The Times-Divide Expression: An Intuitive Approach for Describing Right-Skewed Data in Nursing Practice

Area of Science:

• Nursing Informatics
• Biostatistics
• Data Science in Healthcare

Background:

• Clinical nursing practice requires clear data sharing for team collaboration.
• Traditional descriptive statistics (Mean ± SD) are insufficient for asymmetric, right-skewed data common in nursing.
• Accurate data representation is vital for effective clinical decision-making.

Purpose of the Study:

• To introduce and evaluate the "times-divide expression" (Geometric Mean ×/Geometric Standard Deviation) as an alternative to Mean ± SD.
• To assess the applicability of the times-divide expression for analyzing nursing data, specifically nurse call system data.
• To demonstrate the advantages of the times-divide expression in representing skewed data distributions.

Main Methods:

• A data-driven study using four and a half years of nursing demand data from a nurse call system.
• Comparison of the "times-divide expression" (GMean ×/GSD) with conventional statistical methods (Mean ± SD).
• Evaluation of the methods' ability to represent distributional properties, provide representative values, and indicate percentile ranges.

Main Results:

• The "times-divide expression" significantly outperforms conventional methods in describing skewed data distributions.
• This novel expression provides more suitable representative values and usable percentile range indicators for nursing data.
• The geometric mean and geometric standard deviation offer a superior approach for analyzing asymmetric data.

Conclusions:

• The "times-divide expression" is a more effective statistical tool for analyzing skewed nursing data compared to traditional methods.
• Implementing this method can enhance nurses' understanding of data-driven insights.
• Improved data representation is expected to enhance clinical decision-making and the overall quality of patient care.