Claudia Sessa (Carlo Cattaneo LIUC U Carlo Cattaneo LIUC U) et al. have posted “Identifying Bias in Data Collection: A Case Study on Drugs Distribution” on SSRN. Here is the abstract:
A critical aspect of modern healthcare involves recognizing and addressing pharmaceutical needs. Predictive models serve as valuable decision-making tools in the healthcare sector to proactively prevent supply chain failures. However, training these models on real historical data to reliably reflect actual demand is a delicate process. An effective model, capable of reliably estimating the amount of drugs to be distributed in relation to the patient’s needs, must be accurate and inherently fair. Our study endeavors to bridge legal perspectives on fairness with practical assessments of algorithmic fairness, specifically in the context of predicting drugs to be distributed in the specific area of reference. We provide an in-depth overview of the Italian National Healthcare Service, emphasizing its regulatory role in drug dispensation and its inherent challenges. Furthermore, we delve into fundamental bias research principles, encompassing legal and statistical viewpoints. In addition, we present a comprehensive Exploratory Data Analysis using real-world data to highlight challenges encountered in the initial modeling phase. Our analysis unveils the presence of crucial missing data fields and disparities in medication utilization between genders, potentially indicative of social bias. These findings contribute to an in-depth understanding of patient populations concerning drug collection. Importantly, our study promotes a comprehensive approach that incorporates legal considerations and technical elements to improve the fairness and efficacy of predictive models in healthcare.