Methods for Cleaning and Managing a Nurse-Led Registry.

Area of Science:

• Nursing Research
• Health Informatics
• Data Science

Background:

• Clinical registries are crucial for understanding patient care quality and identifying trends in diagnosis, disease, and treatment.
• There is a growing emphasis on utilizing large datasets in healthcare research.
• Nurse researchers developing data registries often lack expertise in data management.

Purpose of the Study:

• To describe a quality control and data management process for a neuroscience nursing registry.
• To provide a structured approach for nurses and collaborators to manage and clean registry data.

Main Methods:

• The study examined a neuroscience nursing registry containing over 90,000 rows of data from nearly 5,000 patients across 4 US hospitals.
• Data management followed a continuous 5-phase process: screening, data organization, diagnostic, treatment, and missing data, repeated with each update.
• Quality control involved systematic diagnosis and resolution of technical errors using statistical outputs.

Main Results:

• An interdisciplinary approach to data management yielded high-quality data, validated by missing data analysis.
• The majority of technical errors were identifiable and correctable within the source files.
• The described methods facilitated systematic error detection and correction.

Conclusions:

• The implemented data management process ensures high-quality data for clinical registries.
• This structured methodology empowers nurses and collaborators to effectively clean and manage registry data.
• The findings support the use of robust data management practices in nursing research to improve patient care insights.