A Data-Driven Pharmacists Scheduling Problem in a Pharmacy with Fairness Concerns

Yuyao Feng () and Xiang Jie ()
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Yuyao Feng: National University of Singapore
Xiang Jie: Sichuan University

A chapter in LISS 2022, 2023, pp 363-378 from Springer

Abstract: Abstract In a field investigation of the outpatient pharmacy of one hospital in Chengdu, we found it bears problems including the poor working experience of pharmacists, long patient waiting time, and low service efficiency. One major reason is traced to the lack of a scientific scheduling method, therefore, in this study, we proposed an optimization scheduling method based on the data on prescriptions and pharmacist preferences. We built a data-driven pharmacist scheduling model to minimize the difference in work experience and intensity among pharmacists. Specifically, the objective is to minimize the variance of the weighted working time among pharmacists and to lower the work intensity and negative emotions of pharmacists. The case study demonstrates that this model has considerable advantages in strengthening the fairness concerns of pharmacists as well as their work experience. The results also show that the model has good properties regarding stability and robustness in varied circumstances. Through this optimization, we could improve the efficiency of pharmacists and the service quality of the pharmacy.

Keywords: Scheduling optimization; Fairness concerns; Outpatient pharmacy; Data driven; Efficiency; Integer programming (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-2625-1_29

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