BackgroundThe astonishing economic achievements of China in the past few decades have remarkably increased not only the quantity and quality of medical services but also the inequalities in health resources allocation across regions and inefficiency of the medical service delivery.MethodsA descriptive analysis was used to compare the inequities in inputs and outputs of the provincial medical service systems, a non-radial super-efficiency data envelopment analysis model was then used to estimate the efficiency, and a regression analysis of the panel data was used to explore the determinants.ResultsThe inputs and outputs of most provincial medical service systems increased gradually from 2009 to 2014. Overall, the eastern region allocated more human and capital resources than the other two regions, and produced more than 50% of the total outpatient and emergency room visits, whereas the western region produced more inpatient services (about 30% of the total volume of inpatient services) according to the distribution of the population. The average efficiency scores of the provincial medical systems in China’s mainland were 0.895, 0.927, 0.929, 0.963, 0.977 and 0.968 from 2009 to 2014, with a slight average improvement of 1.60%. The efficiency score of each provincial medical service system varied greatly from one another: Tibet (1.475 ± 0.057) performed extremely well, whereas several others including Heilongjiang (0.579 ± 0.001) performed poorly. Furthermore, the proportion of high-class medical facilities was negatively associated with efficiency, whereas the proportion of the vulnerable population, the per capita Gross Domestic Product, the proportion of the illiterate population and the improvement of primary health care had positive effects on efficiency.ConclusionInequity in health resources allocation and service provision existed across the regions, but not all the gaps have begun to narrow since 2009. The difference of efficiency was great among provincial medical service systems but minor across regions, and the score changed very little over time. More importantly, the central region held the lowest average efficiency score in the past 6 years, while the western region held the largest average efficiency score at the first 5 years, which should receive enough attention of the government and decision-makers. In practice, efficiency was related to many complicated factors, indicating that the improvement of efficiency is a complex and iterative process that requires the strong cooperation of many sectors.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5084-7) contains supplementary material, which is available to authorized users.

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Objective Certified Electronic Health Records (EHR) have been shown to improve the health service quality in some health settings, but there is scant evidence related to its adoption in psychiatric hospitals. This paper aimed to examine the relationship between certified EHR adoption and patient experience across psychiatric hospitals in the United States.

Objective: The aim of this study was to explore the relationship between hospital characteristics and certified electronic health record (EHR) adoption in psychiatric hospitals in the US. Methods: Data were drawn from the American Hospital Association Annual Survey Database and the Centers for Medicare and Medicaid Services Hospital Compare data sets in 2016. Binary logistic regression analysis and χ 2 tests were performed to examine the relationship between certified EHR adoption and hospital characteristics. Results: Of 1,059 psychiatric hospitals in the US, 502 (47.4%) have adopted certified EHR technology. Large hospitals (OR 2.29, 95% CI 1.52-3.44; p<0.001), not-for-profit hospitals (OR 1.74, 95% CI 1.22-2.49; p=0.008), and hospitals participating in a network (OR 1.78, 95% CI 1.34-2.37; p<0.001) were more likely to adopt certified EHRs. Hospitals in the northeast were less likely to implement certified EHRs compared to other regions. However, there was no significant association found between EHR utilization and system affiliation, urban location, teaching status, or participation of health-maintenance organizations and preferred provider organizations. Conclusion: The study results suggested variations in EHR adoption according to hospital location, size, ownership, and network participation. This study fills a gap in previous work on certified EHR adoption that focused exclusively on general hospitals, but overlooked psychiatric hospitals. Future policies designed to influence the implementation of certified EHRs should take into consideration how hospital size, ownership, and network-affiliation status affect certified EHR adoption among psychiatric hospitals.

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