Methods for Cleaning and Managing a Nurse-Led Registry
Methods for Cleaning and Managing a Nurse-Led Registry
复制标题
清洁和管理护士主导的登记处的方法
DOI：
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发表时间：
2020
影响因子：
2.3
通讯作者：
F. Atem
中科院分区：
文献类型：
--
作者：
Aardhra M. Venkatachalam;A. Perera;Sonja E. Stutzman;Daiwai M. Olson;V. Aiyagari;F. Atem
文本中提供了补充的数字内容。摘要背景：临床登记处通过提供数据来识别诊断、疾病和治疗中的关联和模式，从而提供对患者护理质量的洞察。这导致了在医疗研究中使用大型数据集的推动。护士研究人员正在开发数据注册表，但大多数人不知道如何管理数据注册表。本文审查了神经科学护理注册，以描述质量控制和数据管理过程。数据质量流程：我们的注册表包含来自4家美国医院近5000名患者的90000多行数据。数据管理是一个连续的过程，包括5个阶段：筛查、数据组织、诊断、治疗和丢失数据。每次更新注册表时，都会重复这些阶段。讨论：数据管理的跨学科方法产生了高质量的数据，缺失数据分析证实了这一点。大多数技术错误可以使用基本统计输出进行系统诊断和解决，并在源文件中修复。结论：所描述的方法为护士及其合作者提供了一种结构化的方式来清理和管理注册表。
Supplemental digital content is available in the text. ABSTRACT BACKGROUND: Clinical registries provide insight on the quality of patient care by providing data to identify associations and patterns in diagnosis, disease, and treatment. This has led to a push toward using large data sets in healthcare research. Nurse researchers are developing data registries, but most are unaware of how to manage a data registry. This article examines a neuroscience nursing registry to describe a quality control and data management process. DATA QUALITY PROCESS: Our registry contains more than 90 000 rows of data from almost 5000 patients at 4 US hospitals. Data management is a continuous process that consists of 5 phases: screening, data organization, diagnostic, treatment, and missing data. These phases are repeated with each registry update. DISCUSSION: The interdisciplinary approach to data management resulted in high-quality data, which was confirmed by missing data analysis. Most technical errors could be systematically diagnosed and resolved using basic statistical outputs, and fixed in the source file. CONCLUSION: The methods described provide a structured way for nurses and their collaborators to clean and manage registries.
是时候停止在地毯下进行数据清理了吗？
影响因子：
3.7
作者：
Woolley C
通讯作者：
Woolley C