Stochastic scheduling of chemotherapy appointments considering patient acuity levels


Karakaya S., Gül S., Çelik M.

European Journal of Operational Research, cilt.305, sa.2, ss.902-916, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 305 Sayı: 2
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.ejor.2022.06.014
  • Dergi Adı: European Journal of Operational Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, EconLit, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.902-916
  • Anahtar Kelimeler: OR in health services, Chemotherapy scheduling, Patient acuity, Stochastic programming
  • TED Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier B.V.The uncertainty in infusion durations and non-homogeneous care level needs of patients are the critical factors that lead to difficulties in chemotherapy scheduling. We study the problem of scheduling patient appointments and assigning patients to nurses under uncertainty in infusion durations for a given day. We consider instantaneous nurse workload, represented in terms of total patient acuity levels, and chair availability while scheduling patients. We formulate a two-stage stochastic mixed-integer programming model with the objective of minimizing expected weighted sum of excess patient acuity, waiting time and nurse overtime. We propose a scenario bundling-based decomposition algorithm to find near-optimal schedules. We use data of a major university hospital to generate managerial insights related to the impact of acuity consideration, and number of nurses and chairs on the performance measures. We compare the schedules obtained by the algorithm with the baseline schedules and those found by applying several relevant scheduling heuristics. Finally, we assess the value of stochastic solution.