The hospital emergency department (ED) is a critical source for health care amid a complex healthcare system in the United States. It is the gateway to care for a broad range of people, arriving from a variety of locations. With this wide reaching net and a decreasing trend in hospital beds, EDs throughout the United States are experiencing overcrowding. ED crowding has various tactical and strategic facility management impacts ranging from facility occupancy issues to adverse health outcomes. Among other factors, recent research has cited the sharp increase in ED visits over the years and ED patient boarding as key contributors to crowding. Home hospital care is a model in which health care is delivered at an individual’s home as a substitute for hospital-level inpatient short-term acute care. Clinical research has shown home hospital to be an effective care model for select illnesses presenting frequently to EDs, such as congestive heart failure, community acquired pneumonia, chronic obstructive pulmonary disease, and cellulitis. While there exist distinct clinical and social criteria for which delineate eligible individuals, home hospital care models have been linked with the potential to free inpatient beds. The overarching objective of this study is to investigate the relationship between home hospital care and ED crowding. To achieve this objective, the study examined the relationship between home hospital care and ED crowding, specific to ED boarding performance at a large, urban, teaching hospital facility. A methodology for identification of potential home hospital patients was used through clinical and social criteria, and a scale for the range of clinical eligibility rates was established for the five suitable illnesses. The study modeled patient flow and bed demand, and utilized computer simulation modeling to assess the impact of home hospital care on ED boarding performance. Various models were simulated to represent different home hospital intervention types. The models incorporated home hospital through an ED Referral program, Inpatient-Transfer Referral program, Community Referral program, and a fully integrated home hospital program. Three scenarios were run for each model to assess practical possibilities for the utilization of the freed bed hours from a home hospital program. This research contributes insight and understanding of home hospital’s impacts on ED crowding. The insight from this study quantifies the effects of a home hospital program on ED boarding and inpatient bed demand. The modeling study is contributes an analytical understanding of the impacts that home hospital could potentially have on crowding, which could prove useful in the struggle against ED congestion. This understanding helps to provide a more thorough understanding of home hospital, and could aid in an organization’s decision-making process of whether to implement a program. The presented modeling methodology for analyzing home hospital and ED crowding can also be used as a model format for researchers and practitioners for analytical purposes in future studies.

Delivering health care services in an efficient and effective way has become a great challenge for many countries due to the aging population worldwide, rising health expenses, and increasingly complex healthcare delivery systems. It is widely recognized that models and analytical tools can aid decision-making at various levels of the healthcare delivery process, especially when decisions have to be made under uncertainty. This thesis employs stochastic models to improve decision-making under uncertainty in two specific healthcare settings: inpatient flow management and infectious disease modeling. In Part I of this thesis, we study patient flow from the emergency department (ED) to hospital inpatient wards. This line of research aims to develop insights into effective inpatient flow management to reduce the waiting time for admission to inpatient wards from the ED. Delayed admission to inpatient wards, also known as ED boarding, has been identified as a key contributor to ED overcrowding and is a big challenge for many hospitals. Part I consists of three main chapters. In Chapter 2 we present an extensive empirical study of the inpatient department at our collaborating hospital. Motivated by this empirical study, in Chapter 3 we develop a high fidelity stochastic processing network model to capture inpatient flow with a focus on the transfer process from the ED to the wards. In Chapter 4 we devise a new analytical framework, two-time-scale analysis, to predict time-dependent performance measures for some simplified versions of our proposed model. We explore both exact Markov chain analysis and diffusion approximations. Part I of the thesis makes contributions in three dimensions. First, we identify several novel features that need to be built into our proposed stochastic network model. With these features, our model is able to capture inpatient flow dynamics at hourly resolution and reproduce the empirical time-dependent performance measures, whereas traditional time-varying queueing models fail to do so. These features include unconventional non-i.i.d. (independently and identically distributed) service times, an overflow mechanism, and allocation delays. Second, our two-time-scale framework overcomes a number of challenges faced by existing analytical methods in analyzing models with these novel features. These challenges include time-varying arrivals and extremely long service times. Third, analyzing the developed stochastic network model generates a set of useful managerial insights, which allow hospital managers to (i) identify strategies to reduce the waiting time and (ii) evaluate the trade-off between the benefit of reducing ED congestion and the cost from implementing certain policies. In particular, we identify early discharge policies that can eliminate the excessively long waiting times for patients requesting beds in the morning. In Part II of the thesis, we model the spread of influenza pandemics with a focus on identifying factors that may lead to multiple waves of outbreak. This line of research aims to provide insights and guidelines to public health officials in pandemic preparedness and response. In Chapter 6 we evaluate the impact of seasonality and viral mutation on the course of an influenza pandemic. In Chapter 7 we evaluate the impact of changes in social mixing patterns, particularly mass gatherings and holiday traveling, on the disease spread. In Chapters 6 and 7 we develop agent-based simulation models to capture disease spread across both time and space, where each agent represents an individual with certain socio-demographic characteristics and mixing patterns. The important contribution of our models is that the viral transmission characteristics and social contact patterns, which determine the scale and velocity of the disease spread, are no longer static. Simulating the developed models, we study the effect of the starting season of a pandemic, timing and degree of viral mutation, and duration and scale of mass gatherings and holiday traveling on the disease spread. We identify possible scenarios under which multiple outbreaks can occur during an influenza pandemic. Our study can help public health officials and other decision-makers predict the entire course of an influenza pandemic based on emerging viral characteristics at the initial stage, determine what data to collect, foresee potential multiple waves of attack, and better prepare response plans and intervention strategies, such as postponing or cancelling public gathering events.