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Keywords

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

 

Authors

  1. Venkatachalam, Aardhra M.
  2. Perera, Anjali
  3. Stutzman, Sonja E.
  4. Olson, DaiWai M.
  5. Aiyagari, Venkatesh
  6. Atem, Folefac D.

Abstract

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.