A Generalized Tool to Assess Algorithmic Fairness in Disease Phenotype Definitions.

For evidence from observational studies to be reliable, researchers must ensure that the patient populations of interest are accurately defined. However, disease definitions can be extremely difficult to standardize and implement accurately across different datasets and study requirements. Furthermore, in this context, they must also ensure that populations are represented fairly to accurately reflect populations' various demographic dynamics and to not overgeneralize across non-applicable populations. In this work, we present a generalized tool to assess the fairness of disease definitions by evaluating their implementation across common fairness metrics. Our approach calculates fairness metrics and provides a robust method to examine coarse and strongly intersecting populations across many characteristics. We highlight workflows when working with disease definitions, provide an example analysis using an OMOP CDM patient database, and discuss potential directions for future improvement and research.