Optimization models for patient and technician scheduling in hemodialysis centers

Farbod Farhadi (), Sina Ansari () and Francisco Jara-Moroni ()
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Farbod Farhadi: Roger Williams University
Sina Ansari: DePaul University
Francisco Jara-Moroni: University Diego Portales

Health Care Management Science, 2023, vol. 26, issue 3, No 10, 558-582

Abstract: Abstract Patient and technician scheduling problem in hemodialysis centers presents a unique setting in healthcare operations as (1) unlike other healthcare problems, dialysis appointments have a steady state and the treatment times are determined in advance of the appointments, and (2) once the appointments are set, technicians will have to be assigned to two types of jobs per appointment: putting on and taking off patients (connecting to and disconnecting from dialysis machines). In this study, we design a mixed-integer programming model to minimize technicians’ operating costs (regular and overtime costs) at large-scale hemodialysis centers. As this formulation proves to be computationally challenging to solve, we propose a novel reformulation of the problem as a discrete-time assignment model and prove that the two formulations are equivalent under a specific condition. We then simulate instances based on the data from our collaborating hemodialysis center to evaluate the performance of our proposed formulations. We compare our results to the current scheduling policy at the center. In our numerical analysis, we reduced the technician operating costs by 17% on average (up to 49%) compared to the current practice. We further conduct a post-optimality analysis and develop a predictive model that can estimate the number of required technicians based on the center’s attributes and patients’ input variables. Our predictive model reveals that the optimal number of technicians is strongly related to the time flexibility of patients and their dialysis times. Our findings can help clinic managers at hemodialysis centers to accurately estimate the technician requirements.

Keywords: Dialysis; Patient scheduling; Technician; Optimization; Predictive analysis; Operations research; Operations management (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10729-023-09642-7

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