Measurement is a core foundation of quality improvement (QI), and analysis of data for QI requires distinct approaches and tools as compared with other areas of healthcare. QI efforts can use structural, process, outcome, and balancing measures, and each measure should have a clear operational definition. Data for improvement should be analyzed dynamically over time, with a focus on understanding the variation present in the data. Distinguishing between common cause and special cause variation is necessary to evaluate and guide improvement efforts. Statistical process control tools such as run charts and control charts can be powerful tools to analyze data over time and help understand variation. This article continues a series of QI educational papers in the Journal of Perinatology, and offers a review of the use of data and measures to drive improvement.

测量是质量改进 (QI) 的核心基础，与其他医疗保健领域相比，对 QI 数据的分析需要不同的方法和工具。QI 工作可以使用结构、过程、结果和平衡措施，每个措施都应该有明确的操作定义。应随着时间的推移动态分析用于改进的数据，重点是了解数据中存在的变化。区分常见原因和特殊原因变异对于评估和指导改进工作是必要的。统计过程控制工具（例如运行图和控制图）可以成为分析随时间变化的数据并帮助理解变化的强大工具。本文延续了Journal of Perinatology上的一系列 QI 教育论文，并审查数据的使用和推动改进的措施。