Identifying Bias in Data Collection: A Case Study on Drugs Distribution

• Main Authors: Sessa, Claudia, Gallese, Chiara, Schettini, Fabrizio, Bellavia, Daniele, Asperti, Federica, Falletti, Elena
• Format: Conference Proceeding
• Language: English
• Subjects: algorithmic fairness; Analytical models; bias; data protection; Decision making; drug administration; Drugs; Law; Medical services; nondiscrimination; Predictive models; statistical analysis; Training
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN60899.2024.10650972

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 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 a specific studied area of reference. An in-depth overview of the Italian National Healthcare Service is provided, emphasizing its regulatory role in drug dispensation and its inherent challenges. Furthermore, a review of fundamental bias research principles is provided, encompassing legal and statistical viewpoints. In addition, a comprehensive Exploratory Data Analysis is conducted using real world data, to highlight challenges that can be encountered in the initial modeling phase. The results of the analysis reveal the presence of missing values in some of the most relevant fields, and signal differences in the drugs distribution patterns between the two genders for specific therapeutic groups. Such disparities would require further investigations to verify the presence 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.