Understanding the relationship between nurse staffing and outcomes: impact of individual nurse education, expertise, and effort level on individual patient outcomes

背景：越来越多的证据表明，护士人员配置(调整后的护士与病人的比例) 用于患者敏锐度/护理需求)和诸如经验的护士的特征， 受教育程度、在特定单位工作的年限会影响患者的预后。除了几个较小的， 单点研究，这些调查没有将患者与直接护理患者的护士配对 在每个班次为他们准备的。以前的研究只能产生有限的 建议，例如建议的最低人员配置比例或增加资源调整员的百分比 拥有学士学位。关于个别护士-病人是如何 任务分配会影响患者的治疗结果，因此我们无法开发智能人员配备方法 根据每个病人的需要量身定做。 意义：将个别病人与每次轮班直接照顾他们的护士配对 解决目前在理解个别护士-病人分配如何影响方面的差距 病人的结果。控制其他已知的影响护士人员配置关系的因素 以及患者的结果，如患者的敏锐度和单位和医院的特点。 创新和影响：这个创新项目将是第一个大规模研究 将护士与病人联系起来的护士人员配置。它将为退伍军人管理局有效地提供必要的信息 使用Cerner电子病历的Clarvia组件中的护士人员配置工具。 具体目标：目标1：审查护士人员配备水平协会和 住院退伍军人的个体护士特征和结局。独立审查 以及护士个人特点(例如，教育程度和经验)与 分配给这些护士直接护理的患者的结果(住院死亡率、未能抢救、 医院获得性感染和风险调整的住院时间(LOS))。 目的2.测试护士人员配置和护士特征与病人之间的关系 结果会因不同的单位轮班情况而改变。护士不会照顾在医院的病人 与世隔绝；每班工作的护士作为一个团队工作，经常相互帮助；我们将测试如何 这种团队合作的效果因单位每个班次的人员配备水平而有所不同， 每个班次和单位工作人员的特征，通过患者的敏锐度/需要来衡量 护理和病人吞吐量(入院和出院)。 目标3：我们将与护理服务办公室合作，将项目结果提交给Key 利益相关者促进将项目结果转化为对护士的建议- 促进住院退伍军人获得高质量结果的患者分配。 方法：弗吉尼亚州2010-2023年护士人员配置数据，包括每个护士的特征(例如， 经验)将与每个患者可用的广泛VA临床数据相匹配 TIU护理评估摘录，必须由护理每个患者的护士填写 换档。将使用多水平回归模型来估计两者之间的关联 护理每个病人的护士的特点(例如，教育)，护士的人员配置水平 每名患者和住院退伍军人的结局。这将产生对 护理投入的影响，以及这种影响如何随每个护士的特点而变化，例如 更有经验的护士可以安全地处理更大的工作量)。 下一步/实施：该项目将与我们的退伍军人事务部密切合作 护理服务合作伙伴，以最大限度地发挥为退伍军人提供护理的潜在利益。

Background: There is a growing body of evidence that nurse staffing (nurse to patient ratio, adjusted for patient acuity/need for nursing care) and characteristics of the nurse such as experience, education level, tenure working on specific unit, affects patient outcomes. Except for a few smaller, single site studies, these investigations have not matched patients with the nurses that directly cared for them during each shift. Previous studies have only been able to produce limited recommendations, such as suggested minimum staffing ratios or to increase the percentage of RNs with baccalaureate degrees. Virtually nothing is known about how individual nurse-patient assignments impact patient outcomes, thus precluding us from developing smart staffing approaches tailored to the needs of each patient. Significance: Match individual patients to the nurses who directly cared for them each shift to address the current gap in the understanding of how individual nurse-patient assignments affect patient outcomes. Controlling for other factors known to affect the relationship between nurse staffing and patient outcomes such as patient acuity and unit- and hospital-level characteristics. Innovation and Impact: This innovative project will be the first large-scale study of the effects of nurse staffing that links nurses to patients. It will provide the information needed for VA to effectively utilize the nurse staffing tools in the Clarvia component of the Cerner electronic medical record. Specific Aims: Aim 1: Examine the joint effects of the association of nurse staffing levels and individual nurse characteristics and outcomes for hospitalized Veterans. Examine independent and joint associations of individual nurses’ characteristics (e.g., education and experience) with the outcomes of the patients assigned to these nurses’ direct care (in-hospital mortality, failure to rescue, hospital-acquired infections, and risk-adjusted length of stay (LOS)). Aim 2. Test how the associations of nurse staffing and nurse characterstings with patient outcomes are modified by varying unit-shift circumstances. Nurses don’t care for patients in isolation; the nurses working each shift work as a team and often help each other; we will test how the effects of this teamwork vary by the staffing levels each shift for the unit as a whole, the characteristics of the staff each shift, and the unit work, measured by the patient acuity/need for nursing care and the patient throughput (admissions and discharges). Aim 3: We will work with the Office of Nursing Services to present the project findings to key stakeholders to facilate the translation of project findings into recommendations for nurse- patient assignments that promote high quality outcomes for hospitalized Veterans. Methodology: VA 2010-2023 nurse staffing data, including the characteristics of each nurse (e.g., experience) will be matched with the extensive VA clinical data available for each patient using the TIU Nursing Assessment extract that must be completed by the nurse caring for each patient, each shift. Multilevel regression models will be used to estimate the association between the characteristics of the nurses (e.g., education) who cared for each patient, the nurse staffing level for each patient and the outcomes for hospitalized Veterans. This will yield both precise estimates of the effects of nursing input and how this varies by the characteristics of each nurse e.g., the extent that more experienced nurses can safely manage larger workloads). Next Steps/Implementation: This project will be conducted in close collaboration with our VA Office of Nursing Services partners to maximize the potential benefits for the care for Veterans.