OBJECTIVES: The objective of this study was to use data from administrative databases in Emilia Romagna to identify potential delivery points (DPs) for oral oncological drugs, focusing on the Romagna area. The objective was to assess the burden of travel and the expected number of accesses for each identified point, aiming to improve the distribution of DPs and optimize patient care.

METHODS: The researchers applied a k-means clustering algorithm using the sklearn Python library to analyze oral oncological drug delivery data from 2019 to 2021. The algorithm was used to identify potential DPs throughout the Romagna area and study the burden of travel. The distance travelled from each municipality to the DP was calculated using geographical distance.

RESULTS: The study revealed significantly reduced distances between patients' residences and the DPs. With the identification of 4 DPs, a patient’s maximum distance to travel was approximately 45 kilometers. This distance was further reduced to about 24 kilometers with 10 DPs, and 14 kilometers with 20 DPs. The optimal case identified 10 DPs located in various municipalities, including Forlì, Cesena, Ravenna, Rimini - San Giuliano mare, Misano Adriatico, Lugo, Sant'Arcangelo di Romagna, Faenza, Perticara, and Santa Sofia.

CONCLUSIONS: The findings suggest that improving the distribution of DPs for oral oncological drugs can positively impact on various aspects of patient care. Decreasing travel time and distance can significantly reduce the burden on patients, their families and caregivers. Additionally, optimizing DPs can decrease CO² emissions and traffic accidents and provide more spare time for patients' families and work commitments. Furthermore, a better geographical distribution of DPs could help prevent delays and mitigate the impact of the COVID-19 pandemic on oncological drug delivery services.