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

Capacity Pooling in Healthcare Systems

Healthcare systems are facing a continuously increasing demand for care while healthcare providers express a need for additional capacity. However, increased capacity in healthcare systems will not be a sufficient option in the near future, and previous research has found a need to improve healthcare capacity planning and management. Capacity planning is aggravated with the presence of variations in a system, and proactive and reactive tools for short-term flexibility in capacity management can be applied to cope with variations in both capacity and demand. One such proactive tool is a capacity pool, which is a general capacity that can be allocated to parts of the system where the temporary need for resources is unusually high. The purpose of this thesis is to develop principles and guidelines for a capacity pooling system in the healthcare sector. A theoretical framework that describes the limiting effects of aggregated variations is modern portfolio theory, first originated in the finance sector. Portfolio theory is in this thesis used to demonstrate the effects on resource utilization when capacity is organized into capacity pools. The study object is Region V\ue4stra G\uf6taland, a healthcare provider and multihospital system in Sweden. The approach of the research project has been systematic, using a mixed-methods approach with predominantly quantitative studies. An interview study, a questionnaire study and a literature review have been conducted to answer the research questions, resulting in three papers.This research project has resulted in several findings which can be useful for healthcare managers when designing and implementing capacity pools. The results include examples on how portfolio theory could be used to design capacity pools, knowledge on the use of proactive and reactive tools for short-term flexibility solutions in healthcare capacity management, and perceived barriers to a capacity pooling approach in healthcare systems. Furthermore, the findings in the three papers contribute to the existing research in several ways. For example, previous studies have requested research with a holistic approach on capacity management in healthcare systems and have highlighted the importance of researching temporary capacity changes in healthcare. The research in this thesis has through a mixed-method systematic approach focused on capacity management in a multihospital system consisting of several healthcare providers, including all types of healthcare personnel, and has provided knowledge on the use of flexibility tools for managing variations in capacity and demand

A Causal Tree Approach for Personalized Health Care Outcome Analysis

Using patient-level data from 35 hospitals for 6 cardiovascular surgeries in New York, we provide empirical evidence that outcome differences between health care providers are heterogeneous across different groups of patients. We then use a causal tree approach to identify patient groups that exhibit significant differences in outcome. By quantifying these differences, we demonstrate that a large majority of patients can achieve better expected outcomes by selecting providers based on patient-centric outcome information. We also show how patient-centric outcome information can help providers to improve their processes and payers to design effective pay-for-performance programs.http://deepblue.lib.umich.edu/bitstream/2027.42/136093/1/1336_Wang.pd

Labour Markets in Professional Sports

Measuring performance and quantifying outcomes can prove a difficult task in empirical economics research. Because of this, economists have often turned to the setting of professional sports to overcome these data limitations. Sports and sports data presents a unique opportunity to study the behaviour of workers, firms and supervisors, since performance can be accurately measured and compared across agents. This thesis offers three chapters in the broad fields of labour and personnel economics, using data from professional sports to illustrate. In Chapter One, we consider the role of Head Coaches at football clubs, and whether teams can benefit from Head Coach turnover. This extends on previous work on this topic along several lines. Most notably, Head Coach turnover can either be voluntary or involuntary. In a principal-agent framework, these are theoretically two quite different events, with each producing different predictions about changes to team performance. We also use data from multiple leagues and can distinguish between a short run “bump” effect, and a longer run learning effect. Results show that teams can benefit from Head Coach turnover, particularly following a dismissal, though the result is sensitive to how we define our follow up period. In Chapter 2, we examine the ability of baseball pitchers to switch between different tasks, by considering how their pitching performance is affected by the additional demands of having to bat and run bases. Despite the prevalence of task switching in modern day work, there is a surprising lack of empirical evidence on its effects on productivity. Baseball is an ideal setting to consider this question empirically, making use of the two-league structure of Major League Baseball. In one league, pitchers are faced with a forced task switching rule of having to both pitch and bat, while in the other, pitchers can focus on their primary job; pitching. The structure of the game of baseball, consisting of innings and a batting order, also means we can cleanly identify cases of workers switching back and forth between tasks. Our results indicate that pitchers can actually benefit from batting, but at all costs should avoid excessive fatigue after running bases. Finally, in Chapter 3, we return to Coaches, this time in the National Football League. We examine the determinants of coaching changes at the levels of Head Coach and Coordinator. In particular, we pay close attention to the role of the league’s affirmative action policy, the Rooney Rule, on the likelihood of minority coaches being appointed to a Head Coaching role. Results suggest that the rule has been somewhat successful, since teams now appear to be hiring equally skilled black and white coaches, despite evidence that there had always been a supply of equally skilled black coaches

Hospital Survivability and Government Policies: The 2010 Affordable Care Act

This dissertation investigates the impact of the U.S. Affordable Care Act (ACA) of 2010 on hospital survivability. To this end, I study two policy changes in the ACA. The first is the Hospital Readmission Reduction Program (HRRP), which ties the Centers for Medicare & Medicaid Services (CMS) payments to hospital readmission rates. A hospital’s readmission rate thus becomes an important financial and healthcare delivery indicator. Hence, in the first project of this dissertation, I test the financial viability of hospitals based on readmission rates. Then, using Simar and Wilson’s two-stage data envelopment analysis (DEA), I test the impact of two dimensions of quality—experiential quality and clinical quality—on hospitals’ financial viability. Results indicate that hospitals that offer higher quality care are more efficient at achieving financial viability. Additionally, the results demonstrate that excelling in both dimensions has had additional benefits for hospitals. The second policy change explored is the ACA’s Medicaid coverage expansion. I examine its impact on hospital closures. This policy expands Medicaid coverage to all adults with incomes lower than 138% of the U.S. federal poverty level. However, based on constitutional arguments against the ACA, the U.S. Supreme Court in 2012 ruled that states could opt out of the mandate. The heterogeneous adoption by states enables researchers to conduct a natural experiment by providing a control group. Therefore, I adopt a difference-in-differences analysis framework with fixed effects using a Poisson regression to test whether the ACA-mandated Medicaid coverage expansion impacted hospital closures. Results show that the mandate reduced the number of hospital closures in states that complied with the mandate by 54% as compared to states that did not. Then, I explore hospital-level operational drivers that contributed to the hospital closure crisis. Results demonstrate that the mandate increased patient revenue and perceived quality of care, while no evidence showed that the mandate affected the number of patient discharges, number of employees, and hospital operating expenses. Furthermore, my results suggest that Medicaid expansion increased hospital revenue not by increasing the number of patients, but rather by decreasing hospitals’ amount of uncompensated care

Optimal Recruitment of Temporary and Permanent Healthcare Workers in Highly Uncertain Environments

There has been a significant increase in the demand for temporary skilled workers in the health sector. They provide volume flexibility, but are generally more expensive than their permanent counterparts. A balance must therefore be struck between staffing cost and service quality by recruiting the right mix of temporary and permanent healthcare workers. Focusing on periods of highly uncertain demand, in this thesis, we propose optimization models aiming to inform permanent and temporary recruitment decision making for settings in which all patients must be served. We pursue this under two different scenarios, a mid-term planning horizon and a long-term planning horizon. The first part of the thesis [1] is devoted to recruitment decision making in a mid-term planning horizon. The main trade-off in this case is between recruitment lead times and staffing costs of temporary and permanent workers. More specifically, permanent skilled workers are cheaper for the healthcare provider than equivalent temporary workers, but have a substantially longer recruitment lead time. Longer recruitment lead time of permanent workers implies that providers face a higher level of demand uncertainty when making permanent recruitment decisions and a higher likelihood of not being able to fill the created positions. Considering a single-interval planning horizon, we propose a two-stage stochastic optimization framework to capture this fundamental trade-off. The first stage of our framework identifies the number of permanent positions to advertise, and the second stage determines the number of temporary workers to recruit. Our framework accounts for the uncertainty in the number of permanent vacancies that will be filled, stochasticity of the service delivery process, and imperfect demand information at the time of advertising for permanent positions. Under a general setting of the problem, we characterize the optimal first- and second-stage decisions analytically, propose fast numerical methods for finding their values, and prove some insensitivity and monotonicity properties for the optimal decisions and their corresponding costs. The benefit/loss of delaying the advertisement for permanent positions to obtain a more accurate demand information, at the expense of a higher risk of not filling the advertised positions, is also investigated. A case study based on data from a geriatric ward illustrates the application of our framework to an inpatient department, and further managerial insights are developed using a combination of analytical and numerical results. The second part of the thesis is dedicated to recruitment decision making in a long-term planning horizon. In addition to the different staffing costs and recruitment lead times of temporary and permanent workers captured in the first part, we consider the difference in their placement durations. This is because permanent workers have substantially longer contracts which may cover periods of low demand, hence in the long run, they are likely to be more expensive to the provider than temporary workers. We capture this by a multi-interval optimization framework which involves a two-stage decision making, similar to the two-stage decision making of the first part, repeated in each interval. The time-varying nature of demand over different intervals is also incorporated into this framework. Using a Markov decision process formulation, we prove that the optimal recruitment policy for permanent healthcare workers in this context has a hire-up-to structure. Numerical experiments then investigate the sensitivity of the hire-up-to value to different system parameters. The potential benefits of using the long-term (multi-interval) recruitment model as compared to the mid-term (single-interval) recruitment model is also evaluated numerically