Our results indicate that in the 2006–2009 period, substantial technological change was the largest contributor to productivity growth among sample hospitals in Beijing, which is consistent with the findings of Pang & Wang  and Ng . In contrast, the contribution of technological change to TFP growth in Beijing is the highest, which may indicate that Beijing enjoys a more advantageous position to maintain technological advantage, which can help understanding why most of Chinese terminally ill patients would always choose to go to best hospitals in Beijing for their last chances of treatments.
In sample hospitals, although the actual number of open beds (7.83%) and the number of employees (11.24%) did not rise sharply, the fixed assets had grown by 30.7%, which mean that hospitals still managed to expand their capacity and scale by increasing more fixed assets, such as building more facilities, purchasing more high-tech equipments, etc. As a result, the total revenue had grown by 66.67% in the same period. Such finding, to some extent, is consistent with the assertion of Ng  that the observed productivity growth rates from 68% to 94% was resulted from adoption of high-tech treatments in hospitals. There are many factors contributing to this phenomenon: (1) sample hospitals could only get less than 10% subsidies from government funding and were allowed to survive by selling drugs and by providing physician examinations, which in return led to over-prescribing drugs and tests, adopting high-tech treatments, and delivering unnecessary medical care, etc.; (2) the prices for health services provided have long been twisted among Chinese hospital system. Many health services provided e.g. labor have been charged at low prices under cost. However, hospitals can have higher margins from services provided by high-tech equipments, which is a driving factor to increase their number of high-tech equipments; (3) there lacks of specific laws, rules and regulations on how hospitals can be allowed to manage their profits for their own development. If they have large profits and keep them in banks, government will be reluctant to provide additional subsidies. Instead, being in debt renders “more reasonable” to ask for subsidies. Therefore, expanding capacity and scale is hospitals’ rational choice. In order to recoup their investment, hospitals would still resort to make profits on patients if effective monitoring is absent. According to Yip and Hsiao , providers’ profit seeking behaviors will result in waste and inefficiency within the healthcare system itself.
In our results, the growth rates of technological change had dramatically declined each year. Although the adoption of new drugs and medicine can facilitate the cure of difficult and complicated illness of patients and help improve productivity, it is obvious that most of these driving factors do not have cost constraints, which will necessarily result in increased healthcare expenses. It is evidenced in the United States that technological progress is a large contributor to high healthcare spending . More driving factors such as process innovation, new treatment methods, and so on need to be explored. In the meanwhile, more hospital employees need to be trained to increase their expertise. Government, universities and hospitals can collaborate closely to cultivate more graduates for hospitals.
Technical efficiency changes
In our findings, technical efficiency changes had trivial contributions to TFP growth, which is consistent with the findings of most of Chinese researchers, indicating this is a common problem among Chinese public hospitals.
In particular, no hospitals showed improvements in scale efficiency, which may be explained by the fact that fixed assets had grown much faster than the number of employees, indicating more qualified health workers were needed to fill vacancies resulting from capacity and scale expansion. However, hospitals were lack of driving factors to cultivate their own staff and they began competing healthcare professionals against each other. As the whole supply is limited, the flow of these workers from first and second grade hospitals to third-grade hospitals would reduce productivity of first two. Therefore, while governments of all levels are restraining big hospitals’ inclination for larger scales, relevant policies should be in place to encourage them to cultivate more qualified staff to satisfy the needs resulting from over-expansion of hospital capacity and scale. Only when hospitals have sufficient qualified healthcare staff will they be able to maximize scale efficiency.
Other aspects causing stagnation or decline in technical efficiency can be classified as pure technical efficiency problems, which call for better hospital governance, better hospital management, etc. In terms of hospital governance, each sample hospital had multiple governing bodies, which increased coordination and bureaucracy; there lacks of well designed mechanisms, rules and regulations to select competitive directors and clearly define their rights, interests, responsibility and accountability; the limited rights of hospital directors also constrain their abilities to hire, fire, pay, and promote employees, which would reduce the quality of management practices and therefore influence pure technical efficiency. In terms of hospital management, Bloom et al.  held that product market competition, labor regulation, ownership, education, information etc. can help explain management practices within countries or across industries. All these challenges call for deeper hospital reforms to improve hospital governance and hospital management and thus improving pure technical efficiency.
Farrell  divided overall efficiency into technical efficiency and allocative efficiency. Achieving technical efficiency does not necessary imply allocative efficiency will be met. In our study, as we lack of sufficient price information, we focus on measurement and analysis of inputs and outputs in volume terms. In this way, allocation efficiency cannot be measured. However, some actions can still be made to improve allocative efficiency.
First, the tasks of hospital directors and managers should be separated from clinical activities. The nature of bureaucratic tasks of hospital management detracts away from patient care time . It has been common in China that hospital directors and managers not only are engaged in managerial work, but also provide clinical services directly to patients. It would be highly inefficient and costly to transform an excellent doctor to be a director or a manager, since it takes a long time and huge resources to cultivate an expert doctor, especially when hospitals lack of expert doctors due to their over-expansion in capacity and scale. Therefore, directors and managers not participating in clinical activities would improve allocative efficiency.
Second, new provider payment methods and mechanisms can be explored. In sample hospitals, payment methods were all based on fee-for-service. In Beijing, DRG and other mixed provider-payment methods have been introduced and experimented [32, 33], which have demonstrated preliminary advantages to save cost and to reduce waiting time (improvement in efficiency). Government of all levels can enlarge the coverage and find ways to consolidate these achievements, such as providing more funding and policy supports to encourage more volunteer hospitals to apply for new payment methods and mechanisms, which will help improving allocative efficiency.
Third, relevant laws and regulations are required to enable the differentiation of healthcare services provided. International experience suggests that countries where primary care physicians act as gatekeepers are more efficient than countries without gatekeepers . Although Chinese government is applying different reimbursement rates between community health service centers and different levels of hospitals as a way to guide inhabitants to go first to community health service centers, this differentiation cannot be accomplished alone without establishing and implementing relevant government laws, rules and regulations. By differentiating services provided, the proportions of human resource inputs will be greatly optimized and less healthcare cost will be spent with the optimized inputs, improving both technical efficiency and allocative efficiency.
The need for multi-dimensional performance evaluation
It is insufficient for government to use efficiency measurement for policy decision-making, as efficiency is just one dimension to drive performance, while effectiveness, appropriateness, clinical quality, patient safety, financial equilibrium etc. are all relevant to the performance of a healthcare system . This calls for introduction of a multi-dimensional reporting measurement system. At the end of July 2011, Beijing established a hospital authority within the municipal Bureau of Health to manage 22 municipal third-grade Class A hospitals, 7 of which hospitals are our sample hospitals. It will be of strategic importance if the hospital authority can enable performance benchmarking among their affiliated hospitals, which is both an effective way to improve efficiency and performance and an effective tool to learn best practices from peer hospitals. In this aspect, the experience in Tuscany region in Italy is a good example for China’s purpose [36–38] and Li et al.  explored the possibility of building China’s municipal healthcare performance evaluation system by learning from the Tuscan experience.
Limitations and further development
This research has some limitations. The first one is the limited number of hospitals available for the research. In further research design, the sample can be extended to include all three grades of hospitals in Beijing or in another region, with respective efficiency and productivity analysis for comparison. In this way, the flow of healthcare resources among the three types of hospitals would be much clearer, which would be helpful to identify weakness of hospital system for further improvements in efficiency and productivity.
In our study, bias adjustments of efficiency scores were not conducted due to limitation of Coelli’s approach. In future research, a Bootstrap-Malmquist approach can be applied for more exact results.
Coelli and Rao  pointed out that in Malmquist DEA, the explicit price information is replaced by implicit (or shadow) price information. These implicit prices may differ substantially from market prices, thus result in TFP measures that may differ substantially from those obtained using other methods. Furthermore, the piece-wise linear nature of the DEA surface (and the regular occurrence of slack regions) can result in wide variations in shadow prices, which subsequently lead to significant differences in the weights assigned to different inputs across the sample. Therefore, in future research, alternative methods are encouraged to be applied for robustness check.