This chapter presents strategies to improve the management and prioritization of oncology waiting lists in the Chilean public health system. Based on a collaborative project with a secondary-level hospital, the work integrates data science, process standardization, automation, and optimization tools to address fragmentation, inefficiencies, and inequities in cancer care. Key results include the digitalization of clinical processes, the use of natural language processing to automate patient tracking, and the development of preliminary optimization models to support decision-making. The proposed public policies aim to strengthen data infrastructure, improve resource allocation, and enhance the timeliness and equity of cancer care delivery.