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The purpose of this paper is to investigate the relationship between hospital adoption and use of computed tomography (CT) scanners, and magnetic resonance imaging (MRI) machines and in-patient mortality and length of stay.

This study used panel data (2007–2010) from 124 hospital corporations operating in Ontario, Canada. Imaging use focused on medical patients accounting for 25 percent of hospital discharges. Main outcomes were in-hospital mortality rates and average length of stay. A model for each outcome-technology combination was built, and controlled for hospital structural characteristics, market factors and patient characteristics.

In 2010, 36 and 59 percent of hospitals had adopted MRI machines and CT scanners, respectively. Approximately 23.5 percent of patients received CT scans and 3.5 percent received MRI scans during the study period. Adoption of these technologies was associated with reductions of up to 1.1 percent in mortality rates and up to 4.5 percent in length of stay. The imaging use–mortality relationship was non-linear and varied by technology penetration within hospitals. For CT, imaging use reduced mortality until use reached 19 percent in hospitals with one scanner and 28 percent in hospitals with 2+ scanners. For MRI, imaging use was largely associated with decreased mortality. The use of CT scanners also increased length of stay linearly regardless of technology penetration (4.6 percent for every 10 percent increase in use). Adoption and use of MRI was not associated with length of stay.

These results suggest that there may be some unnecessary use of imaging, particularly in small hospitals where imaging is contracted out. In larger hospitals, the results highlight the need to further investigate the use of imaging beyond certain thresholds. Independent of the rate of imaging use, the results also indicate that the presence of CT and MRI devices within a hospital benefits quality and efficiency.

To the authors’ knowledge, this study is the first to investigate the combined effect of adoption and use of medical imaging on outcomes specific to CT scanners and MRI machines in the context of hospital in-patient care.

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.

Reviews available literature on gender bias and the process of medical care. Current findings point to possible gender bias in treatment protocols for kidney and cardiac patients. Other clinical conditions have not been studied. Identifies methodological challenges to such research and discusses the need for further research.

Researchers, policymakers and hospital managers often encounter numerous quality measures when assessing hospital quality. The purpose of this paper is to address the challenge of summarising, interpreting and comparing multiple quality measures across different quality dimensions by proposing a simple method of constructing a composite quality index. The method is applied to hospital administrative data to demonstrate its use in analysing hospital performance.

Logistic and fixed effects regression analyses are applied to secondary admitted patient data from all hospitals in the state of Victoria, Australia for the period 2000/2001–2011/2012.

The derived composite quality index was used to rank hospital performance and to assess changes in state-wide average hospital quality over time. Further regression analyses found private hospitals, day hospitals and non-acute hospitals were associated with higher composite quality, while small hospitals were associated with lower quality.

The method will enable policymakers and hospital managers to better monitor the performance of hospitals. It allows quality to be related to other attributes of hospitals such as size and volume, and enables policymakers and managers to focus on hospitals with relevant characteristics such that quantity and quality changes can be better understood, monitored and acted upon.

A simple method of constructing a composite quality is an indispensable practical tool in tracking the quality of hospitals when numerous measures are used to capture different aspects of quality. The derived composite quality can be used to summarise hospital performance and to identify factors associated with quality via regression analyses.

In Sweden, hospital stays, deaths, sick‐ listings and censuses have long been stored on electronic media. The purpose of the study was to apply post‐hospital survival measures to hospitals having differing degrees of specialization by linking existing data in censuses and in‐patient registers. In‐patient records totaling 3.6 million were collected. They were linked to the 1985 and 1990 censuses regarding patients’ background data, and the national insurance register. Results found that the survival was longer and the return to work quicker when hospitals were well staffed, had competent personnel and many specialties. In general, small hospitals were worse off in all aspects. Concludes that small hospitals should be given better support.

Purpose – The aim of this paper is to examine the diffusion of a new surgical procedure with lower per-case cost and how its diffusion path is affected by the simultaneous introduction of a new drug class that may be an effective treatment to prevent surgery. In particular, we examine whether a process of technology substitution exists that influences the diffusion process of the surgical technology. Given their different cost implications, the interaction of these two different technologies, surgery and drug intervention, is relevant from the perspective of health expenditure. This is of particular interest in health care as technology adoption and diffusion has been cited as a major driver of expenditure growth. Such expenditure growth has been increasingly targeted through the use of market-orientated policy tools aimed at increasing efficiency. Our research is thus addressing the question of how economic incentives influence the diffusion process and we discuss the impact of a set of incentives on hospital behavior.

Design/methodology – Hospital admission data for the financial years 1998/1999 to 2007/2008 in England are used to empirically test the contribution of prescription uptake and market-oriented reforms. Dynamic panel data models are used to capture any changes in technology preference during the period of study.

Findings – Our results suggest that the hospital sector exhibits a strong new technology preference, tempered by the interaction of competition for patients and the ability of the primary care sector to substitute treatments.

Value/originality – Given the current fast technological change, we examine the technological race occurring in the health care sector. We account simultaneously for the diffusion of different technologies not only within the same typology but also with technologies of a different class.

In Saudi Arabia, quality management receives a significant amount of attention in higher education. In medical education, specifically, Saudi colleges have been fully engaged with quality assurance and accreditation since 1992, under the supervision of the Saudi Council for Health Specialties. Along with the quality standards determined by accreditation agencies, the perspective of employers on the quality of learning and teaching needs to be acknowledged. The needs of medical employers can be translated into quality standards for medical education to help overcome the perceived deficiencies which lead to poorly equipped graduates. This chapter explores how employers conceptualize quality in medical education with an emphasis on learning and teaching and employers’ perspectives on the quality attributes of medical graduates. This chapter is based on interviews with 14 medical employers in Saudi Arabia. From the employers’ perspective, a high-quality medical education is marked by high quality educational systems, curricula, faculty members, and medical training. Additionally, medical graduates must attain a balance of soft skills, practical and clinical skills, and theoretical medical knowledge. Understanding employers’ perspectives on quality in medical education will complement our existing understanding of quality in medical education.

Hospital managers are confronted with decisions that have to account for multiple objectives, which may be in conflict with regard to efficiency and quality of care. In empirical studies occupancy and staffing ratios as well as in‐hospital mortality are frequently used measures for efficiency and quality‐of‐care, respectively. Efficiency and quality measures vary on a daily basis. However, most empirical studies fail to take this variation into account, especially because data of daily staffing levels are lacking. The paper seeks to exploit the notion that staffing levels are planned according to expected occupancy levels, i.e. estimated daily occupancy levels account for unobserved daily staffing levels.

Using administrative data from 2004 for a sample of 62 departments in 33 German hospitals, the relation between daily occupancy levels and in‐hospital mortality count on the department level is analyzed. In an OLS‐framework the paper estimates daily occupancy level for all departments and then uses the predicted occupancy levels in a zero‐inflated Poisson (ZIP) regression framework to explain in‐hospital mortality count.

The results show a potential trade‐off relation between predicted occupancy rates and mortality. More specifically, the paper finds that the trade‐off relation is less pronounced in hospitals with a higher number of available staff per bed.

First, the paper shows evidence for a negative trade‐off between measures of managerial and medical performance on a day‐to‐day basis. Second, interactions between single measures of efficiency are modeled, namely predicted occupancy rate and staff per bed ratios, and policy implications are developed. Third, first empirical results in this respect using German data are presented.

The purpose of this paper is to analyze the influencing factors of patients’ financial burden through a case study of hospital on public‒private partnerships (PPP) model (Chaonan Minsheng Hospital of Guangdong Province) and provide some useful information to policymakers for better development of hospitals on PPP model.

There are total six indicators that are defined as patients’ financial burden, basing on the policy of “indicators of medical quality management and control on the third level large general hospital (2011 edition),” issued by Chinese Government. In total, 23 potentially influencing factors of patients’ financial burden for hospital on PPP model were chosen from the above policy. The five-year (2007‒2011) data for the above 29 indicators come from statistic department of hospital on PPP model. Grey relational analysis (GRA) was applied to analyze the influencing factors of patients’ financial burden for hospital on PPP model.

A clear rank of influencing factors of patients’ financial burden is obtained and suggestions are provided from results of GRA, which provide reference for policymakers of hospital on PPP model. The five main influencing factors of patients’ financial burden for hospital on PPP model, in sequence, are rescuing critical ill patients on emergency, rescuing critical ill inpatients, inpatient bed occupancy rate, working days per bed and medical building area.

The study on the influencing factors of patients’ financial burden for hospital on PPP model not only provides decision-making for policymaker of hospital and controlling of medical expenditure but also contributes to release patients’ financial burden for hospitals on PPP model.

The paper's purpose is twofold: to provide a predictive model for estimating in‐hospital mortality rates after coronary artery bypass grafting (CABG) in Spanish autonomous regions (AR) after adjusting relevant factors; and to determine whether there is a difference between expected and observed mortality rates.

All patients registered in a minimum basic data set (MSBD) undergoing CABG between 2000 and 2004 were selected. After bivariate analysis to explore associations between in‐hospital death and other variables, a multivariate analysis using logistic regression was conducted. The predictive model was evaluated using calibration and discrimination techniques. Standardized mortality ratios by AR were calculated.

The expected Spanish in‐hospital mortality rate after CABG was 7.68 and the observed rate was 7.69 deaths per 100 operations. Discrimination obtained with the model resulted in an area under the curve of 0.70 (95 per cent CI, 0.69‐0.71). When each AR's mortality rate is calculated and compared with the observed rate, some ARs present an observed mortality rate higher or lower than the expected rate according to adjusted variables in the model.

The MSBD registry does not contain patients' critical data, such as arterial damage severity, or in which hospital procedures were performed.

There are factors related to individual patient variation, financial resources or healthcare quality in different ARs, which should be investigated in follow‐up studies.

The paper shows that, although the global expected mortality rate is almost the same as the observed Spanish mortality rate, this similarity disappears when AR rates are compared.