次模型方面进展缓慢，可扩展的工作量测量系统，或其他健康信息技术干预措施，以改善传统的工作量管理工具主要衡量工作量管理和患者安全。并使用单位级别（例如，人员配置比率）或患者级别（例如，敏锐度）数据而不是数据来预测工作量收集人为因素工程师 (HFE) 推荐的四个工作量级别 - 单位、工作、因此，当前的工具低估了提供者所经历的工作量，并且并非旨在识别对提供商工作量影响最大的可变微系统因素。工作负荷研究的一个有希望的发展是越来越重视测量情境工作量最能解释讲师由于医疗保健微系统设计而经历的工作量。情境工作负载受本地性能障碍（即延迟、中断等）的影响最大。工作环境，可以应用于单位、工作或患者层面。最重要的是，它可以诊断。迄今为止，情景工作量已经成为潜在的影响因素，因此可以采取行动进行改进。使用主观调查进行测量，这会干扰工作，因此难以融入实践。范德比尔特大学医学中心 (VUMC) 与约翰霍普金斯大学 (JHU) 合作，将采用以人为本的系统工程设计流程来设计、开发和验证 NICU护士从业人员工作量的新多级模型源自易于访问的电子设备健康记录 (EHR) 数据经过验证的模型将成为未来基于 EHR 的临床的基础。决策支持 (CDS) 工具将跟踪 NICU 提供者的实时工作量，预测近期情况术语未来单位工作量，并指导工作量减少和平衡项目的干预措施。三个具体目标是：目标 1. 对 NICU 提供者进行基于 HFE 的全面分析（即新生儿执业护士）工作量；目标 2. 设计和开发实时多变量工作负载模型和目标 3. 验证 VUMC (A) 的实时工作负载模型并确定外部医院模型的普遍性 (B)。