Kemoterapi randevularının hemşireler arasındaki iş yükü dengesi gözetilerek belirsizlik altında çizelgelenmesi

Öz

Belirsizlik varken ayaktan kemoterapi randevularının çizelgelenmesi esnasında hemşirelerin iş yükünü dengelemek zor bir problemdir. Bu çalışmada, hastaların randevu vakitlerinin belirlenmesini ve hemşirelerin kendi aralarındaki iş yükü dengesini bozmadan hastaların koltuklara ve hemşirelere atanmasını sağlayan iki aşamalı stokastik karışık tamsayılı programlama modeli tasarlanmıştır. Çalışmada pre-medikasyon ve infüzyon sürelerindeki belirsizlik dikkate alınmıştır. Modelin amaç fonksiyonu, hastaların bekleme süreleri ve hemşirelerin fazla mesai sürelerinin ağırlıklandırılmış toplamının beklenen değerini enküçüklemektedir. Büyük bir hastanenin ayaktan kemoterapi ünitesinden elde edilmiş veriler kullanılarak bilgisayısal deneyler yapılmıştır. Çalışmada gözetilen iki rakip ölçüt arasındaki denge incelenmiştir. Klinikte bulunan koltuk ve hemşire sayısı ile performans ölçütleri arasındaki ilişki irdelenmiştir. Stokastik çözüm değeri hesaplanarak, belirsizliği dikkate alarak çözüm bulmanın faydası ölçülmüştür.

Anahtar Kelimeler

• Kemoterapi çizelgeleme
• Stokastik programlama
• Sağlık hizmeti operasyonları

Chemotherapy appointment scheduling under uncertainty by considering workload balance among nurses

Abstract

Balancing nurse workload while scheduling outpatient chemotherapy appointments under uncertainty is a challenging problem. In this study, a two-stage stochastic mixed-integer programming model is proposed for setting appointment times of patients and assigning patients to chairs and nurses without distorting the workload balance among nurses. The uncertainty in pre-medication and infusion durations is considered. The objective function of the model minimizes the expected weighted sum of patient waiting time and nurse overtime. Computational experiments are conducted based on data from an outpatient chemotherapy unit of a large hospital. The trade-off between the two competing criteria of the study is investigated. The relationship between the numbers of chairs and nurses in the clinic with the performance measures is examined. The benefit of considering uncertainty is assessed by calculating the value of stochastic solution.

Keywords

• Chemotherapy scheduling
• Stochastic programming
• Healthcare operations

References

1. [1] Wilson BE, Jacob S, Yap ML, Ferlay J, Bray F. “Estimates of global chemotherapy demands and corresponding physician workforce requirements for 2018 and 2040: a population-based study”. The Lancet Oncology, 20(6), 769-780, 2019.
2. [2] Castaing J, Cohn A, Denton BT, Weizer A. “A stochastic programming approach to reduce patient wait times and overtime in an outpatient infusion center”. IIE Transactions on Healthcare Systems Engineering, 6(3), 111-125, 2016.
3. [3] Lame G, Jouini O, Cardinal JSL. “Outpatient chemotherapy planning: A literature review with insights from a case study”. IIE Transactions on Healthcare Systems Engineering, 6(3), 127-139, 2016.
4. [4] Gupta D, Denton B. “Appointment scheduling in health care: Challenges and opportunities”. IIE Transactions, 40(9), 800-819, 2008.
5. [5] Cayirli T, Veral E. “Outpatient scheduling in health care: a review of literature”. Production and Operations Management, 12(4), 519-549, 2003.
6. [6] Ahmadi-Javid A, Jalali Z, Klassen KJ. “Outpatient appointment systems in healthcare: a review of optimization studies”. European Journal of Operational Research, 258(1), 3-34, 2017.
7. [7] Turkcan A, Zeng B, Lawley M. “Chemotherapy operations planning and scheduling”. IIE Transactions on Healthcare Systems Engineering, 2(1), 31-49, 2012.
8. [8] Heshmat M, Nakata K, Eltawil A. “Solving the patient appointment scheduling problem in outpatient chemotherapy clinics using clustering and mathematical programming”. Computers & Industrial Engineering, 124, 347-358, 2018.

9. [9] Sevinc S, Sanli UA, Goker E. “Algorithms for scheduling of chemotherapy plans”. Computers in Biology and Medicine, 43(12), 2103-2109, 2013.
10. [10] Hesaraki AF, Dellaert NP, de Kok T. “Generating outpatient chemotherapy appointment templates with balanced flowtime and makespan”. European Journal of Operational Research, 275(1), 304-318, 2019.
11. [11] Liang B, Turkcan A. “Acuity-based nurse assignment and patient scheduling in oncology clinics”. Health Care Management Science, 19(3), 207-226, 2016.
12. [12] Santibanez P, Aristizabal R, Puterman ML, Chow VS, Huang W, Kollmannsberger C, Nordin T, Runzer N, Tyldesley S. “Operations research methods improve chemotherapy patient appointment scheduling”. The Joint Commission Journal on Quality and Patient Safety, 38(12), 541-553, 2012.
13. [13] Hahn-Goldberg S, Beck JC, Carter MW, Trudeau M, Sousa P, Beattie K. “Solving the chemotherapy outpatient scheduling problem with constraint programming”. Journal of Applied Operational Research, 6(3), 135-144, 2014.
14. [14] Huggins A, Claudio D, Perez E. “Improving resource utilization in a cancer clinic: an optimization model”. IIE Annual Conference and Expo 2014, Montreal, Canada, 31 May-3 June 2014.
15. [15] Alvarado M, Ntaimo L. “Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming”. Health Care Management Science, 21(1), 87-104, 2018.
16. [16] Mandelbaum A, Momcilovic P, Trichakis N, Kadish S, Leib R, Bunnell CA. “Data-driven appointment scheduling under uncertainty: The case of an infusion unit in a cancer center”. Management Science, 66(1), 1-28, 2019.
17. [17] Demir NB, Gul S, Çelik M. “A stochastic programming approach for chemotherapy appointment scheduling”. Naval Research Logistics, 68(1), 112-133, 2021.