Evaluating Hospital Policy and Performance: Contributions from Hospital Policy and Productivity Research: Volume 18

Hospitals worldwide command the majority of any countries’ health care budget. Reasons for these higher costs include the aging of the population requiring more intensive health care treatments provided in hospitals, the relatively high costs of labor in this labor intensive industry and payment systems that may encourage inefficient behavior on the part of hospital managers and physicians. Governments are seeking to instruments to mitigate this cost rise. Liberalizing hospital markets, deregulation, changing budget systems and changing ownership are only a few examples of attempts to make hospitals more efficient.

Chapter 1 discusses the objective of the book and presents an outline. It explains the relevance of the subject not only from a social point of view, but also for the economy as a whole. Hospitals worldwide command the majority of any countries’ health care budget. Reasons for these higher costs include the aging of the population requiring more intensive health care treatments, the relatively high costs of labor in this labor-intensive industry and payment systems that may encourage inefficient behavior on the part of hospital managers and physicians. There is also a special role of technology in the hospital. It has been argued that advances in technology are one of the major reasons for hospital cost increases. Further Chapter 1 indicates that from international comparison we may conclude that large differences in hospital productivity exists. Chapter 1 presents an outline of the other chapters in the book, varying from issues dealing with privatizing, liberalizing, ownership, networks, budgeting, management skills, innovations and government facilitating research on productivity enhancement.

Hospitals worldwide command the majority of any countries’ health care budget. Reasons for these higher costs include the aging of the population requiring more intensive health care treatments provided in hospitals, the relatively high costs of labor in this labor intensive industry and payment systems that may encourage inefficient behavior on the part of hospital managers and physicians, that have not been fully mitigated via reforms and regulations.

There are two main dimensions in which the performance of a production unit can be assessed. The first is the dimension of time. The basic question here is: how is this or that production unit doing over time? Assessing a unit's performance over time is called monitoring. The second dimension is characterized by the question: how is this or that production unit doing relative to other, similar units? To answer this question one needs to specify the reference set of units and one needs sufficient information on each of the members of this set. This activity is usually called benchmarking. A combination of the two dimensions in the setting of a panel is also possible.

Research on hospital productivity has progressed over the last few decades considerably from early models where measurements of hospital services simply counted inpatient days, and perhaps outpatient visits or numbers of surgeries performed. This simplicity represents an extreme of aggregation, focuses the attention of the analysis entirely on the structure of the organization at the highest levels, and provides no insight into the specific services that might be provided to each patient as well as the characteristics of those patients, which might lead to specialization of their care. This process is fundamentally complex, which makes it especially difficult to model. This table-setting chapter will characterize some of the key contextual choices that must be made by researchers in this field which are then applied in subsequent chapters. The key point of this chapter will be to argue that there are very few “one size fits all” decisions in this process and thus the context of particular research objectives and questions will determine how modeling choices are made in practice. Some intuition about how these decisions have substantial implications for outcomes of measurement for hospital productivity will be provided; however, no attempt will be made to conduct a literature review of all the choices that have been made. Instead, we will suggest that new careful attention to the choices made can make future studies more effective in communicating to the communities implementing the research.

Over the last decade, the United States (US) hospital industry has become increasingly consolidated through the formation of multi-hospital health systems and networks and the legal merger of institutions under a single license. In relation to the former, health networks are strategic alliances or contractual affiliations of hospitals, in which affiliated institutions retain their individual ownership. Health systems, on the other hand, typically own and operate a core set of hospitals that offer an array of services and products. In many markets across the country, there are now only three to five hospital organizations in operation, after one accounts for their combined ownership or network affiliations.

This chapter investigates whether signing more hospital contracts with Health Maintenance Organizations (HMOs) and Preferred Provider Organizations (PPOs), hospital affiliation in a system, having more system hospital members located in the same area, and increased competition from area hospitals, contributes to improvements in the cost efficiency of U.S. Midwestern hospitals. Hospitals may offer HMOs and PPOs discounts on contracts to provide health care services to firm employees enrolled in HMOs and PPOs (discounts would lead to smaller price mark-ups over costs for hospital services). Enacting policies to enhance cost efficiency may help hospitals maintain a specified level of profits.

There is a large body of literature on the efficiency and productivity of hospitals. Most studies focus on the effects of environmental pressures on hospital efficiency, such as payment systems (Dismuke & Sena, 1999; Sommersguter-Reichmann, 2000), competition (Rosko, 1999, 2004), Sari, 2003), and property rights (Gruca & Nath, 2001). Other studies pinpoint their attention to economic phenomena, such as economies of scale (Lindrooth, Lo Sasso, & Bazzoli, 2003; Dranove & Lindrooth, 2003), economies of scope (Prior & Sola, 2000; Grosskopf, Margaritis, & Valdmanis, 2001; Li & Rosenman, 2001), chain membership (Menke, 1997), economic behavior (Blank & Merkies, 2004), and expense preference (Rodriguez-Alvarez & Lovell, 2004).

There were 4,919 registered, short-term, community hospitals in the 2004 American Hospital Association (AHA) Annual Survey of Hospitals; 60 percent of those hospitals were non-profit (NP), 23 percent of them were public (non-federal government owned and operated), and 17 percent were for-profit (FP). In general, while the absolute number of hospitals in the United States has decreased in recent years, the share of hospitals that are FP has increased. For example, in 1997, the AHA reported 5,057 registered, short-term, community hospitals, of which 59 percent were NP, 25 percent were public, and 16 percent were FP.

The health care industry has been influenced by changes in the market structure and new technological developments during the recent decades. With the new technological developments in medicine, some less complex care moved out of the hospitals that led to decrease in demand for inpatient services. This recent change in hospital care created excess capacity in hospital markets, and therefore hospitals started to explore potential financial gains through horizontal consolidations. This has resulted in a wave of mergers in 1990s, which transformed the US, Canadian and European hospital markets. This, in turn, created concerns among policy makers and researchers in terms of its welfare implications.

Based on the Current Population Survey, 46.6 million Americans did not have health insurance in 2005 (Center on Budget and Policy Priorities, 2006). Lack of insurance is often associated with lower utilization rates, which may in turn adversely affect health status (Ayanian, Weissman, Schneider, Ginsburg, & Zaslavsky, 2000). Since universal health insurance is not provided for in the US, uninsured individuals must either self-pay or rely on charity care provided by hospitals and health clinics. The majority of charity care is produced in the public sector, either at the state, county, or local level (federal hospitals primarily serve a particular segment of the population – e.g., veterans in the case of Veterans Administration hospitals). Public hospital provision of “safety net” hospital services is particularly prevalent in large urban areas (Lipson & Naierman, 1996). These safety net hospitals are defined by the Institute of Medicine as having an “open door policy to serve all patients regardless of their ability to pay and provide substantial levels of care to Medicaid, the uninsured, and other vulnerable patients” (IOM, 2000). Private not-for-profit (NFP) hospitals also provide charity care but to a lesser extent than public providers, especially since the imposition of cost cutting measures both by Medicare and Medicaid (federal programs that fund health care for the elderly and indigent, respectively) and by managed care. Given that approximately 15% of US GDP is allocated to health care, cost cutting measures are laudable; however, care still needs to be provided for individuals who cannot afford it, and the burden of providing this care has to be borne somewhere in the health care system.

One common feature facing diverse health care organisations is a need to compare performance across geographical areas, institutions or individual practitioners. In all health care systems, comparative data help the central government formulate policies for distributing central grants, clinical education, public health, research and tackling disparities. Good comparative data also provides an important resource for decision-making by local managers and clinicians. Through the process usually known as benchmarking, institutions can explore which of their peers are performing best, and seek out detailed qualitative and quantitative information on the context and processes contributing to good performance. Benchmarking also helps local managers set targets and rewards, and permits local electorates pass judgment on their local governments. The central theme of this chapter is to describe how the national hospital benchmarking system (BMS) was implemented in Finland, focusing on the use of BMS for managerial purposes and its impact on hospital care.

Increasing the productivity of publicly funded infrastructure and human capital is an imperative faced by every nation, especially in the health sector, where most nations are struggling with almost continuous increases in the proportion of national budgets spent each year on health and health care. Efficiency is one aspect of the broader issue of productivity within the health sector. This case study examines how a generic Government-funded body, with no specific health or health care mandate, can stimulate improvements in efficiency in Government-funded hospitals and healthcare and thereby contribute to improved productivity in these vital services.

The purpose of this chapter is to suggest a general framework for assessing the efficiency of health care in general, and health care interventions specifically. We begin with a three-pronged overview of assessing performance in health care which begins with what we call the budget or cost side model relating budgets and costs to treatments. Next we proceed to describing an intermediate outputs specification which relates hospital resources to medical outcomes, and we conclude with a final outcomes model which relates the medical outcomes to patient health outcomes. The third model is illustrated with an application to data from Swedish cataract patients.

It is well recognized that hospitals do not operate in a competitive market typically observed in the economics literature, but rather alternative measures of performance must be developed. In other words, health policy analysts, managers, and decision-makers cannot rely on determining efficiency via the typical profit maximizing/cost minimizing firm but develop techniques that address the issues germane to hospital productivity. What has been presented in this book demonstrates the research in both productivity and policy that must attend to this anomaly. In this introductory section, we briefly summarize the theoretical underpinnings of this book.

Ila Semenick Alam is an associate professor. She received her Ph.D. in 1995 from Rice University in Houston, Texas. Her primary fields of specialization are applied econometrics and productivity in various sectors including the airlines, healthcare, and financial institutions. She has published in: Journal of Productivity Analysis, International Economic Review, World Bank Economic Review, Journal of Money, Credit and Banking, Singapore Economic Review, and Productivity and Economic Performance in the Asia-Pacific Region.