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Objective. To quantify the association between obesity and somatic hospital costs and number of overall somatic hospital contacts – number of inpatient admissions, number of outpatient visits, and number of emergency department visits – based on anthropometric measurements of waist circumference (WC) and information from The National Patient Registry and The Danish Case-Mix System (DRG).

Participants. The study population consisted of two sub-samples from the Inter99 study at Research Centre for Prevention and Health in 1999–2001. One sub-sample used WC as an indicator for obesity (n=5,151), whereas the other used BMI as an indicator for obesity (n=4,048). Using WC, obesity was defined as WC > 102cm for men and > 88cm for women. Normal weight was defined as circumference < 94cm for men and < 80cm for women. Using BMI, obesity was defined as BMI > 30kg/m2, whereas individuals with BMI=18.5–24.9kg/m2 were defined as normal weight. Individuals with BMI < 18.5kg/m2 were excluded from both sub-samples.

Design. We undertook a 3-year retrospective study of the relationship between obesity and use of hospital resources. Data on hospital contacts and costs were obtained from The National Patient Registry and DRG. Analyses were performed using two-part models and Poisson regression. Outcome variables were costs and hospital contacts.

Results. This study has demonstrated that obese individuals have a greater use of hospital services and greater hospital costs compared with normal weight individuals. When using WC as an indicator for obesity, mean hospital costs were 33.8% greater among obese women and 45.3% greater among obese men in a 3-year period but the differences were not significant. When using BMI to measure obesity, obese men had significantly greater costs (57.5%) than normal weight men.

Furthermore, obese men and women (indicated byWC) had an increased number of hospital contacts compared with normal weight individuals (rate ratio 1.32, 95% CI 1.21–1.43 for men and 1.20, 95% CI 1.11–1.28 for women) including inpatient admissions, outpatient visits, and emergency department visits. The same trends were seen when obesity was indicated by BMI.

Research suggests that improving hospital work environments and solving the nursing shortage are critical to improving patient care. The Houston–Galveston region created an aggressive approach to this issue by forming an unusual coalition of business, university, and hospital leaders and using a quality improvement approach. Four years later, the project has achieved over 40% participation among hospitals in the 13-county region and includes 50 hospitals employing approximately 15,000 registered nurses. The data that have been collected by this collaborative to date suggests that hospitals are taking action to improve outcomes by modifying their key initiatives to address the attributed causes of poor work environments. From 2004 to 2005, executives of top performing hospitals increasingly attributed successful work environment outcomes to an emphasis on management development and executive-driven initiatives, de-emphasizing specific employee behavior, process, and outcome-based initiatives.

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.

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.

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.

Whereas many researchers have examined the way in which health institutions have been transformed through funding modalities, and particularly through prospective payment systems (PPS), few have investigated the architecture of these systems, that is, costs and cost variance. Focusing on the study of costs and on the production of hospital rates, this chapter shows that the French PPS, called “rate per activity” made possible what we call a policy of variance. For health policymakers, the aim was to make the different accounting figures between hospitals, and between ways of practising healthcare, visible, in order to reduce these variances. This policy was attended by uncertainty in the processes of quantification, which led to metrological controversies. As a consequence of the issues around the way of calculating costs, some accounts and calculations were redone. In this chapter, we consider the case of metrological controversy over the remuneration of costs for cystic fibrosis patients’ hospital stays, and over the action of a patient organization that criticized the costs calculated officially. It leads to the analysis of the way calculative infrastructures, as cost accounting and rates, are challenged, and how some actors try to stabilize them.

Hospitals are complex organisations accounting for most of total health expenditure. They play a critical role in providing care to patients with high levels of need. A key policy concern is that patients receive high quality care. Policymakers have attempted to influence hospital quality in different ways. This chapter focuses on three key policy levers: the extent to which hospital competition and higher hospital tariffs (of the DRG type) can stimulate quality, and whether non-profit hospitals provide higher or lower quality than for-profit ones. The chapter outlines key methodological challenges and selectively reviews the main findings from the literature. While several studies suggest that hospital competition reduces mortality rates for heart attack cases when hospital tariffs are fixed (under a DRG system), at this stage is unclear whether the effect holds across a range of quality indicators. Moreover, the limited literature on hospital mergers tends to suggest that hospital quality does not change following a merger. Finally, whether non-profit hospitals provide higher or lower quality varies across regions and institutional arrangements. The economic theory suggests several mechanisms with opposite effects on quality. To guide policy, future work needs to further unpack the various mechanisms through which these three key policy issues affect hospitals incentives.

Inappropriate cardiac monitoring leads to increased hospital resource utilization and alarm fatigue, which is ultimately detrimental to patient safety. Our institution implemented a continuous cardiac monitoring (CCM) policy that focused on selective monitoring for patients based on the American Heart Association (AHA) guidelines. The primary goal of this study was to perform a three-year median follow-up review on the longitudinal impact of a selective CCM policy on usage rates, length of stay (LOS), and mortality rates across the medical center. A secondary goal was to determine the effect of smaller-scale interventions focused on reeducating the nursing population on the importance of cardiac alarms.

A system-wide policy was developed at The Ohio State University in December 2013 based on guidelines for selective CCM in all patient populations. Patients were stratified into Critical Class I, II, and III with 72 hours, 48 hours, or 36 hours of CCM, respectively. Pre- and post-implementation measures included average cardiac monitoring days (CMD), emergency department (ED) boarding rate, mortality rates, and LOS. A 12-week evaluation period was analyzed prior to, directly after, and three years after implementation.

There was an overall decrease of 53.5% CMDs directly after implementation of selective CCM. This had remained stable at the three-year follow-up with slight increase of 0.5% (p = 0.2764). Subsequent analysis by hospital type revealed that the largest and most stable reductions in CMD were in noncardiac hospitals. The cardiac hospital CMD reduction was stable for roughly one year, then dipped into a lower stable level for nine months, then returned to the previous post-implementation levels. This change coincided with a smaller intervention to further reduce CMD in the cardiac hospital. There was no significant change in mortality rates with a slight decrease of 3.1% at follow-up (p = 0.781). Furthermore, there was no significant difference in LOS with a slight increase of 1.1% on follow-up (p = 0.649). However, there was a significant increase in ED boarding rate of 7.7% (p < 0.001) likely due to other hospital factors altering boarding times.

Implementing selective CCM decreases average cardiac monitoring rate without affecting LOS or overall mortality rate. Selective cardiac monitoring is also a sustainable way to decrease overall hospital resource utilization and more appropriately focus on patient care.

Since Jan. 1, 2019, the Centers for Medicare and Medicaid Services' (CMS) rule requiring hospitals publish their “standard charges” (also called “charge description masters” or “chargemasters”) in a public, machine-readable format has been in effect. The research at hand assesses hospital compliance with the federal regulation. In addition, a sentiment analysis of the chargemaster webpages compared to hospital homepages is performed to assess the consumer friendliness of the content in terms of language usage. A stratified sample of 212 hospitals was used to conduct observations. Strata were based on patient satisfaction scores drawn from the Hospital Consumer Assessment of health care Providers and Systems survey, and controls for hospital bed size and geographic US census region were utilized from the American Hospital Association Annual Survey. Descriptive statistics are presented, and chi-square testing is used to test for statistically significant differences. Key results are presented for compliance and sentiment. Most hospitals' websites are not presenting chargemaster data in a way that is readily collectable or comparable to other facilities. In addition, the tone of language used on chargemaster transparency webpages is generally more negative than that of hospitals' homepages. In particular, the messaging on transparency pages routinely suggests consumers to not use the data for decision-making purposes.

The goal of this contribution is to shed light on the benefits for research in health care coming from the use of administrative data, especially in terms of measuring hospitals’ outcomes. The main approaches to health outcome evaluation are reviewed and the possible improvements deriving from the use of administrative data are highlighted. Administrative data may be an essential element in the process of gathering to the public true rankings of health care organizations, reducing the degree of asymmetric information that typically arises in health care. Patients will be more aware of the best institutions, which will induce most of them to demand to be admitted in them, taking into account the costs associated with distance and with the severity of the illness. This in turn may ask for a reorganization of the sector, closing some organizations and expanding others, having as final goal to improve the health status of the population, without income barriers. This is one of the first attempts to provide an overview of the advantages that administrative data may gather in health care.