بازگشت به جستجو

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

عنوان	Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study
نویسنده	Bozkır, Cem Deniz Çağlar, Özmemiş, Çağrı, Kurbanzade, Ali Kaan, Koyuncu, Burcu Balçık, Gunes, E. D., Tuglular, S.
تاریخ انتشار:	2023-01-01
محل انتشار	- Elsevier
موضوع	COVID-19 pandemic, Hemodialysis, OR in health services, Patient cohorting, Stochastic programming
نوع	دوره ای
زبان	انگلیسی
دیجیتال	بله
نسخه خطی	خیر
کتابخانه:	دانشگاه اوزیغین
شناسه دارایی کتابخانه	0377-2217
شماره ثبت	f38b0b99-609d-40e5-8cdb-909c9e1a5d8f
محل کتابخانه	Industrial Engineering
تاریخ	2023-01-01
یادداشت‌ها	AXA Research Fund
متن نمونه	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

مشاهده در منبع دانشگاه اوزیغین دانشگاه اوزیغین - موتور جستجوی نسخه های خطی عثمانی

دانشگاه اوزیغین

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

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

تاریخ انتشار 2023-01-01

محل انتشار - Elsevier

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

نوع دوره ای

زبان انگلیسی

دیجیتال بله

نسخه خطی خیر

کتابخانه دانشگاه اوزیغین

شناسه دارایی کتابخانه 0377-2217

شماره ثبت f38b0b99-609d-40e5-8cdb-909c9e1a5d8f

محل کتابخانه Industrial Engineering

تاریخ 2023-01-01

یادداشت‌ها AXA Research Fund

متن نمونه 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