Providing Safe Care to Older Hospitalized Patients through Discrete Event Simulation Modeling

项目摘要 许多医院的病人，特别是老年病人，都依赖于工作人员的帮助活动 日常生活（ADL），如步行和散步。协助开展这些活动对工作人员来说很费时， 有证据表明，ADL援助并不总是在医院提供。这种不一致的ADL 护理通常与不良事件有关，特别是福尔斯，30-60%的医院福尔斯与患者的 试图使用浴室。有证据表明，持续的运动和行动援助可以防止 福尔斯，但与人员配备和复杂的医院工作环境有关的障碍使其难以提供这一服务 在日常急性护理实践中持续护理。 美国国家医学院已经确定了一种称为离散的系统工程方法 事件模拟（DES）作为一种方法，以提高医疗保健的质量和效率。这种方法有 在其他行业中使用了几十年，最近应用于医疗保健环境。离散事件 模拟在数学上对定义护理环境的多个工作参数进行建模，例如， 提供一段护理，并预测该环境中工作人员在不同点提供护理的能力 根据其他竞争需求和护理要求及时提供。敏感性和预测分析可以 用于检查系统的变化，如不同的工作人员时间表或护理程序的顺序， 并改善护理提供，从而促进更有效的管理。 该调查小组已将DES应用于养老院设置，以确定人员配置需求， 提供基于一系列居民ADL依赖性和人力资源的护理。然而，这个DES 该方法尚未应用于急性护理环境，其中许多老年患者需要类似的ADL护理 服务，也面临着福尔斯的风险。因此，本研究的目的是将DES应用于医院 设置集中在照顾大量具有ADL依赖性的老年患者的单位。 这项研究将产生一种新的技术，医院供应商可以用来有效地管理 医疗单位通过试验创新的方法，以改善ADL护理之前，更昂贵的 努力在实际中实施和检验干预措施。这项技术将首先进行开发和测试 老年人急性护理（ACE）然而，模型准确描述护理的程度 将在另外两个不同的医院单位进行测试，为向其他单位传播该模型做准备， 医院通过网络模拟工具。

Project Summary Many hospital patients, particularly older patients, are dependent on staff for assistance with activities of daily living (ADL) such as walking and toileting. Assistance with these activities is time-consuming for staff, and evidence suggests that ADL assistance is not consistently provided in hospitals. This inconsistent ADL care is often associated with adverse events, particularly falls, with 30-60% of hospital falls related to patients’ attempts to use the bathroom. There is evidence that consistent toileting and mobility assistance can prevent falls, but barriers related to staffing and the complex hospital work environment make it difficult to provide this care consistently in daily acute care practice. The National Academy of Medicine has identified a systems-engineering approach called Discrete Event Simulation (DES) as a method to improve the quality and efficiency of health care. This approach has been used for decades in other industries and more recently applied to healthcare settings. Discrete Event Simulation mathematically models multiple work parameters that define a care setting, such as the time to provide an episode of care, and predicts the ability of staff within that setting to provide care at different points in time based on other competing demands and care requirements. Sensitivity and predictive analyses can be used to examine how changes to the system, such as different staff schedules or order of care routines, affect and improve care delivery thus facilitating more efficient management. This investigative team has applied DES to the nursing home setting to identify staffing needs to provide care based on a range of resident ADL dependencies and staffing resources. However, this DES methodology has not been applied to the acute care setting, where many older patients need similar ADL care services and also are at risk for falls. Thus, the purpose of the proposed study is to apply DES to the hospital setting focused on units that care for large numbers of older patients with ADL dependencies. This study will produce a novel technology that hospital providers may use to efficiently manage medical units by experimenting with innovative approaches to improve ADL care prior to more expensive efforts to implement and test interventions in actual practice. This technology will be first developed and tested in an Acute Care for the Elderly (ACE) unit. However, the degree to which the model accurately describes care in two other diverse hospital units will be tested in preparation for disseminating the model to other units and hospitals via a web-based simulation tool.