  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.
