Levels, trends, and determinants of effectiveness on the hierarchical medical system in China: Data envelopment analysis and bootstrapping truncated regression analysis

Abstract

BackgroundThe hierarchical medical system (HMS) refers to the classification of treatment according to disease priorities based on severity and difficulty to promote the fairness of medical services for residents, which is regarded as the key to the success of medical reform in China.MethodsIn the past decade of “New Medical Reform,” the efficiency of HMS, including secondary and tertiary hospitals and primary healthcare centers (PHCs), was measured horizontally and vertically by employing the combination of an output-oriented superefficiency slack-based model-data envelopment analysis (SE-SBM-DEA) model with the Malmquist total factor productivity index (MTFP). In the second stage, the overall technical efficiency (OTE) scores were regressed against a set of environmental characteristics and several managerial factors through bootstrapping truncated regression.ResultsOn average, the OTE score in tertiary hospitals was 0.93, which was higher than that in secondary hospitals and PHCs (0.9 and 0.92, respectively). In terms of trend, the OTE of tertiary hospitals declined at first and then increased. The opposite was true of secondary hospitals, in which the APC of the OTE was 10.82 and −3.11% in early and late 2012, respectively. The PHCs generally showed a fluctuating downward trend. In the aspects of productivity, all institutions showed a downturn by an annual average rate of 2.73, 0.51, and 2.70%, respectively. There was a significant negative relationship between the ratio of outpatients to inpatients and tertiary hospitals. Additionally, the medical technical personnel per 1,000 population negatively affected PHCs. In contrast, the GDP per capita had a significantly positive effect on tertiary hospitals, and the number of beds per 1,000 population positively influenced PHCs.ConclusionThe efficiency of medical institutions at various levels in HMS was unbalanced and took the form of an “inverted pyramid.” Multilateral factors influence the efficiency of HMS, and to address it, multi-intervention packages focusing on sinking high-quality medical resources and improving healthcare capacity, and guiding hierarchical medical practice should be adopted.

Assessing Resource Allocation Based on Workload: a Data Envelopment Analysis Study on Clinical Departments in a Class A Tertiary Public Hospital in China

“…That is detrimental to the overall technical improvement of hospitals. used a super-e ciency SBM model to measure the e ciency of secondary and tertiary hospitals and primary healthcare centers within the hierarchical medical system in China and made suggestions [53].…”

Agent-Based Modeling and Simulation (ABMS)on the influence of adjusting medical service fees on patients' choice of medical treatment

“…In response to the third law, in the premise that there is little difference in medical service fees between different levels of medical institutions and residents have the freedom to choose, residents have developed a preference for higher-level hospitals, and common diseases that can be solved in primary medical institutions are "siphoned" to tertiary hospitals [ 26 ]. In addition, a study has confirmed that tertiary hospitals "siphon" mainly target secondary hospitals rather than primary hospitals [ 27 ]. Tertiary hospitals have been in a favorable position in the competition, while primary hospitals have been supported by national policies, and secondary hospitals are in a " sandwich " predicament [ 28 ].…”

New evidence on the real role of digital economy in influencing public health efficiency

“…Ye et al 38 used the super-efficiency SBM-DEA model to measure the medical service efficiency of county-level public general hospitals in Shanxi Province. There are also some scholars 39 conducted horizontal and vertical measurements on the efficiency of medical and health services in secondary and tertiary hospitals and primary medical and health service centers (PHCs) respectively, and found that the HMS efficiency of medical institutions at all levels was unbalanced, showing an “inverted pyramid” shape. On the other hand, it is studied from the macro level of countries and regions.…”

“…Due to the existence of the digital divide, the gap between urban and rural infrastructure, industrial structure and technological cognition has been further widened. From the perspective of employment and income distribution, some studies have found that the influence of digital economy development on urban–rural income gap shows a U-shaped trend of narrowing first and then expanding 39 . The construction of smart cities will also significantly expand the urban–rural income gap in terms of the urban–rural income gap.…”