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

Name: Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study
Author: Bozkır, Cem Deniz Çağlar, Özmemiş, Çağrı, Kurbanzade, Ali Kaan, Koyuncu, Burcu Balçık, Gunes, E. D., Tuglular, S.

İsim	Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study
Yazar	Bozkır, Cem Deniz Çağlar, Özmemiş, Çağrı, Kurbanzade, Ali Kaan, Koyuncu, Burcu Balçık, Gunes, E. D., Tuglular, S.
Basım Tarihi:	2023-01-01
Basım Yeri	- Elsevier
Konu	COVID-19 pandemic, Hemodialysis, OR in health services, Patient cohorting, Stochastic programming
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0377-2217
Kayıt Numarası	f38b0b99-609d-40e5-8cdb-909c9e1a5d8f
Lokasyon	Industrial Engineering
Tarih	2023-01-01
Notlar	AXA Research Fund
Örnek Metin	Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of patients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a 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 hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.
DOI	10.1016/j.ejor.2021.10.039
Cilt	304

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

Yazar Bozkır, Cem Deniz Çağlar, Özmemiş, Çağrı, Kurbanzade, Ali Kaan, Koyuncu, Burcu Balçık, Gunes, E. D., Tuglular, S.

Basım Tarihi 2023-01-01

Basım Yeri - Elsevier

Konu COVID-19 pandemic, Hemodialysis, OR in health services, Patient cohorting, Stochastic programming

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0377-2217

Kayıt Numarası f38b0b99-609d-40e5-8cdb-909c9e1a5d8f

Lokasyon Industrial Engineering

Tarih 2023-01-01

Notlar AXA Research Fund

Örnek Metin Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of patients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a 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 hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.

DOI 10.1016/j.ejor.2021.10.039

Cilt 304