Seasonality in laboratory healthcare data is associated with possible under- and overdiagnoses of patients in the clinic. Here, we present a protocol to analyze electronic health record data for seasonality patterns and adjust existing reference intervals for these changes using R software. We describe steps for preprocessing population-wide patient laboratory data into a single dataset. We then detail steps for defining strata, normalizing to median, and fitting data to selected functions. For complete details on the use and execution of this protocol, please refer to Muse et al. (2023).¹