Innovative use of operational tools to improve care delivery for the uninsured ESRD patients and to inform healthcare policy-makers

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End-stage renal disease (ESRD) is a direful diagnosis for which regular (i.e., periodically scheduled) dialysis is typically the only immediate and accessible treatment. ESRD patients who are uninsured are in a high-risk category as they do not have access to regular treatment and have to rely on safety-net hospitals, funded by county governments, for access to dialysis. Since no national funding provides scheduled dialysis to this population, their only option is to seek dialysis under “emergency” conditions. These conditions are such that without urgent medical attention in the Emergency Room (ER), the patient’s life is under threat. Hence, ER serves as a screening stage for gaining access to regular dialysis by the uninsured, and the resulting practice is known as “compassionate dialysis,” a type of emergent dialysis treatment frequently offered at county hospitals serving uninsured ESRD patients. For a typical compassionate dialysis practice, existing county policy is such that patients are subject to a screening protocol upon arrival in the ER. The protocol serves to assess the severity of the patients’ condition in the ER, and, hence, a certain fraction of the patients may not be offered treatment, i.e., these patients have to revisit the hospital at a later time, potentially within a few hours due to the nature of the underlying disease. The fraction of patients not offered the treatment is referred to as the screening threshold. As documented in the literature, the practice is costly and leads to significant congestion and treatment delays. Motivated by a real-life compassionate dialysis practice, we employ process flow mapping to gain a better understanding of the patient flow and identify inefficiencies and bottlenecks caused by the screening protocol of the existing county policy. We use simulation modeling to examine and estimate various system and patient-oriented metrics as a function of stochastic arrival rates and service times. Our eventual goal is to explore and analyze two proposals as alternatives to the current practice: one modifies the existing screening threshold based on the available capacity, and the other schedules and consolidates the future revisits of patients. We analyze and compare the effectiveness of both proposals using simulation optimization approaches. Ultimately, our goal is to propose solutions for alleviating congestion and treatment delays, and to inform hospital administrators and policy-makers about such solutions.