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We identified mortality-, age-, and sex-associated differences in relation to reference intervals (RI) for laboratory tests in population-wide data from nearly two million hospital patients in Denmark and comprising of more than 300 million measurements. A low-parameter mathematical wave-based modification method was developed to adjust for dietary and environment influences during the year. The resulting mathematical fit allowed for improved association rates between re-classified abnormal laboratory tests, patient diagnoses and mortality. The study highlights the need for seasonally modified RIs and presents an approach that has the potential to reduce over- and underdiagnosis, impacting both physician-patient interactions and EHR research as a whole.

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