Patient scheduling in hemodialysis service

Liu, Zhenyuan; Lu, Junwen; Liu, Zaisheng; Guang-Rui, Liao; Zhang, Hao Howard; Dong, Junwu

doi:10.1007/s10878-017-0232-z

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…This problem can also be investigated with regard to the human resource fatigue. Similar characteristics can be found in the patient appointment scheduling in radiotherapy clinics (Legrain et al, 2015) and in hemodialysis treatment services (Liu et al, 2019).…”

Section: Introductionsupporting

confidence: 57%

Coupled task scheduling with time-dependent processing times

Khatami

Salehipour

2021

J Sched

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The single machine coupled task scheduling problem includes a set of jobs, each with two separated tasks and there is an exact delay between the tasks. We investigate the single machine coupled task scheduling problem with the objective of minimizing the makespan under identical processing time for the first task and identical delay period for all jobs, and the time-dependent processing time setting for the second task. Certain healthcare appointment scheduling problems can be modeled as the coupled task scheduling problem. Also, the incorporation of time-dependent processing time for the second task lets the human resource fatigue and the deteriorating health conditions be modeled. We provide optimal solution under certain conditions. In addition, we propose a dynamic program under the condition that the majority of jobs share the same time-dependent characteristic. We develop a heuristic for the general case and show that the heuristic performs well.

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Section: Introductionsupporting

confidence: 57%

Coupled task scheduling with time-dependent processing times

Khatami

Salehipour

2021

J Sched

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“…The existing studies usually consider deterministic settings. Liu et al. (2019) study a dialysis scheduling problem with different types of patients (conventional, hepatitis B, and hepatitis C) and present an integer programming model to minimize night shifts and meet patient preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Given that the global prevalence rate of chronic kidney disease (all stages) is 9.1% ( Bikbov et al., 2020 ), effective planning of HD treatments during the pandemics by accounting for infection spread risk can protect the lives of millions of people in vulnerable conditions. However, even without a pandemic disturbance, scheduling treatments in a single HD unit is a difficult problem (e.g., Fleming, Gartner, Padman, & James (2019) ; Holland (1994) ; Liu et al. (2019) ).…”

Section: Introductionmentioning

confidence: 99%

Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study

Bozkir

Ozmemis

Kurbanzade

et al. 2023

European Journal of Operational Research

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“…Although there exist a few operations research studies that address problems related to planning hemodialysis treatments, no study has yet addressed the particular challenges faced during a pandemic setting. Liu et al (2019) study a dialysis scheduling problem with different types of patients (conventional, hepatitis B, and hepatitis C) that must be treated in a dialysis center. The authors present an IP model to minimize night shifts and meet patient preferences.…”

Section: Hemodialysis Planning During Pandemicsmentioning

confidence: 99%

“…Given that the global prevalence rate of chronic kidney disease (all stages) is 9.1% (Bikbov et al, 2020), effective planning of dialysis treatments during the pandemics by accounting for infection spread risk can protect the lives of millions of people in vulnerable conditions. However, even without a pandemic disturbance, planning and scheduling of treatments in a single dialysis unit is already a difficult problem (e.g., Liu et al (2019); Holland (1994); Fleming et al (2019)). When resources become tighter, such as the aftermath of an earthquake (Sever et al, 2009) or during a pandemic (Corbett et al, 2020), the increased demands and uncertainties make it ever more challenging to plan for dialysis services.…”

Section: Introductionmentioning

confidence: 99%

Capacity Planning for Effective Cohorting of Dialysis Patients during the Coronavirus Pandemic: A Case Study

Bozkir,

Ozmemis,

Kurbanzade

et al. 2021

Preprint

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Chronic dialysis patients have been among the most vulnerable groups of the society during the coronavirus (COVID-19) pandemic as they need regular treatments in a hospital environment, facing infection risk. Moreover, the demand for dialysis resources has significantly increased since many COVID-19 patients need acute dialysis due to kidney failure. In this study, we address capacity planning decisions of a hemodialysis clinic located within a major hospital in Istanbul, designated to serve both infected and uninfected patients during the pandemic with limited resources (i.e., dialysis machines). The hemodialysis clinic applies a three-unit cohorting strategy to treat four types of patients in separate units and at different times to mitigate infection spread risk among patients.Accordingly, at the beginning of each week, the clinic needs to determine the number of available dialysis machines to allocate to each unit that serves different patient cohorts. Given the uncertainties in the number of different types of patients that will need dialysis, it is a challenge to allocate the scarce dialysis resources effectively by evaluating which capacity configuration would minimize the overlapping treatment sessions of infected and uninfected patients over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a two-stage stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the clinic to illustrate the effectiveness of the proposed modeling approach and compare the performance of different cohorting strategies.

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Patient scheduling in hemodialysis service

Cited by 9 publications

References 30 publications

Coupled task scheduling with time-dependent processing times

Coupled task scheduling with time-dependent processing times

Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study

Capacity Planning for Effective Cohorting of Dialysis Patients during the Coronavirus Pandemic: A Case Study

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