An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

This paper explores the rationing of bed capacity in a cardiac intensive care unit (ICU). We find that the length of stay for patients admitted to the ICU is influenced by the occupancy level of the ICU. In particular, a patient is likely to be discharged early when the occupancy in the ICU is high. This in turn leads to an increased likelihood of the patient having to be readmitted to the ICU at a later time. Such “bounce-backs” have implications for the overall ICU effective capacity—an early discharge immediately frees up capacity, but at the risk of a (potentially much higher) capacity requirement when the patient needs to be readmitted. We analyze these capacity implications, shedding light on the question of whether an ICU should apply an aggressive discharge strategy or if it should follow the old quality slogan and “do it right the first time.” By comparing the total capacity usage for patients who were discharged early versus those who were not, we show that an aggressive discharge policy applied to patients with lower clinical severity levels frees up capacity in the ICU. However, we find that an increased number of readmissions of patients with high clinical severity levels occur when the ICU is capacity constrained, thereby effectively reducing peak bed capacity

The Impact of Delays on Service Times in the Intensive Care Unit

Mainstream queueing models are frequently employed in modeling healthcare delivery in a number of settings, and they further are used in making operational decisions for the same. The vast majority of these queueing models ignore the effects of delay experienced by a patient awaiting care. However, long delays may have adverse effects on patient outcomes and can potentially lead to a longer length of stay (LOS) when the patient ultimately does receive care. This work sets out to understand these delay issues from an operational perspective. Using data of more than 57,000 emergency department (ED) visits,we use an instrumental variable approach to empirically measure the impact of delays in intensive care unit (ICU) admission, i.e., ED boarding, on the patient's ICU LOS for multiple patient types. Capturing these empirically observed effects in a queueing model is challenging because the effect introduces potentially long-range correlations in service and interarrival times. We propose a queueing model that incorporates these measured delay effects and characterizes approximations to the expected work in the system when the service time of a job is adversely impacted by the delay experienced by that job. Our approximation demonstrates an effect of system load on work that grows much faster than the traditional 1/(1 - ρ) relationship seen in most queueing systems. As such, it is imperative that the relationship of delays and LOS be better understood by hospital managers so that they can make capacity decisions that prevent even seemingly moderate delays from causing dire operational consequences. Key words: Delay effects, queueing, HealthcareNational Science Foundation (U.S.) (CAREER Grant CMMI-1054034

Empirical research in healthcare operations: past research, present understanding, and future opportunities

We examine the published empirical literature in healthcare operations management over the last 20 years. We note several unique characteristics of the research in healthcare operations, including a focus on operational and organizational variables, an interest in the underlying mechanisms that explain operational causal pathways, and an interest in economic and managerial implications. We organize the prior findings under five distinct themes: importance of operational variables, importance of volume, routing patients through healthcare systems, to err is human, and managing the improvement process. We also identify several key areas of future research, including personalized medicine, value based healthcare, and connected health. We conclude with a call to action for greater engagement with the medical community in areas where tools and insights of operations management can bring about improvements in healthcare delivery

A Model to Create an Efficient and Equitable Admission Policy for Patients Arriving to the Cardiothoracic ICU

To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons. Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs

The effect of competition on process and outcome quality of hospital care: An empirical analysis for the Netherlands

The paper focuses on the relationship between competition and quality in the Dutch hospital sector. We analyse the period of 2004-2008, in which a healthcare reform took place in the Netherlands, introducing competition in the healthcare sector. The increased attention to hospital quality and its growing importance in a new institutional environment have resulted in a gradual increase of the voluntary disclosure of quality indicators by Dutch hospitals. We use panel data on Dutch general and academic hospitals in 2004-2008, including both process indicators (e.g., share of operation cancellations on short notice and share of diagnoses within 5 days) and outcome indicators (e.g., mortality rates) of hospital quality. We take the correlation between the disclosure decision and the level of the disclosed quality indicators explicitly into account by estimating a bivariate model. We find that competition explains differences in performance on process indicators, but not on outcome indicators.

Medical Devices Competitiveness and Impact on Public Health Expenditure

This study provides an analytical overview of the state of the European Union medical device industry. The medical device industry sector encompasses an extremely large variety of products and technologies. It covers hundreds of thousands of products that range from more traditional products, such as bandages or syringes, to sophisticated devices that incorporate bioinformatics, nanotechnology and engineered cells. These are designed for use by practitioners, patients and healthy individuals in a variety of settings: hospitals, surgeries and private homes. Besides being a vital and innovative industry, medical devices are a key component of healthcare systems and represent, together with pharmaceuticals, the bulk of ‘medical technology’. The analysis of the sector must therefore investigate medical devices as an industry – an innovative contributor to the economy – as well its key input to healthcare systems. The following aspects are taken into account: a) the impact of innovation in medical devices on health costs and expenditure; b) the innovativeness of the European medical device industry; c) the competitiveness of the European medical device industry as compared to that of the United States and Japan.healthcare expenditure; medical devices; competitiveness; innovation

Modeling Patient Flow in a Network of Intensive Care Units (ICUs)

Beginning in 2012, the Department of Health and Human Services (HHS) started adjusting payment for specific conditions by 30% for hospitals with 30-day patient readmission rates higher than the 75th percentile (HHS.gov, 2011). Furthermore, starting in 2013, HHS requires hospitals to publish their readmission rates (HHS.gov, 2011). It is also estimated that by 2013, healthcare expenditures in the United States will account for 18.7% of the Gross Domestic Product (GDP) (Centers of Medicare and Medicaid Services and US Bureau of Census, 2004). Yet the US healthcare system still suffers from congestion and rising costs as illustrated by hospital congestion. One way to reduce congestion and improve patient flow in the hospital is by modeling patient flow. Using queueing theory, we determined the steady state solution of an open queueing network, while accounting for instantaneous and delayed feedback. We also built a discrete event simulation model of patient flow in a network of Intensive Care Units (ICUs), while considering instantaneous and delayed readmissions, and validated the model using real patient flow data that was collected over four years. In addition, we compared several statistical and data mining techniques in terms of classifying patient status at discharge from the ICU (highly imbalanced data) and identify methods that perform the best. Our work has several contributions. Modeling patient flow while accounting for instantaneous and delayed feedback is considered a major contribution, as we are unaware of any patient flow study that has done so. Validating the discrete event simulation model allows for the implementation and application of the model in the real world by unit managers and administrators. The simulation model could be used to test different scenarios of patient flow, and to identify optimal resource allocation strategies in terms of number of beds and/or staff schedules in order to maximize patient throughput, reduce patient wait time and improve patients’ outcome. Moreover, identifying high risk patients who are more likely to die in the ICU ensures that those patients are receiving appropriate and timely care, so their risk of death is reduced

Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to intensive care units

Instrumental variable methods, subject to appropriate identification assumptions, enable consistent estimation of causal effects in the presence of unobserved confounding. Near–far matching has been proposed as one analytic method to improve inference by strengthening the effect of the instrument on the exposure and balancing observable characteristics between groups of subjects with low and high values of the instrument. However, in settings with hierarchical data (e.g. patients nested within hospitals), or where several covariate interactions must be balanced, conventional near–far matching algorithms may fail to achieve the requisite covariate balance. We develop a new matching algorithm, that combines near–far matching with refined covariate balance, to balance large numbers of nominal covariates while also strengthening the instrumental variable. This extension of near–far matching is motivated by a case-study that aims to identify the causal effect of prompt admission to an intensive care unit on 7-day and 28-day mortality

Hospital-physician Integration and Physician Collaboration: Implications for Care Efficiency and Outcomes

This thesis focuses on healthcare operations management and consists of two essays that investigate empirically how the relationship between physicians and hospitals and the relationship between peer physicians, respectively, affect clinical care outcomes and care efficiency. In the first essay, I study hospital-physician integration as a type of organization-service provider relationship. Many prior studies have provided insights into the benefits of a tight collaboration between hospitals and physicians. However, neutral and even negative effects of this relationship on healthcare performance have been observed and discussed in the literature. This mixed evidence points to a need for further study to elucidate the implications of hospital-physician integration for healthcare performance. This essay adopts an activity-based approach to operationalization of hospital-physician integration, referred to as ABI. I utilize patient-visit level information for patients who have been treated with coronary artery bypass graft (CABG) surgery to demonstrate a U-shaped association between ABI and clinical outcomes such as patient length of stay (LOS), in-hospital mortality risk, and readmission risk. I also find that hospital teaching status and bed utilization suppress the effect of ABI on patient LOS. The results suggest that a medium level of integration could be desirable, since a strategy of high integration trades off potentially higher patient volumes and knowledge ossification for suboptimal care outcomes. In the second essay, I study collaboration between physicians working in emergency department (ED), a horizontal relationship between peer service providers. More specifically, I use measures of physician familiarity and a physician’s level of exposure to different peer partners, referred to as partner exposure, to denote peer collaboration. Using data on patient visits to hospital emergency departments in the U.S. state of Florida, I build econometric models to evaluate empirically the relationship between peer collaboration and care efficiency, as measured by a patient’s time spent in the ED and the number of procedures received. My investigation shows that both physician familiarity and level of partner exposure help improve care efficiency, with the associated effects being stronger for patients with severe conditions. Besides the main hypotheses, we provide several post-hoc analyses which further reveal that physicians’ single-siting status complements and enhances the relationships between physician familiarity and partner exposure, respectively, and care efficiency