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Protocol for EHR laboratory data preprocessing and seasonal adjustment using R and RStudio.

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Seasonality in laboratory data can skew diagnoses. This protocol uses R software to analyze electronic health records for seasonal patterns and adjust clinical reference intervals, improving diagnostic accuracy.

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Area of Science:

  • Clinical laboratory science
  • Biostatistics
  • Health informatics

Background:

  • Laboratory healthcare data exhibits seasonal variations, potentially leading to patient under- or overdiagnosis.
  • Accurate diagnostic reference intervals are crucial for effective clinical decision-making.

Purpose of the Study:

  • To present a reproducible protocol for analyzing seasonality in laboratory healthcare data.
  • To provide methods for adjusting existing reference intervals based on identified seasonal patterns.
  • To leverage electronic health record (EHR) data for robust seasonal analysis.

Main Methods:

  • Preprocessing of population-wide patient laboratory data into a unified dataset.
  • Definition of relevant strata for seasonal analysis.
  • Normalization of data to the median and fitting to selected statistical functions using R software.

Main Results:

  • The protocol enables the identification and quantification of seasonal trends within laboratory test results.
  • Methods are detailed for adjusting reference intervals to account for seasonality.
  • The approach facilitates more accurate interpretation of laboratory data throughout the year.

Conclusions:

  • Analyzing seasonality in laboratory data is essential for mitigating diagnostic errors.
  • This R-based protocol offers a standardized method for adjusting reference intervals, enhancing diagnostic precision.
  • Implementing these adjustments can improve patient care by ensuring more accurate diagnoses.