Master chemotherapy planning and clinicians rostering in a hospital outpatient cancer centre

Giuliana Carello (), Paolo Landa (), Elena Tànfani () and Angela Testi ()
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Giuliana Carello: Informazione e Bioingegneria Politecnico di Milano Via Ponzio 34
Paolo Landa: Département d’opérations et systèmes de décision Faculté des sciences de l’administration Université Laval 2325
Elena Tànfani: Università degli studi di Genova
Angela Testi: Università degli studi di Genova

Central European Journal of Operations Research, 2022, vol. 30, issue 1, No 6, 159-187

Abstract: Abstract In the past years, the number of patients in need of chemotherapy treatments has been constantly increasing. Chemotherapy treatments must be carefully planned to provide a suitable and timely care. They are often provided within an outpatient setting. Clinicians and nurses staff must face the increasing demand for chemotherapy treatment with limited resources, such as exam rooms, beds, and seats. In this work, we consider a cancer centre shared among different oncologist specialties, as suggested by the Organisation of European Cancer Institutes. We focus on the oncologist visit that each patient must undergo before the drug infusion, to check if the patient’s conditions are compatible with the drug infusion. We consider the problem of planning the weekly assignment of consultation rooms to cancer pathologies, referred to as Master Chemotherapy Planning. Further, we jointly address the problem of selecting a clinician with suitable skills to cover each consultation room in the weekly schedule, given the cliniciansávailability over a month. Several criteria are considered, such as the number of visits in overtime, the amount of met demand and the clinicians’ workload. The problem is formulated as a lexicographic multiobjective optimisation problem and solved using a sequence of MIP models. Further, we propose a rolling horizon approach to tackle a long planning horizon up to one year, aiming also at keeping the changes of weekly plans from one month to the other as small as possible. The models and rolling horizon procedure are tested on real data from an Italian hospital.

Keywords: Chemotherapy planning; Oncologists rostering; Multiobjective optimisation; MIP models; Rolling horizon procedure (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10100-021-00786-x

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