Patient Flow in Hospitals : a Data - Based Queueing - Science Perspective

Patient flow in hospitals can be naturally modeled as a queueing network, where patients are the customers, and medical staff, beds and equipment are the servers. But are there special features of such a network that sets it apart from prevalent models of queueing networks? To address this question, we use Exploratory Data Analysis (EDA) to study detailed patient flow data from a large Israeli hospital. EDA reveals interesting and significant phenomena, which are not readily explained by available queueing models, and which raise questions such as: What queueing model best describes the distribution of the number of patients in the Emergency Department (ED); and how do such models accommodate existing throughput degradation during peak congestion? What time resolutions and operational regimes are relevant for modeling patient length of stay in the Internal Wards (IWs)? While routing patients from the ED to the IWs, how to control delays in concert with fair workload allocation among the wards? Which leads one to ask how to measure this workload: Is it proportional to bed occupancy levels? How is it related to patient turnover rates? Our research addresses such questions and explores their operational and scientific significance. Moreover, the above questions mostly address medical units unilaterally, but EDA underscores the need for and benefit from a comparative-integrative view: for example, comparing IWs to the Maternity and Oncology wards, or relating ED bottlenecks to IW physician protocols. All this gives rise to additional questions that offer opportunities for further research, in Queueing Theory, its applications and beyond.