Prevention of Surgical Site Infection using Statistical Process Control Charts

 描述（由申请人提供）：手术部位感染（SSI）是美国最常见和最昂贵的医疗保健相关感染。虽然大多数医院已经大大提高了对重要流程措施的依从性，但依从性的提高并没有导致SSI发生率的降低。因此，非常需要预防SSI的创新策略。将SSI数据反馈给手术人员是SSI预防的基石，但传统的反馈方法需要随着时间的推移汇总测量结果，并且速度很慢。SSI发病率的变化，包括反映疾病暴发的变化，通常在发病率首次变化后几个月才被发现。统计过程控制（SPC）是统计学的一个分支，它将时间序列分析方法与数据的图形表示相结合，以确定过程的当前变化是否代表“共同原因”的自然变化或“特殊原因”的非自然变化，这些变化是由于过程中以前没有固有的环境造成的。换句话说，SPC方法有助于将真实信号与“噪声”分离。“这项提案的总体目标是确定SPC方法预防SSI的临床有效性。首先，我们将优化SPC方法，以确定手术部位感染率的趋势，并向外科医生提供反馈（具体目标1）。然后，我们将使用多中心随机分组试验设计（特定目标2）来确定这种方法是否降低了手术部位感染的发生率。这 该提案将利用杜克感染控制外展网络（DICON）和医疗系统工程研究所（HSyE）的研究人员之间独特、创新和先前成功合作的优势。这项合作研究将结合联合收割机的几个项目优势，包括SSI的专业知识，SPC理论的专业知识及其在患者安全问题中的应用，以及一个预先存在的和成功的多中心试验平台。我们的中心假设是，通过优化SPC方法结合即时反馈快速识别SSI发生率的增加将导致SSI发生率的降低。拟议的研究是创新的，因为它代表了一个新的和实质性的偏离SSI的监督和反馈的现状。这项研究的贡献将是显著的，因为它将导致一个新的战略，以降低SSI的发生率，从而改善美国人口的健康和安全。

 DESCRIPTION (provided by applicant): Surgical site infections (SSIs) are the most common and most costly healthcare-associated infections in the US. While most hospitals have greatly improved compliance with important process measures, increased compliance has not led to decreased rates of SSI. As a result, innovative strategies to prevent SSI are greatly needed. Feedback of SSI data to surgical personnel is a cornerstone of SSI prevention, but traditional feedback methods require aggregation of measurements over time and are slow. Changes in rates of SSI, including those reflecting outbreaks, often are detected several months after the rate first changed. Statistical process control (SPC) is a branch of statistics that combines time series analysis methods with graphical presentation of data to determine whether the current variation of a process represents "common cause" natural variation or "special cause" unnatural variation due to circumstances that have not previously been inherent in the process. In other words, SPC methods help separate a true signal from "noise." The overall objective of this proposal is to determine the clinical effectiveness of SPC methods to prevent SSI. First, we will optimize SPC methods to identify trends in rates of surgical site infections and provide feedback to surgeons (Specific Aim 1). We will then determine if this approach decreases the rates of surgical site infections using a multicenter cluster randomized trial design (Specific Aim 2). This proposal will capitalize on the strengths of a unique, innovative, and previously successful collaboration between investigators in the Duke Infection Control Outreach Network (DICON) and the Healthcare Systems Engineering Institute (HSyE). This collaborative investigation will combine several programmatic strengths, including expertise in SSI, expertise in SPC theory and its application to patient safety issues, and a pre-existing and successful platform for performing multicenter trials. Our central hypothesis is that rapid identification of increases of rates of SSI through optimized SPC methods coupled with immediate feedback will lead to decreases in the rates of SSI. The proposed research is innovative because it represents a new and substantive departure from the status quo of SSI surveillance and feedback. The contribution of this study will be significant because it will lead to a new strategy to decrease rates of SSI, thereby improving the health and safety of the US population.