Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine

The diagnosis of diseases is a complex process involving the integration of multiple parameters obtained from various sources, including laboratory findings. The interpretation of laboratory data is inherently comparative, necessitating reliable references for accurate assessment. Different types of references, such as reference intervals, decision limits, action limits, and reference change values, are essential tools in the interpretation of laboratory data. Although these references are used to interpret individual laboratory data, they are typically derived from population data, which raises concerns about their reliability and consequently the accuracy of interpretation of individuals' laboratory data. The accuracy of diagnosis is critical to all subsequent steps in medical practice, making the estimate of reliable references a priority. For more precise interpretation, references should ideally be derived from an individual's own data rather than from population averages. This manuscript summarizes the current sources of references used in laboratory data interpretation, examines the references themselves, and discusses the transition from population-based laboratory medicine to personalized laboratory medicine.