Methods for Cleaning and Managing a Nurse-Led Registry

Aardhra M. Venkatachalam, Anjali Perera, Sonja E. Stutzman, Dai Wai M. Olson, Venkatesh Aiyagari, Folefac D. Atem

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)328-332
Number of pages5
JournalJournal of Neuroscience Nursing
Volume52
Issue number6
DOIs
StatePublished - Dec 1 2020

Keywords

  • clinical registries
  • data cleaning, data management
  • data quality
  • missing data, nursing
  • pupillometry
  • statistical analysis

ASJC Scopus subject areas

  • Surgery
  • Endocrine and Autonomic Systems
  • Clinical Neurology
  • Medical–Surgical

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