An approximate dynamic programming approach to network-based scheduling of chemotherapy treatment sessions

AbstractA solution approach is proposed for the interday problem of assigning chemotherapy sessions at a network of treatment centres with the goal of increasing the cost-efficiency of system-wide capacity use. This network-based scheduling procedure is subject to the condition that both the first and last sessions of a patient's treatment protocol are administered at the same centre the patient is referred to by their oncologist. All intermediate sessions may be administered at other centres. It provides a systematic way of identifying effective multi-appointment scheduling policies that exploit the total capacity of a networked system, allowing patients to be treated at centres other than their home centre. The problem is modelled as a Markov decision process which is then solved approximately using techniques of approximate dynamic programming. The benefits of the approach are evaluated and compared through simulation with the existing manual scheduling procedures at two treatment centres in Santiago, Chile. The results suggest that the approach would obtain a 20% reduction in operating costs for the whole system and cut existing first-session waiting times by half. A key conclusion, however, is that a network-based scheduling procedure brings no real benefits if it is not implemented in conjunction with a proactive assignment policy like the one proposed in this paper.Keywords: OR in health serviceschemotherapy schedulingmarkov decision processesapproximate dynamic programminglinear programmingsimulation AcknowledgmentsThe authors would like to thank the Adult Chemotherapy Unit of the Red de Salud UC CHRISTUS (CECA) for generously supplying the necessary data to carry out the practical application discussed in this paper.Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe authors confirm that most of the data supporting the findings of this study are available within the article. Additional information is available from the corresponding author, AS, upon reasonable request.Additional informationFundingThis research was partially supported by the Chilean National Agency for Research and Development (ANID-Fondecyt) [grant number Regular 2023-1231320] and the Natural Sciences and Engineering Research Council of Canada (NSERC) [grant number RGPIN-2018-05225].Notes on contributorsArturo WenzelArturo Wenzel has a professional degree in Engineering with specialisation in Operations Research and Hydraulics and a master's degree in engineering sciences from the Pontificia Universidad Católica de Chile. His professional interests include the development and implementation of decision support systems for practical problems including chemotherapy scheduling.Antoine SauréAntoine Sauré is an associate professor at the Telfer School of Management at the University of Ottawa. His research interests include stochastic modelling, dynamic optimisation, and decision-making under uncertainty. He has many years of experience developing and applying advanced analytics techniques to large-scale problems in several industries. He has worked on the development of numerous capacity planning and patient scheduling systems aimed to provide timely access to quality cancer care.Alejandro CataldoAlejandro Cataldo is an assistant professor at the Instituto de Ingeniería Matemática y Computacional at the Pontificia Universidad Católica de Chile. His research interests include stochastic programming and evidence-based decision making under uncertainty. He has worked on the development and application of numerous solution methodologies for large-scale problems in industries such as health care, agriculture, and mining. More recently, he has collaborated with the Government of Chile in several research and development projects involving public services.Pablo A. ReyPablo A. Rey is an assistant professor at the Departamento de Industrias and an associate researcher of the Programa Institucional de Fomento a la Investigación, Desarrollo e Innovación at the Universidad Tecnológica Metropolitana de Chile. He holds a B.Sc. degree in Mathematics from the Universidad Nacional de Córdoba, Argentina, and a Ph.D. in Electrical Engineering from the Pontifícia Universidade Católica do Rio de Janeiro, Brazil. His research interests include optimisation, simulation, and transportation.César SánchezCésar Sánchez is an associate professor at the School of Medicine of the Pontificia Universidad Católica de Chile. His research interests include breast cancer, clinical trials, and real-world data analysis in oncology. He has worked on breast cancer clinical characterisation, endocrine therapy resistance mechanisms, and predictive biomarkers. He is currently in charge of the cancer research unit at the Hematology-Oncology Department of the School of Medicine at the Pontificia Universidad Católica de Chile.