Regular and Off-Shift Nursing: Impacts on Patient Outcomes and Cost of Care

对退伍军人医疗保健的预期影响：从这项研究中获得的知识将有助于 整个退伍军人管理局医疗系统的医院人员配备做法、护理交付和患者结局。为了确保 为了将我们的调查结果传播并转化为有效的退伍军人政策，我们聘请了一名高级顾问 代表重要利益相关者和思想领袖的小组，包括来自 弗吉尼亚州，以及来自联合委员会和医疗保健改进研究所的代表。 项目背景：尽管有越来越多的证据表明 在普通急性护理环境中，在RN人员配备水平和高质量的患者结果之间，仍然存在 重大的科学知识差距。例如，医院提供每周7天、24小时的服务，而且有 有证据表明，非轮班(夜间、周末和节假日)入院的患者预后更差。然而， 几乎没有研究对轮班护士人员配置之间的关系进行过研究 和病人的结果。由于之前数据集的限制，研究人员无法解释 单位一级患者需求的变化，也没有考虑到其他重要的劳动力 教育和终身教职等特点。此外，大多数护士人员配备研究都是交叉的- 分段的，这限制了推断因果关系的能力。最后，很少有研究探讨经济上的 与护士人员配置相关的考虑。我们刚刚完成了一个项目，该项目利用了详细的VA 数据系统来解决其中一些限制。具体地说，我们使用了一个为期4年的月度、单位级别的小组 所有退伍军人管理局急性护理病房的数据，以检查护士人员配备对不良患者事件和持续时间的影响 留下来(洛杉矶)。通过集成退伍军人管理局的工资数据，我们能够超越仅仅是人员配备水平，以检查 合同制护士的作用、护理人员的特点(学历、退伍军人任期等)和稳定性 护理团队的成员。 项目目标：此项目的目的是扩展我们当前的研究，以检查 定期和非轮班护士人员配置的差异会影响患者的预后，并进行详细的 研究增加护士人员配备的成本和收益之间的权衡。现有数据集 我们已创建集成了来自所有退伍军人管理局急性护理医院的患者、会计和工资数据 03-06财年国家/地区。我们建议在2011财年将其扩展，以获取DSS医疗成本数据，并开发 与轮班护士人员配置相关的指标，创造最全面的护士人力资本图景 以及在美国可获得的患者结果。我们还将把该模型扩展到对照治疗 医生。使用这个数据集，在接下来的两年里，我们的目标是1)检查 护理投入的定期轮班和非轮班(人员配备水平、技能组合、合同制护士和综合设施- 特定的、单位/团队特定的人力资本)影响护理敏感的患者结果和2)分析效率 在护理服务中，通过研究护理人员成本和通过 减少对护理敏感的不良事件和LOS。 项目方法：我们将使用固定效果模型来获得最新和最全面的 检查护士人员配备战略以及对重要护理敏感患者结局的影响(例如， 卧位溃疡、抢救失败)和洛杉矶。将针对单元类型(ICU与其他)单独控制人员配置 急症护理病房)。除了控制护士人员配置和护理团队的特点外，我们还 将包括一些变量，以捕捉非轮班护士与常规轮班护士的不同之处。要计算 在各种人员配备策略的成本方面，我们将使用人力资本影响变量的估计 并将这些数据链接到关联的工资数据。我们将使用以下成本重新运行模型 将患者遭遇作为因变量来估计潜在的成本节约。

Anticipated Impact on Veterans Health Care: The knowledge gained from this study will help to inform hospital staffing practices, nursing delivery and patient outcomes across the VA healthcare system. To ensure dissemination and translation of our findings into effective VA policies, we have recruited a high-level advisory group representing important stakeholders and thought leaders including the Chief Nursing Officer from the VA, and representatives from both the Joint Commission, and the Institute of Healthcare Improvement. Project Background: Although there is increasing evidence that establishes the positive relationship between the level of RN staffing and quality patient outcomes in general acute care settings, there are still significant scientific knowledge gaps. For example, hospitals provide 24-hour, 7-day a week service and there is evidence that patients admitted on off-shifts (nights, weekends, and holidays) have worse outcomes. However, there is almost no research that has been conducted examining relationships between off-shifts nurse staffing and patient outcomes. Due to limitations in previous datasets, researchers have not been able to account for variation in patient needs at the unit-level, nor have they been able to consider other important workforce characteristics such as education and tenure. Furthermore, most nurse staffing studies have been cross- sectional, which limits the ability to infer causality. Finally, very few studies have explored the economic considerations related to nurse staffing. We are just completing a project that has exploited the detailed VA data systems to address some of these limitations. Specifically, we used a 4-year panel of monthly, unit-level data for all VA acute care units to examine the effects of nurse staffing on adverse patient events and length of stay (LOS). By integrating VA payroll data, we were able to move beyond just staffing levels to examine the effects of contract nurses, the characteristics of the nursing staff (education, VA tenure, etc.), and the stability of the nursing teams. Project Objectives: The purpose of this project is to expand on our current research to examine how the differences in nurse staffing between regular and off-shifts affect patient outcomes, and to conduct a detailed examination of the trade-offs between the costs and benefits of increased nurse staffing. The existing dataset we have created integrates patient, accounting, and payroll data from all VA acute care hospitals across the nation for FY 03-06. We propose to expand this through FY11, to pull DSS cost of care data, and to develop indicators related to off-shift nurse staffing to create the most comprehensive picture of nurse human capital and patient outcomes available in the United States. We will also extend the model to control for treating physician. Using this dataset, in the next two years our aims are 1) examine how the differences between regular shifts and off-shifts in nursing inputs (staffing levels, skill mix, contract nurses, and general, facility- specific, unit/team-specific human capital) effect nursing-sensitive patient outcomes and 2) analyze efficiency in nursing services by studying the trade-offs between nursing personnel costs and cost savings from a reduction in nursing-sensitive adverse events and LOS. Project Methods: We will use fixed effects models to obtain the most current and comprehensive examination of nurse staffing strategies and the impact on important nursing sensitive patient outcomes (e.g., decubitus ulcers, failure-to-rescue) and LOS. Staffing will be controlled separately for unit type (ICUs vs. other acute care units). In addition to controlling for nurse staffing and the characteristics of the nursing team, we will include variables to capture how the nurse staffing on the off-shifts differs from regular shifts. To compute the cost of various staffing strategies, we will use the estimates of the effects of the human capital variables from the previous aim 1 and link these data to associated wage data. We will re-run models using the cost of patient encounters as the dependent variable to estimate the potential cost savings.