Analytics and Decision Support in Cancer Care Operations Management: A Focus on Outpatient Chemotherapy Planning and Scheduling.

As per the World Health Organization, cancer continues to be one of the leading causes of death worldwide, accounting for nearly one in six deaths, which sets its associated health, economic and societal burdens to worrisome levels. However, many cancer cases have a high chance of being cured if treated timely. For these reasons, reducing the cancer burden has always been a central question for researchers from different backgrounds. Fostered by the huge development of Information and Communication Technologies (ICT), the democratization of the Internet of Things (IoT), and the evolution of Artificial Intelligence (AI) and optimization techniques, research on Cancer Care Operations Management (CCOM) has taken a lot of interest and attention in the recent years as it opened the door for new opportunities to optimize cancer patient pathways leading to a significant cancer burdens’ reduction. Well anchored in this setting, the present research looks at where the research in CCOM has gone in the past decades and leverages data analytics, simulation, and optimization techniques to design effective strategies for the management of outpatient chemotherapy; one of the cancer main treatment options, and one of the main challenging links in the cancer patient pathway. These objectives are achieved in two phases. In the first phase, a bibliometric analysis is used to map the aspects of 1288 published articles in the field of CCOM in the past decade. Then, specific emphasis is given to the research focusing on the optimization of outpatient chemotherapy. In the second phase, we collaborated with a large cancer care center to undertake data-driven research aimed at finding innovative solutions for outpatient chemotherapy management. Our efforts culminated in the creation of a novel approach that incorporates unsupervised machine learning and stochastic simulation optimization models for planning and scheduling of outpatient chemotherapy appointments. The experimental studies showed improvement in patient waiting time, staff overtime, and computational time, with reductions of 37%, 17%, and 48% respectively.