Resources Allocation and Utilization Efficiency in China’s Healthcare Sector
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Junhao Wang
Abstract
The reasonable allocation of healthcare resources across different levels of healthcare facilities is the key to promoting the tiered diagnosis and treatment approach. The sudden outbreak of COVID−19 underscores the shortage of resources and service capability of China’s primary healthcare facilities. From the perspective of the vertical division of labor in the healthcare service system and based on the quality adjustment and quantitative correction of healthcare workers, this paper comprehensively calculates and analyzes the evenness of resources allocation between hospitals and primary healthcare facilities; and then, combining the theoretical model derivation with China’s empirical data test, this paper demonstrates how the misallocation of healthcare resources affects their utilization efficiency. The results are as below. (1) There are varying degrees of quantity and quality imbalance in various healthcare resources between hospitals and primary healthcare facilities. (2) When other conditions remain unchanged, the more misallocated healthcare resources are, the lower the “actual” utilization efficiency after quality adjustment is. (3) Compared with the absence of price regulation, government price regulation has led to a relative “overtreatment equilibrium” in the healthcare service market. Therefore, measures should be taken to optimize the structure of healthcare resources allocation and improve the efficiency of resources utilization, such as strengthening the government’s healthcare financing function, formulating policies that favor primary healthcare facilities, and encouraging social capital to invest at the community level.
1 Introduction
On June 2, 2020, Chinese President Xi Jinping emphasized at a symposium of experts and scholars that “people’s safety and security is the cornerstone of national safety and security, and human health is the foundation of social civilization and progress. It is imperative to increase investment in science and technology in the field of health and promote the integration of health into all policies. The development of healthcare undertakings has always been in a fundamental position.” Healthcare undertakings, which provide medical services for all people, guarantees their safety and health, constitute an important pillar of stable economic and social development. The Guidelines of the National Healthcare Service System (2015−2020) of the General Office of the State Council in 2015 pointed out that the scale of some public hospitals in China was too large, which squeezed the development space of primary healthcare institutions, causing insufficient service capacity of primary healthcare institutions. The “Thirteenth Five-Year Plan for Health and Wellness” of the State Council in 2016 pointed out that the total amount of healthcare resources in China is insufficient and the structure is unreasonable, which restrict the reform and development of healthcare undertakings. The outbreak of COVID−19 in early 2020 posed a serious threat to people’s lives in China, and also exposed the long-standing imbalance in the allocation of healthcare resources of the country. Academician Dong Jiahong of Beijing Tsinghua Changgung Hospital, noted after participating in the pandemic relief efforts and investigation that “medical resources are not the biggest problem, but the irrational allocation of resources is the core problem”.
The misallocation of healthcare resources in China has been formed against a special economic and social system background. In the period of planned economy, China adopted a unified allocation system of healthcare resources in accordance with the national plan and administrative means. As human resources could not flow freely, the distribution of healthcare resources was relatively stable, and people sought medical treatment nearby. In 1997, the market-oriented reform of healthcare services began, under which the allocation of healthcare resources changed from the planned method of “direct allocation to suppliers” to “subsidizing the demand side with money”, and then through the form of patient consumption flowing to healthcare institutions (Dai and Wang, 2014), the change of allocation means made healthcare resources gradually flow to large hospitals with high technology and good quality, resulting in misallocation of healthcare resources. While system reform leads to the imbalance of resource allocation, stakeholder behaviors further aggravate the imbalance. The first stakeholder is the government. China’s healthcare system adopts an administrative hierarchical model, in which administrative forces determine resource allocation (Du and Zhu, 2016). The higher level a hospital is at, the more government financial investment, the larger the land occupation, the more advanced the equipment, and the higher doctors’ salary level (Zhu, 2017). The next stakeholder is the hospital. As early as in 1989, China began to implement the hospital classification management system. In pursuit of various benefits brought by rating, hospitals excessively pursue scale expansion, exerting a “siphon effect” on primary healthcare resources. The third stakeholder is the patient. Seeing that community-level healthcare institutions are short of resources and backward in technology, a large visits flock to large hospitals.
The contradiction between people’s demand for high-quality healthcare resources and misallocation and insufficient supply of healthcare resources constitutes the main contradiction in the field of healthcare care (Yang and Li, 2016). Misallocation of healthcare resources has led to inefficient operation of China’s medical system, and therefore promoting even healthcare resources allocation is a livelihood issue that the Chinese government should address as a major problem (Zou, 2014).
2 Literature Review
With regard to the allocation of healthcare resources, many research works focus on the urban and rural dimensions. China’s healthcare resources are unbalanced in urban and rural distribution, with unequal resource accessibility (Hu et al., 2013; Jin et al., 2015). This imbalance is reflected in urban and rural financial health expenditure (Wei and Gustafson, 2005), and urban and rural healthcare human resources (Anand et al., 2008; Zhou et al., 2015) and material resources (Dai and Wang, 2014). In recent years, no significant improvement has been made in narrowing the gap in the allocation of healthcare resources and human resources between urban and rural areas, while the gap in human resources has been widening (Yang and Li, 2016). According to Zhu et al. (2014), China’s health fiscal compensation mechanism of “tiered management and a fiscal system with division of revenue and expenditure” aggravates the imbalance of healthcare resources between urban and rural areas. Lv and Zhao (2018) demonstrate that the hospital rating system is the main reason for the imbalance of human resources between urban and rural areas in China.
Meanwhile, the regional allocation of healthcare resources has also caught the attention of scholars. There are regional inequalities in healthcare resources in China (Ling et al., 2011), and within the same city there are such problems as huge differences in healthcare resources and unfair accessibility across different districts (Chen et al., 2019). While there are significant regional differences in the scale of health expenditure in China (Wang, 2007), the differences of public health expenditure in eastern, central and western regions are narrowing (Wang, 2009). The allocation of healthcare material resources across various regions is developing towards a reasonable trend, but the narrowing of the gap among health professionals is relatively slow (Liang and Tang, 2018).
In recent years, the allocation and utilization of healthcare resources in the vertical healthcare service system has also attracted scholars’ attention. Lv (2009) maintains that the structural contradiction in the allocation of healthcare resources is mainly the polarization between urban and rural areas, and between large urban hospitals and primary healthcare institutions. Unreasonable expansion of large public hospitals limits the development of other healthcare institutions (Wang and Cao, 2016), and the healthcare system with administrative hierarchy renders primary healthcare institutions at the lowest level lacking social resources most (Du and Zhu, 2016). Jiang et al. (2020) find that the resources of China’s primary healthcare service system are under-utilized, tertiary hospitals are overcrowded, and the resource allocation structure of the healthcare service system is unreasonable.
In the existing research a variety of methods have been selected to measure the degree of misallocation of healthcare resources in China, mainly including the Lorentz curve, the Gini coefficient, the Theil index, the modified weighted coefficient of variation, the equalization index and so on. However, the research on the utilization efficiency of healthcare resources mainly uses the data envelopment analysis method to compare different provinces or regions (Fang and Zhao, 2013), but little research has been done on the internal influence relationship between the allocation of healthcare resources and their utilization efficiency.
To sum up, the existing literature mainly focuses on the urban-rural or regional gap, with relatively little attention paid to the longitudinal healthcare service system; the research on the imbalance of healthcare resources allocation mostly stays at the level of quantity imbalance, while the deep-seated problem of quality heterogeneity still needs to be studied in depth; existing research is still weak in answering the question of how the allocation of healthcare resources affects their utilization efficiency in China. In view of this, this paper attempts to innovate from the following aspects. Firstly, focus on the allocation structure of healthcare resources between hospitals and primary healthcare institutions,[1] in an attempt to analyze the hierarchical division of labor and complementary functions of China’s healthcare service system. Secondly, the paper discusses the balance between quantity and quality of healthcare resources, thus breaking through the limitation that most previous studies only focus on quantity imbalance. Thirdly, based on theoretical model derivation and testing of empirical data in China, this paper addresses the influence path of misallocation of healthcare resources on their utilization efficiency.
3 Estimation and Analysis of the Degree of Misallocation of Healthcare Resources
3.1 Development of an Index System
In this paper, an index system is constructed from three levels, i.e., healthcare human resources, material resources and financial resources, to analyze the balance of resource allocation between hospitals and primary healthcare institutions. Among them, human resources are mainly indexed by health professionals. To further analyze their internal structural differences, practicing (assistant) doctors and registered nurses are selected at the same time as the measurement indexes of healthcare human resources. The number of beds is usually used as an index of material resources. In this paper, the index of total value of equipment above RMB10000 yuan is added to highlight the quality gap of healthcare material resources. The total assets (RMB10000 yuan) are selected as a measurement index of financial resources, which represent the comprehensive strength of healthcare institutions in terms of equipment, beds, building area, etc., and reflect their comprehensive quality. At the same time, to investigate the government’s financial support to healthcare institutions at all levels and analyze the future policy orientation, the average fiscal subsidy income[1] (RMB10000 yuan) is taken as a measurement index. The specific evaluation index system is presented in Table 1.
The Index System for Evaluating the Equilibrium of Allocation of Healthcare Resources
| Primary index | Secondary index | Tertiary index |
|---|---|---|
| The allocation of healthcare resources in hospitals and primary healthcare institutions | Human healthcare resources for AL | Number of health professionals AL1 |
| Number of practicing (assistant) doctors AL2 | ||
| Number of registered nurses AL3 | ||
| Material resources for healthcare AK | Number of beds AK1 | |
| Total value of equipment over RMB10,000 yuan (RMB10000 yuan) AK2 | ||
| Financial resources for healthcare AF | Total assets (10000 yuan) AF1 | |
| Average financial assistance income (RMB10,000 yuan) AF2 |
3.2 Estimation of the Degree of Imbalance
Over a long period of time, China’s high-quality healthcare resources have been concentrated in cities (Yang and Li, 2016), and the distribution of healthcare resources is unbalanced (Zou, 2014). According to the index system in Table 1, this paper will calculate and analyze, in detail, the degree of misallocation of the main healthcare resources in hospitals and primary healthcare institutions.
3.2.1 Data Source and Index Adjustment
The research data in this part mainly come from China Health Statistics Yearbook, China Health Development Statistical Bulletin and China Health Database. The available data of primary healthcare institutions began in 2010. According to the composition of primary healthcare institutions in China Health Statistics Yearbook, this paper calculates the relevant data of each index[1] to obtain the data of primary healthcare institutions in 2009. Therefore, the window period of the data in this study is from 2009 to 2019.
Human resources are the most important healthcare resources with an important impact on the quality of healthcare services and playing a decisive role in the development of health undertakings. Liu et al. (2015) adjust the quality of healthcare human resources in Shaanxi Province from two aspects: the number of years of education and the daily average number of diagnosis and treatment of a doctor. Lv and Zhao (2018) take doctors’ service ability (per capita amount of diagnosis /per capita amount of service coverage) as the doctor’s quality adjustment coefficient. Limited to the coefficient conversion of a single index, the existing research can hardly reflect, comprehensively, the resource allocation gap caused by the heterogeneity of healthcare human resources quality. In view of this, this paper attempts to adjust the quality of healthcare human resources from three aspects: educational background, professional and technical qualifications and service ability. The specific indexes are shown in Table 2.
Indexes for the Coefficients of the Quality Adjustment of Healthcare Human Resources
| Variable for quality adjustment | Index | Implications |
|---|---|---|
| Degree | Proportion of health professionals with a graduate degree, bachelor’s degree, junior college degree, secondary school diploma, high school diploma or below | The quality of healthcare human resources measured by educational background |
| Professional and technical qualifications | Proportion of health professionals with a professional title, senior, associate, intermediate, assistant or primary | The quality of human resources in healthcare measured from the perspective of professional titles |
| Service ability | The average daily visits | The quality of human resources in healthcare measured from the perspective of service ability |
| The average daily inpatients |
Note: (1) The professional qualifications in China Health Statistics Yearbook also include the proportion of personnel with “unknown” title, who are omitted in calculation. (2) Due to the lack of data on academic qualifications and professional qualifications of primary healthcare institutions, with references to Dai and Wang (2014), the data have been replaced with data from community health service centers.
According to Table 2, the calculation process of the quality adjustment coefficient of each index of healthcare human resources is as follows.
(1) Practicing (assistant) doctors. With regard to the educational background variable of hospitals and primary healthcare institutions, for example, the quality adjustment process is as follows: First, rank the doctors according to their educational background. Second, the ranking is changed into a score, and the kth place is scored n+1−k. Therefore, the first place gets a score of n, and the nth place gets a score of 1 (Guo, 1995). Third, calculate the average score of educational background variables, which is equal to =Σ (the proportion of degree ×score), are recorded as E1 in hospitals and E2 in primary healthcare institutions. For example, in 2009, the average score of academic variables of hospital practicing (assistant) doctors; Finally, the quality adjustment coefficient of qualification variables of practicing (assistant) doctors in hospitals is = (10.6×5+49.9×4+27.1×3+11.3×2+1.1×1)/100=3.576.[1] Finally the quality adjustment coefficient of the degree variables of the practicing (assistant) doctors in hospitals and primary healthcare institutions is K1=E1/E2. The calculation process of quality adjustment coefficient K2 of professional qualification variables is the same as above.
The adjustment coefficient of the service ability variables of the practicing (assistant) doctors K3: the two indexes, “doctors’ average daily visits” and “doctors’ average daily inpatients”, [2] can better reflect their service ability. The calculation process is as follows:
Through the above adjustments, the comprehensive quality adjustment coefficient
K=K1×K2×K3, the “real” number of hospital practicing (assistant) doctors =K×the number of nominal hospital practicing (assistant) doctors. The nominal numbers are the official data on China Health Statistics Yearbook.
(2) Registered nurses. As the work of registered nurses is mainly reflected in their average daily inpatients. The relevant K3 adjustment process is as follows:
The registered nurses’ daily inpatients are inferred from the relevant data of China Health Statistics Yearbook: registered nurses’ daily inpatients=actual number of days of inpatients (day) /average number of registered nurses/365. The relevant K1 and K2 adjustment process is the same as above.
(3) Health technicians. Health technicians mainly consist of practicing (assistant) doctors and registered nurses. Therefore, the average of the quality adjustment coefficient K3 of the two types of personnel is adopted as the quality adjustment coefficient K3 of health technicians. The relevant K1 and K2 adjustment process is the same as above.
Based on the above methods, the quality adjustment coefficients K1, K2 and K3 of various indexes of healthcare human resources of hospitals and primary healthcare institutions and the comprehensive quality adjustment coefficient K are calculated from three aspects, degree, professional qualifications and service ability, as shown in Table 3.
Quality Adjustment Coefficients of Healthcare Human Resources of Hospitals and Primary Healthcare Institutions 2009−2019
| Practicing (assistant) doctors | Registered nurses | Health technicians | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| K1 | K2 | K3 | K | K1 | K2 | K3 | K | K1 | K2 | K3 | K | |
| 2009 | 1.193 | 1.186 | 2.222 | 3.145 | 1.079 | 1.063 | 1.288 | 1.477 | 1.120 | 1.134 | 1.755 | 2.229 |
| 2010 | 1.187 | 1.196 | 1.892 | 2.686 | 1.072 | 1.063 | 1.379 | 1.571 | 1.113 | 1.134 | 1.636 | 2.064 |
| 2011 | 1.198 | 1.164 | 2.156 | 3.007 | 1.072 | 1.083 | 1.435 | 1.666 | 1.116 | 1.108 | 1.795 | 2.221 |
| 2012 | 1.196 | 1.212 | 2.080 | 3.016 | 1.072 | 1.012 | 1.385 | 1.502 | 1.119 | 1.122 | 1.732 | 2.176 |
| 2013 | 1.186 | 1.189 | 2.068 | 2.916 | 1.062 | 0.962 | 1.404 | 1.434 | 1.103 | 1.083 | 1.736 | 2.074 |
| 2014 | 1.155 | 1.193 | 2.344 | 3.229 | 1.002 | 1.084 | 1.460 | 1.587 | 1.105 | 1.073 | 1.902 | 2.255 |
| 2015 | 1.177 | 1.205 | 2.303 | 3.267 | 1.054 | 0.932 | 1.486 | 1.460 | 1.098 | 1.063 | 1.895 | 2.210 |
| 2016 | 1.172 | 1.196 | 2.349 | 3.294 | 1.045 | 0.920 | 1.519 | 1.459 | 1.091 | 1.051 | 1.934 | 2.217 |
| 2017 | 1.164 | 1.185 | 2.308 | 3.182 | 1.033 | 0.913 | 1.573 | 1.483 | 1.083 | 1.041 | 1.940 | 2.186 |
| 2018 | 1.153 | 1.175 | 2.255 | 3.055 | 1.034 | 0.912 | 1.708 | 1.610 | 1.073 | 1.030 | 1.982 | 2.190 |
| 2019 | 1.144 | 1.146 | 2.614 | 3.429 | 1.028 | 0.919 | 1.899 | 1.794 | 1.069 | 1.034 | 2.256 | 2.496 |
3.2.2 Method of Measurement
The equalization index d is a method to measure the income gap. Because of its good statistical properties, it is flexibly applied to other measurement problems related to gap (Li and Zhang, 2005). According to the research purpose, this paper uses the equalization index as a tool to measure the degree of imbalance.[1]
The value range of d is between 0 and 1. The smaller the d value is, the lower the degree of imbalance; the larger the d value is, the higher the degree of imbalance. i represents hospitals or primary healthcare institutions, xi represents various healthcare resource indexes of hospitals or primary healthcare institutions, and
3.2.3 Result and analysis of measurement
The equalization index of various healthcare resources of hospitals and primary healthcare institutions is calculated according to formula (3) and Table 3, as shown in Table 4, in which “before adjustment” and “after adjustment” refers to before and after adjustment of the quality of healthcare human resources.
Equalization Indexes of Healthcare Human Resources of Hospitals and Primary Healthcare Institutions 2009−2019
| Year | AL1 | AL2 | AL3 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Before adjustment | After adjustment | Before adjustment | After adjustment | Before adjustment | After adjustment | AK1 | AK2 | AF1 | AF1 | |
| 2009 | 0.272 | 0.591 | 0.127 | 0.605 | 0.517 | 0.645 | 0.479 | 0.823 | 0.705 | 0.981 |
| 2010 | 0.285 | 0.575 | 0.141 | 0.562 | 0.518 | 0.664 | 0.479 | 0.839 | 0.751 | 0.976 |
| 2011 | 0.308 | 0.615 | 0.153 | 0.607 | 0.535 | 0.693 | 0.500 | 0.842 | 0.764 | 0.968 |
| 2012 | 0.328 | 0.623 | 0.163 | 0.615 | 0.552 | 0.678 | 0.517 | 0.859 | 0.768 | 0.961 |
| 2013 | 0.349 | 0.622 | 0.177 | 0.613 | 0.559 | 0.671 | 0.545 | 0.869 | 0.776 | 0.958 |
| 2014 | 0.371 | 0.662 | 0.196 | 0.656 | 0.573 | 0.708 | 0.564 | 0.867 | 0.784 | 0.957 |
| 2015 | 0.384 | 0.665 | 0.211 | 0.668 | 0.577 | 0.689 | 0.581 | 0.867 | 0.781 | 0.956 |
| 2016 | 0.394 | 0.672 | 0.223 | 0.677 | 0.579 | 0.691 | 0.596 | 0.865 | 0.785 | 0.955 |
| 2017 | 0.396 | 0.669 | 0.229 | 0.670 | 0.572 | 0.690 | 0.600 | 0.819 | 0.735 | 0.951 |
| 2018 | 0.391 | 0.667 | 0.223 | 0.656 | 0.560 | 0.702 | 0.609 | 0.842 | 0.749 | 0.950 |
| 2019 | 0.379 | 0.694 | 0.204 | 0.677 | 0.543 | 0.716 | 0.616 | 0.801 | 0.782 | 0.951 |
By analyzing the size and changing trend of the equalization index in Table 4, it is found that the degree of misallocation of healthcare resources between hospitals and primary healthcare institutions presents the following characteristics.
(1) The imbalance of healthcare human resources is aggravated, with both quantity and quality being out of balance. Before and after the adjustment of the quality of healthcare human resources, the equalization index of each healthcare human resource index has increased greatly, which shows that the imbalance of healthcare human resources is not only reflected in quantity, but also in quality gap. Overall, the misallocation of healthcare human resources between hospitals and primary healthcare institutions is aggravating. Structurally, the imbalance of registered nurses is more serious than that of practicing (assistant) doctors.
(2) The shortage of healthcare resources in primary healthcare institutions is obvious. The equalization index of the number of beds increased from 0.479 in 2009 to 0.616 in 2019, and the degree of imbalance increased year by year. The equalization index of the total value of equipment above RMB10000 yuan (RMB10000 yuan) has been above 0.8, and there is a great difference in the total value of equipment between hospitals and primary healthcare institutions.
(3) A huge gap is present in the supply scale of healthcare services, with primary healthcare institutions still in need of financial input increase. The equalization index of total assets increased from 0.705 in 2009 to 0.782 in 2019. There is a great disparity in total assets between hospitals and primary healthcare institutions. From the total amount of fiscal subsidy income (RMB10000 yuan), the gap between hospitals and primary healthcare institutions in China is not large, but if the amount is allocated to each healthcare institution to obtain the “average fiscal subsidy income (RMB10000 yuan) per unit, the equalization index of hospitals and primary healthcare institutions is as high as 0.9, and there is a great disparity between the fiscal subsidy income obtained by each hospital and that by each primary healthcare institution. The financial investment of primary healthcare institutions is relatively insufficient.
4 Impact of Misallocation of Healthcare Resources on Their Utilization Efficiency
4.1 Research Hypotheses
For the convenience of analysis, this paper assumes that the hospitals are the production department A and the primary healthcare institutions are the production department B (hereinafter referred to as A and B). Most of China’s healthcare institutions are public and aim to promote public welfare to some extent. However, against the backdrops of increasing costs and decreasing financial support, the trend of healthcare institutions pursuing profits is becoming more and more prominent. Therefore, this paper assumes that A and B departments aim at pursuing profits while shouldering certain social responsibilities. According to the hypothesis of Herr (2011), the utility of social responsibility of healthcare institutions is expressed as “δ×the number of patients serving”, in which δ (δ≥0)[1] represents the coefficient of social preference. Therefore, the objective function of departments A and B is to pursue the maximization of the linear combination of profit pursuit and the number of patients serving.
Suppose that the number of healthcare services provided by the A and B departments is q1 and q2 respectively, the market counter-demand function is P=a−bQ (a,b>0), Q is the total amount of healthcare services in the market, Q=q1+q2 and Q is also the total demand for healthcare services when the market is balanced, the marginal cost of Department A is c1, and the marginal cost of Department B is c2. The objective function of Department A is z1=π1+δ1y1 and that of Department B is z2=π2+δ2y2, wherein π1 and π2 represent the profits of the A and B sectors, respectively, y1 and y2 the number of patients served, and the relationship with q1 and q2 is linear; for the purpose of simplifying the analysis, it is assumed that y1=q1 and y2=q2. [2]
Therefore, the objective functions for the A and B departments to maximize utility are:
4.2 Theoretical Derivation and Analysis
4.2.1 Balance in Anarchic Price Controls
Assuming that there is no government price control and no disturbance by other factors, A and B take the quantity of health services as the decision-making variable of competition and maximize their objective function. ∂z1/∂q1 = 0 and ∂z2/∂q2 = 0 are paired. In market equilibrium, the equilibrium services, the total market equilibrium, and the equilibrium price of A and B are respectively:
By formula (6) the following can be derived: ∂q1/∂ δ1 = 2/3b > 0 , and ∂q1/∂ δ2 = −1/3b < 0 . Which indicate that A and B departments in the market competition increase the amount of services with their own social preference coefficient and decrease the amount of services with the increase of the social preference coefficient of the other party.
In the healthcare factor market, assuming that the total amount of resources is S, when the resources are allocated in a balanced manner, the healthcare resources of Department A are λS, and that of Department B is (1−λ)S, of which 0<λ<1, the production functions of the A and B departments are: q1=αλS and q2=β(1−λ)S, of which, α, β>0 and α and β are the technical efficiency of the A and B departments’ providing healthcare services in the healthcare factor market. When the healthcare service market is in equilibrium, the following condition is met:
If they are subject to other factors outside the market mechanism, such as government policy, a certain degree of resource misallocation appears in the A and
B departments, with a large number of high-quality health resources concentrated in Department A, and Department B lacking high-quality health resources. Compared to the balanced allocation of resources, assuming that Department B has θ(θ>0) proportion of resources transferred to Department A, the value of θ can represent the degree of misallocation of health resources, the greater the value, the greater the degree of misallocation.
Resource utilization efficiency refers to the ability to achieve the same output with the least amount of health resources, or to achieve greater output with the same resource input. The less health resources are invested, the more services are provided, and the more efficient the utilization is (Li, 2015). Because of the particularity of healthcare services, analysis of the efficiency of resource utilization is oriented by patient demands. That is, the optimal output Q* at the time of market equilibrium is taken as an orientation to study the optimal input S' required to achieve the optimal output Q*.
Assuming that healthcare resources is misallocated, and the technical efficiency of health services provided by the A and B departments is α' and β' respectively, the new production functions for A and B are:
To achieve a balanced total output Q* in the market, the following conditions need to be met: Q*
Pair (10) and (13), the following formula can be obtained:
Because S and S' represent the investment of healthcare resources required for the same output Q* before and after the misallocation of healthcare resources, S/S' reflects the utilization efficiency healthcare resources. In formula (14), α 'λ + β '(1−λ ) αλ + β (1−λ ) depends on technical efficiency α and α', and the changes of β and β' before and after is the effect of the change of production technology, that is, the effect of “technological efficiency”. (α '−β ')θ (1−λ ) αλ + β (1−λ ) is mainly affected by the degree of imbalance of healthcare resources θ, which can be understood as the effect of “allocation efficiency”. This paper mainly studies the effect of “allocation efficiency”. To eliminate the interference of technical efficiency, it is assumed that the technical efficiency of production remains unchanged, that is, α'=α, and β'=β, and the following formula can be obtained:
Formula 15 shows that the efficiency of resource utilization in the health services market is related to the imbalance between resource allocation θ, the efficiency gap between the two departments α−β and the share of resources in both departments λ.
4.2.2 Equilibrium with Government Price Control
China’s health services industry has always been subject to government price controls aimed at driving down the price of medical services (Du, 2013) and has mainly adopted a price cap policy (Liu, 2016). The A and B departments take the amount of healthcare services as a decision-making variable, assume that the government follows the principle of maximizing social welfare, and formulate the price cap of medical services
Compared with the situation of anarchic price controls, Q* is no longer a stable equilibrium output, healthcare institutions will determine their output under new price
With the misallocation of healthcare resources, the θ proportion of healthcare resources is transferred from Department B to Department A. According to the principle that the healthcare service market is oriented towards patient demand, healthcare resource investment S′ is needed to realize balanced output Q , it can be seen that:
Join (16) and (17), we get:
By comparison of formulas (15) and (18), regardless of whether the government implements price control, the impact of China’s misallocation of healthcare resources on the utilization efficiency healthcare resources remains unchanged: because,
Assuming that
The first derivative is less than 0, i.e. the larger the
In addition, from
4.3 Data Inspection
The prerequisite for the establishment of the previous conclusion is
4.3.1 Index Construction and Weighting
The number of discharges per medical staff member per year and bed utilization rate, etc. are common indicators to measure the efficiency of hospital services (Xu et al., 2011). Cheng and Qian (2012) argue that the indicators of the amount of health service utilization mainly include the number of outpatient and emergency visits, the number of inpatients, hospitalization rate and so on. According to the indicators of healthcare resources in Table 1, this paper pertinently constructs the indicators to measure the utilization efficiency of healthcare resources, as shown in Table 5. [1]
Evaluation Indexes and Calculation Methods for the Utilization Efficiency of Healthcare Resources in China
| Index | Calculation method | The meaning of the index |
|---|---|---|
| Daily visits per health technician | Number of visits/health technicians/251 | Measure the |
| UL1 | utilization efficiency | |
| Daily inpatients per health | Actual total number of days occupied (days) / | of human resources in |
| technician UL2 | number of health technicians / 365 | healthcare |
| Total amount of occupied days | The actual number of days occupied (days) / | |
| per bed UK1 | number of beds | Measure the |
| The output efficiency of equipment valued more than RMB10000 yuan UK2 | Medical income/total value of equipment of over RMB10000 yuan | utilization efficiency of material resources in healthcare |
To evaluate the utilization efficiency gap of healthcare resources between the A and B departments, the ratios of hospitals and primary healthcare institutions with regard to four indexes in Table 5 are taken as the corresponding “utilization efficiency gap of healthcare resources” index. With the index of “average daily visits per health technician” as an example, “average daily visits per health technician of hospitals/ average daily visits per health technician in healthcare institutions < 1” means that for this index, the utilization efficiency of healthcare resources in hospitals is lower than that in primary healthcare institutions, and vice versa. Finally, the four types of efficiency gap indexes are weighted, and the total efficiency gap of healthcare resources utilization between the A and B departments is calculated.
The entropy method,[1] which determines weights according to index variations, is objective. This paper adopts the extremum normalization method for data treatment, and the operation process is as follows:
First, various index data are normalized with the following formula:
(20)In which Yij is the index after normalization, i means the Year research target in this paper, i=1, 2, …, n, j refers to various indexes for measuring the utilization efficiency of healthcare resources, j=1, 2,…,K, with K representing the number of indexes.
Calculate the information entropy of various indexes with the following formula:
(21)(22)Determine weights by the following formula:
(23)
4.3.2 Use Efficiency Gap to Measure Results and Analysis
Based on our research purpose, it is of practical significance to compare the value of indexes with 1. In the final calculation, the gap of healthcare resource utilization efficiency
Utilization Efficiency Gap of Healthcare Resources between Department A and Department B 2009−2019
| UL1 | UL2 | Overall utilization efficiency gap | ||||||
|---|---|---|---|---|---|---|---|---|
| Before adjustment | After adjustment | Before adjustment | After adjustment | UK1 | UK2 | Before adjustment | After adjustment | |
| 2009 | 0.325 | 0.146 | 2.318 | 1.040 | 1.425 | 0.403 | 1.070 | 0.734 |
| 2010 | 0.314 | 0.152 | 2.417 | 1.171 | 1.528 | 0.467 | 1.128 | 0.807 |
| 2011 | 0.314 | 0.142 | 2.511 | 1.131 | 1.579 | 0.525 | 1.175 | 0.826 |
| 2012 | 0.313 | 0.144 | 2.426 | 1.115 | 1.527 | 0.528 | 1.143 | 0.811 |
| 2013 | 0.306 | 0.148 | 2.402 | 1.158 | 1.466 | 0.510 | 1.118 | 0.799 |
| 2014 | 0.313 | 0.139 | 2.466 | 1.094 | 1.496 | 0.548 | 1.151 | 0.802 |
| 2015 | 0.316 | 0.143 | 2.463 | 1.114 | 1.467 | 0.556 | 1.147 | 0.803 |
| 2016 | 0.326 | 0.147 | 2.480 | 1.119 | 1.446 | 0.576 | 1.154 | 0.804 |
| 2017 | 0.336 | 0.154 | 2.500 | 1.144 | 1.442 | 0.775 | 1.207 | 0.866 |
| 2018 | 0.355 | 0.162 | 2.650 | 1.210 | 1.470 | 0.661 | 1.229 | 0.856 |
| 2019 | 0.382 | 0.153 | 2.882 | 1.155 | 1.521 | 0.843 | 1.347 | 0.907 |
By analyzing the gap in healthcare resources utilization efficiency between traditional Chinese medicine hospitals and primary healthcare institutions in Table 6, it is found that the ratio of total healthcare resources utilization efficiency between hospitals and primary healthcare institutions is both greater than 1 before the quality adjustment of healthcare human resources, which indicates that in the data window period from 2009 to 2019
5 Conclusions and Policy Recommendations
5.1 Main Conclusions
This paper measures the misallocation and utilization efficiency of healthcare resources in hospitals and primary healthcare institutions in China, and demonstrates the internal influence mechanism between them based on model derivation and in the light of data from Chinese experiences. The main conclusions are as follows: (1) In the recent decade, the misallocation of human resources in hospitals and primary healthcare institutions is still increasing, which is manifested by the double imbalance of quantity and quality. From the structural point of view, the imbalance of registered nurses is more serious than that of practicing (assistant) doctors. (2) In terms of material resources, with regard to the “total value of equipment above RMB 10000 yuan”, among others, which represents the core competitiveness of medical institutions, is seriously insufficient in primary healthcare institutions, and thus such healthcare institutions have been at disadvantage for a long time. (3) There is a great disparity in total assets between hospitals and primary healthcare institution, and the fiscal subsidies of primary healthcare institutions are relatively insufficient. (4) The resource misallocation in hospitals and primary healthcare institutions is inversely proportional to their “real” utilization efficiency. (5) When subject to price cap control, due to price reduction, healthcare institutions can only maximize their benefits by increasing the supply of medical services, resulting in “over-treatment equilibrium”.
5.2 Policy Recommendations
Healthcare services are obvious for public welfare. But information asymmetry and positive externalities make the market mechanism unable to play its full role in the allocation of healthcare resources, and therefore government policy guidance and intervention are needed. According to the research conclusions, this paper proposes the following policy suggestions.
First, strengthen the government’s health financing function and fully leverage the adjustment role of government finance in the allocation of healthcare resources. Under the background of the reform of the fiscal decentralization system, government health expenditure is often squeezed out by other economic expenditures, and fiscal health investment is relatively insufficient. After the new healthcare reform, although the scale of fiscal investment in health has increased, the proportion of government health expenditure in GDP has been less than 2%. Finance plays a key role in adjusting the allocation of healthcare resources. The government should ensure the scale of healthcare resources investment in total by directly increasing fiscal investment in health and indirectly guiding social capital to participate, so as to meet the people’s growing demand for healthcare services.
Second, we should formulate various health policies favoring the primary healthcare institutions and optimize the allocation structure of healthcare resources. First of all, primary healthcare institutions should be provided with an attractive salary system, and policies such as talent subsidies and preferential purchase of houses should guide the flow of high-quality healthcare human resources to primary institutions. Secondly, according to the need of “tackling areas of weakness” of the healthcare service system, we should increase the input of modern medical equipment in primary healthcare institutions and build healthcare institutions with complete medical equipment, high technical level and appropriate scale. Thirdly, we should optimize the distribution structure of government health finance, and channel the investment of financial resources from big hospitals to primary healthcare institutions, and from cities to rural areas and economically backward areas.
Third, we should deepen the reform of public hospitals and promote the flow of high-quality healthcare resources to primary institutions. The administrative level of public hospitals should be weakened, and the resource allocation mode matching public hospitals with their administrative should be abolished. The rating system of hospitals should be reformed, with the evaluation mechanism “dominated by government administration” replaced by a market-oriented and diversified evaluation mechanism, and the siphon effect of large public hospitals on healthcare resources should be weakened. Public hospitals should be encouraged to build branches at the community level to achieve substantial sinking of high-quality healthcare resources.
Fourth, we should formulate the policy of “asymmetric control” to promote the rapid and healthy development of private hospitals. Tax incentives, direct subsidies and designated medical insurance qualifications may be given to new private hospitals to change the pattern that high-quality healthcare resources are monopolized by public hospitals. Private hospitals should be given the right to reasonably pursue the maximization of economic benefits, while public hospitals should be directed to undertake more public welfare responsibilities, so that private hospitals and public hospitals can play their respective functions and improve the allocation and utilization efficiency of healthcare resources. The government control policy of running medical service with social capital should be refined and the practice behavior of private hospitals should be strictly regulated. Under such constraint, social capital should be encouraged to invest in the construction of diversified healthcare institutions at the community level, and the weakness of healthcare resources in the primary healthcare institutions should be addressed, so that the people at the community level can enjoy better healthcare services nearby their homes.
References
Anand, S., Fan, V., Zhang, J., Zhang, L., Ke, Y., Dong, Z., & Chen, L. (2008). China’s Human Resources for Health: Quantity, Quality and Distribution. The Lancet, 372(9651), 1774−1781.10.1016/S0140-6736(08)61363-XSuche in Google Scholar
Chen, L., Zhang, Z., & Lan, X. (2019). Impact of China’s Referral Reform on the Equity and Spatial Accessibility of Healthcare Resources: A Case Study of Beijing. Social Science & Medicine, 235, 1−9.10.1016/j.socscimed.2019.112386Suche in Google Scholar
Cheng, G., & Qian, Z. (2012). The Conceptual Framework and Measurement Methods of Efficiency Evaluation of the Health System: On the Methodology of Applying Data Envelopment Analysis. China Health Policy Research (Zhongguo Weisheng Zhengce Yanjiu), 5 (3), 52−60.Suche in Google Scholar
Dai, Y., & Wang, Z. (2014). The Allocation of Medical Resources in China: Imbalance and Adjustment. Journal of Northeast University of Finance and Economics (Dongbei Caijing Daxue Xuebao), 1, 47−53.Suche in Google Scholar
Du, C., & Zhu, H. (2016). The Evolution Logic of China’s Urban Healthcare System. Social Sciences in China (Zhongguo Shehui Kexue), 8, 66−89.Suche in Google Scholar
Du, C. (2013). Price Control and Over-Treatment. The Journal of World Economy (Shijie Jingji), 1, 116−140.Suche in Google Scholar
Fang, P., & Zhao, L. (2013). A Study on the Realization of Health Equity Based on the Efficiency of the Use of Health Resources. Population and Development (Renkou Yu Fazhan), 6, 74−83.Suche in Google Scholar
Guo, X. (1995). A New Comprehensive Evaluation Method: Combined Evaluation Method. Statistical Research (Tongji Yanjiu), 5, 56−59.Suche in Google Scholar
Herr, A. (2011). Quality and Welfare in a Mixed Duopoly with Regulated Prices: The Case of a Public and a Private Hospital. German Economic Review, 12 (4), 422−437.10.1111/j.1468-0475.2010.00527.xSuche in Google Scholar
Hu, R., Dong, S., Zhao, Y., Hu, H., & Li, Z. (2013). Assessing Potential Spatial Accessibility of Health Services in Rural China: A Case Study of Donghai County. International Journal for Equity in Health, 12(1), 1−11.10.1186/1475-9276-12-35Suche in Google Scholar
Jiang, S., Gu, Y., Yang, F., Wu, T., Wang, H., Cutler, H., & Zhang, L. (2020). Tertiary Hospitals or Community Clinics? An Enquiry into the Factors Affecting Patients’ Choice for Healthcare Facilities in Urban China. China Economic Review, 63(10), 1−14.10.1016/j.chieco.2020.101538Suche in Google Scholar
Jin, C., Cheng, J., Lu, Y., Huang, Z., & Cao, F. (2015). Spatial Inequity in Access to Healthcare Facilities at a County Level in a Developing Country: A Case Study of Deqing County, Zhejiang, China. International Journal for Equity in Health, 14(1), 1−21.10.1186/s12939-015-0195-6Suche in Google Scholar
Li, D. (2015). Health Economics (Weisheng Jingjixue), Beijing: Chinese Medicine Press.(in Chinese)Suche in Google Scholar
Li, J., & Zhang, D. (2005). The Methodology and Application of the Equal Index of Measured Income Distribution. The Journal of Quantitative & Technical Economics (Shuliang Jingji Jishu Jingji Yanjiu), 6, 33−42.Suche in Google Scholar
Li, J., Zhang, X., Gao, Y., & Zang, J. (2012). Dynamic Evaluation of Intensive Use of Urban Land Based on Improved Entropy Model: A Case Study of Guangzhou. Areal Research and Development (Diyu Yanjiu Yu Kaifa), 31 (4), 118−123.Suche in Google Scholar
Ling, R., Liu, F., Lu, X., & Wang, W. (2011). Emerging Issues in Public Health: A Perspective on China’s Healthcare System. Public Health, 125 (1), 9−14.10.1016/j.puhe.2010.10.009Suche in Google Scholar
Liang, W., & Tang, Y. (2018). A Study on the Spatial Misallocation of Health Resources in China. Health Economics Research (Weisheng Jingji Yanjiu), 9, 68−73.Suche in Google Scholar
Liu, X. (2016). China’s Medical Market: Structure, Regulation and Performance (Zhongguo De Yiliao Shichang: Jiegou, Guize Yu Jixiao), Beijing: Economic Daily Press. (in Chinese)Suche in Google Scholar
Lv, G., & Zhao, M. (2018). More Rating More Imbalance? — Research on Hospital Rating and Doctor HR Allocation in China. Economic Management (Jingji Guanli), 7, 110−127.Suche in Google Scholar
Lv, G. (2009). From Polarization to Balanced Allocation: A Basic Idea for Integrating Urban and Rural Medical Resources. Business and Management Journal (Jingji Guanli), 12, 163−167.Suche in Google Scholar
Wang, J. (2007). A Study on the Scale of Chinese Government Health Expenditures: Three Mistakes and Empirical Evidence. Management World (Guanli Shijie), 2, 27−36.Suche in Google Scholar
Wang, W., & Cao, X. (2016). Can Increasing the Supply of Medical Resources Solve the Problem of “Expensive Medical Care”? — Analysis Based on China’s Inter-Provincial Panel Data. Management World (Guanli Shijie), 6, 98−106.Suche in Google Scholar
Wang, X. (2009). Empirical Analysis of the Equal Level of Public Health Expenditure in China: Quantitative Analysis Based on Regional Differences. Finance & Trade Economics (Caimao Jingji), 2, 46−49.Suche in Google Scholar
Wei, Z., & Gustafson, B. (2005). An Analysis of the Unfairness of Medical Expenditure in China. Economic Research Journal (Jingji Yanjiu), 12, 26−34.Suche in Google Scholar
Xu, W., Li, C., & Wu, M. (2011). Empirical Analysis of the Misallocation of Health Resources in China: Based on the Perspective of Medical Competition Model. Business and Management Journal (Jingji Guanli), 8, 156−161.Suche in Google Scholar
Yang, L., & Li, S. (2016). Factors and Improvements in the Misallocation of Urban and Rural Medical Resources. Economic Perspectives (Jingjixue Dongtai), 9, 57−68.Suche in Google Scholar
Zhang, C., Zhong, C., Jiang, T., & Li, X. (2020). Temporal and Spatial Differences and Their Influence Factors in the Regional Coordinated Development of China. Economic Geography (Jingji Dili), 9, 15−26.Suche in Google Scholar
Zhou, K., Zhang, X., Ding, Y., Wang, D., Lu, Z., & Yu, M. (2015). Inequality Trends of Health Workforce in Different Stages of Medical System Reform (1985- 2011) in China. Human Resource for Health, 13(1), 1−8.10.1186/s12960-015-0089-0Suche in Google Scholar
Zhu, H., Zan, X., & Xiang, H. (2014). The Evolution of Financial Compensation System and the Reform of the De-Administration of Public Hospitals. Economic Perspectives (Jingjixue Dongtai), 12, 61−71.Suche in Google Scholar
Zhu, H. (2017). The Root Cause for the Difficulty of Development a Hierarchical Medical System Is in Medical Care, Not in Medical Insurance. China Health Insurance (Zhongguo Yiliao Baoxian), 5, 20−22.Suche in Google Scholar
Zou, W. (2014). Poverty Reduction Effects and Threshold Characteristics of Equalization of Health Services: Analysis Based on Spatial Heterogeneity. Economist (Jingjixuejia), 8, 59−65.Suche in Google Scholar
© 2021 Junhao Wang and Jia Wanwen, published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
Artikel in diesem Heft
- Frontmatter
- Are the Economic Consequences of Climate Change Really “Pro-Poor”?
- Tax Sharing and Fiscal Imbalances: Reconstruction of the VAT Sharing System in China
- Vertical Fiscal Imbalance, Transfer Payment and Local Government Governance
- Does Rising House Price Push Chinese Households to “Leverage Up” More?
- Resources Allocation and Utilization Efficiency in China’s Healthcare Sector
- China’s Government Information Capacity, Medical Resource Allocation and COVID−19 Prevention and Control
Artikel in diesem Heft
- Frontmatter
- Are the Economic Consequences of Climate Change Really “Pro-Poor”?
- Tax Sharing and Fiscal Imbalances: Reconstruction of the VAT Sharing System in China
- Vertical Fiscal Imbalance, Transfer Payment and Local Government Governance
- Does Rising House Price Push Chinese Households to “Leverage Up” More?
- Resources Allocation and Utilization Efficiency in China’s Healthcare Sector
- China’s Government Information Capacity, Medical Resource Allocation and COVID−19 Prevention and Control
