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The recent focus on health care quality improvement and cost containment has led some policymakers and practitioners to advocate the adoption of health information technology. One such technology is the Electronic Medical Record (EMR), which is predicted to change and improve health care in the USA. Little is known about factors that influence hospital adoption of this relatively new technology. The purpose of this paper is to determine the national prevalence of EMR adoption in acute care hospitals while examining the organizational and environmental correlates using a Resource Dependence Theoretical Perspective. Significant predictors of hospital EMR use may indicate barriers to use for some hospitals and can be used to guide policy. This study uses a non-experimental cross sectional design to examine hospital EMR use in 2004. A logistic regression approach is used to determine the correlations between hospital EMR use and organizational and environmental characteristics. Hospital EMR use was identified using the HIMSS Analytics data. Organizational and environmental variables were measured using data from the AHA, CMS (financial and case mix) and ARF. Hospital EMR adoption is significantly associated with environmental uncertainty, type of system affiliation, size, and urbanness. The effects of competition, munificence, ownership, teaching status, public payer mix, and operating margin were not statistically significant. Significant predictors of hospital EMR adoption represent barriers that may prevent certain hospitals from obtaining and using EMRs. These hospitals include those that are smaller, more rural, non-system affiliated, and in areas of low environmental uncertainty. Since EMR adoption may be an organizational survival strategy for hospitals to improve quality and efficiency, hospitals that are at risk of missing the wave of implementation should be offered services and incentives to enable them to implement and maintain EMR systems.

Using a sample of Virginia hospitals, performance measures of quality were examined as they related to technical efficiency. Efficiency scores for the study hospitals were computed using Data Envelopment Analysis (DEA). The study found that the technically efficient hospitals were performing well as far as quality measures were concerned. Some of the technically inefficient hospitals were also performing well with respect to quality. DEA can be used to benchmark both dimensions of hospital performance: technical efficiency and quality. The results have policy implications in view of growing concern that hospitals may be improving their efficiency at the expense of quality.

The objective of this study was to examine the change in efficiency of health care systems of 34 OECD countries between 2000 and 2012, a period marked by significant health reform in most OECD countries. This paper uses a novel Dynamic Network Data Envelopment Analysis (DNDEA) model to analyze the efficiency of the public health system and the medical care system of these OECD countries independently along with assessing the efficiency of their overall health system. This helps understand the relative priorities for improving the overall health system. The data for this study was obtained from the OECD Health Facts database. The study findings suggest that countries which improved their public health system were more likely to show overall improvement in efficiency.

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