Prevention of Surgical Site Infection using Statistical Process Control Charts

使用统计过程控制图预防手术部位感染

• 批准号: 9302308
• 负责人: Deverick John Anderson
• 金额: $ 49.43万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9302308
• 关键词: Prevention; Surgical; Site; Infection; using

 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.

 描述（由申请人提供）：手术部位感染 (SSI) 是美国最常见且成本最高的医疗保健相关感染。虽然大多数医院大大提高了对重要流程措施的遵守情况，但遵守情况的提高并没有导致 SSI 发生率下降。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预防 SSI 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，而且速度很慢。 SSI 发生率的变化（包括反映疫情爆发的变化）通常在发生率首次变化几个月后才被检测到。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“常见原因”自然变化或由于过程中先前未固有的情况而导致的“特殊原因”非自然变化。换句话说，SPC 方法有助于将真实信号与“噪声”分开。该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，我们将使用多中心集群随机试验设计（具体目标 2）来确定这种方法是否可以降低手术部位感染率。这 该提案将利用杜克大学感染控制外展网络（DICON）和医疗系统工程研究所（HSyE）研究人员之间独特、创新且先前成功合作的优势。这项合作研究将结合多项计划优势，包括 SSI 方面的专业知识、SPC 理论及其在患者安全问题上的应用方面的专业知识，以及用于执行多中心试验的现有且成功的平台。我们的中心假设是，通过优化的 SPC 方法以及即时反馈来快速识别 SSI 率的增加将导致 SSI 率的下降。拟议的研究具有创新性，因为它代表了对 SSI 监视和反馈现状的新的实质性偏离。这项研究的贡献将是巨大的，因为它将带来降低 SSI 发生率的新策略，从而改善美国人口的健康和安全。