Emergency medical resource allocation among hospitals with non-regressive production technology: A DEA-based approach

Can Comprehensive Medical Reform Improve the Efficiency of Medical Resource Allocation? Evidence From China

Objectives: To evaluate the impact of comprehensive medical reform on the efficiency of medical resource allocation in China. Methods: This study employs the Slacks-Based Measure- Directional Distance Function (SBM-DDF) to estimate the efficiency of medical resource allocation (MRAE) in China, using panel data from 30 provinces during 2009-2021. Moreover, a multi-period Difference in differences (DID) model is developed to explore the effect of the comprehensive medical reform pilot (CMRP) strategy on efficiency of medical resource allocation in China. Results: The results show that the average value of China's medical resources allocation efficiency is 0.861 during the sample period. Coastal area has a higher MRAE than that in the inland area. The DID results show that the comprehensive medical reform pilot strategy has a good, long-lasting impact on the efficiency of medical resource allocation. And the results remain valid after a series of robustness analysis. Additionally, the comprehensive medical reform policy has heterogeneous impact on efficiency of medical resource allocation. The promotion effect is only statistically significant in the eastern and central regions, the groups of higher MRAE and larger population size. Conclusion: China's comprehensive medical reform policy can effectively promote the improvement of regional efficiency of medical resource allocation.

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA's status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.

Research on equity of medical resource allocation in Yangtze River Economic Belt under healthy China strategy

Objective

This study aimed to assess the fairness of medical resource allocation in the Yangtze River Economic Belt, based on the Healthy China strategy. It aimed to identify the issues with resource allocation fairness and provide optimization suggestions.

Methods

To assess the allocation fairness from a geographical population perspective, the study used the Health Resource Concentration and Entropy Weight TOPSIS methods. Additionally, the study analyzed the allocation fairness from an economic level angle, using the Concentration Curve and Concentration Index.

Results

The study found that the downstream area had higher resource allocation fairness than the midstream and upstream areas. The middle reaches had more resources than the upper and lower reaches, based on population concentration. The Entropy Weight TOPSIS method found that Shanghai, Zhejiang, Chongqing, and Jiangsu had the highest comprehensive score index of agglomeration. Furthermore, from 2013 to 2019, the fairness of medical resource distribution gradually improved for different economic levels. Government health expenditure and medical beds were distributed more equitably, while general practitioners had the highest level of unfairness. However, except for medical and health institutions, traditional Chinese medicine institutions, and primary health institutions, other medical resources were mostly distributed to areas with better economic conditions.

Conclusion

The study found that the fairness of medical resource allocation in the Yangtze River Economic Belt varied greatly based on geographical population distribution, with inadequate spatial accessibility and service accessibility. Although the fairness of distribution based on economic levels improved over time, medical resources were still concentrated in better economic areas. The study recommends improving regional coordinated development to enhance the fairness of medical resource allocation in the Yangtze River Economic Belt.

Resource Allocation Efficiency of Urban Medical and Health Financial Expenditure Under the Background of Employees' Health

Introduction

The study proposes a method based on difference-in-differences (DID) to improve the resource allocation efficiency of medical and health financial expenditure to better guarantee the health level of enterprise employees. The DEA method is utilized to measure the comprehensive technology, pure technology, and scale as the resource allocation efficiency values of urban medical and health financial expenditure.

Methods

The proposed method includes the use of DEA to measure the resource allocation efficiency values of urban medical and health financial expenditure. The benchmark regression model and DID model are used to analyze the impact effect, robustness, and parallel trend of the policy.

Results

The study shows that the proposed method effectively evaluates and analyzes the impact of medical comprehensive reform on the resource allocation efficiency of urban medical and health financial expenditure. The comprehensive medical reform can improve the comprehensive efficiency and scale efficiency of urban medical and health financial expenditure, leading to improved resource allocation efficiency of urban employees' medical and health financial expenditure. The results also indicate a significant positive effect on the time trend, which can have a long-term impact and effectiveness.

Discussion

The proposed method can provide useful insights into the resource allocation efficiency of medical and health financial expenditure, which can help improve the health level of enterprise employees. The study suggests that comprehensive medical reform can be an effective way to improve resource allocation efficiency and guarantee the health of employees in urban areas. Further research can be conducted to evaluate the impact of medical reform on other aspects of health care, such as quality and accessibility.