The more slack resources, the better the quality of medical care? The evidence from the public hospitals in Beijing

doi:10.21203/rs.3.rs-1983340/v1

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The more slack resources, the better the quality of medical care? The evidence from the public hospitals in Beijing

https://doi.org/10.21203/rs.3.rs-1983340/v1

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Background: In recent years, the scale of Chinese hospitals and the resources they occupy are in a situation of rapid expansion. Whether these resources can be fully and effectively used and whether they are conducive to the improvement of the quality of medical care remains to be studied.

Methods: The DEA model and the panel regression model were used to analyze the slack resources status, and to study the relationship between slack resources and the quality of medical care.

Results: First, slack in 23 hospitals was on the rise from 2015 to 2018, with significant reduction in 2019. Second, low-risk mortality increased from .014% to .003% when slack resources increased from 0 to about 0.225. However, the low-risk mortality rate increased to .086% when slack resources increased to about .577.

Conclusions: There is an inverted U-shaped curve relationship between slack resources and the quality of medical care in public hospitals in Beijing. That is, a small number of slack resources in the hospital is conducive to the improvement of the quality of medical care, but when it increase to a certain level it will have a negative impact on the quality of medical care.

public hospitals

slack resources

the quality of medical care

After maintaining rapid development for more than 30 years, China’s economy has begun to experience a continuous decline from the historic double-digit rate to the current level of 6% to 7% and entered a new economic development model—the ‘new normal.’ Since the end of 2015, a supply-side structural reform has been launched in various industries in China, which is a major structural transformation toward a new development model focused on achieving better quality growth that is more economically and environmentally sustainable and achieves better social outcomes for the Chinese people. At the same time, with the continuous acceleration of industrialisation, urbanisation, and the ageing of the population, the impact of lifestyle, the ecological environment, and food safety on the citizens’ health has gradually emerged. China’s disease spectrum has changed, and the number of patients with chronic diseases and dying has been increasing. The supply-side reform of China’s health industry is facing even greater challenges, including finding more diverse health care providers who have more freedom to innovate and improve service.

In countries around the world, hospitals are an important part of the supply side of medical services. According to the China Health Statistical Yearbook, in 2018 public hospitals provided 92% of outpatient services for all hospitals in China and 82% of inpatient services[^1]. Public hospitals are the main places where Chinese people receive medical services, and they occupy an important, even dominant, position on the supply side of China’s medical services. In the past 20 years, the number of public hospitals in China has expanded dramatically. After the new medical reform in 2009, the Chinese government issued several documents to control the number of public hospitals, but it did not work. Chinese hospitals are still in a situation of rapid expansion. At the same time, the resources used by hospitals are increasing at an alarming rate. According to the 2020 China Health Statistical Yearbook, between 2014 and 2019 the number of hospital staff across the country increased by 36.54%, the total number of beds increased by 38.41%, and the number and total value of more than ¥10,000 worth of equipment increased by 72.18% and 90.09%, and total assets increased by 120.59% [²]. Comparing the growth of the number of employees in other industries in the same period, in the 19 industries in the 2020 China Statistical Yearbook the growth rate of the number of hospital employees has exceeded that of the emerging ‘information transmission industry (35.39%)’ and is only lower than the country’s important core competitiveness ‘finance industry (45.88%)’ and the new financial ‘leasing and business service industry (46.95%)’[^3]. With such rapid growth, can medical resources be fully and effectively utilised?

In 2020, the COVID-19 pandemic swept the world. The unprepared medical and health systems in many countries were overwhelmed. The lack of medical protection materials, reflecting the importance of reasonable storage and scheduling of resources in the medical system, undoubtedly was a fatal blow. There is an old Chinese saying: ‘Be prepared for danger in times of peace’, which refers to the importance of reserves. As the foundation of organisational development, resources not only provide the organisation’s normal production and operation needs but also increase its defense capabilities and counterattack capabilities in emergencies. Organisational theorists call this kind of slack resources owned by the organisation organisational slack, that is, the part of the resources that the organisation has that exceeds the minimum resources required for a given output level, which also are referred to as ‘unused resources’, including slack inputs, such as slack employees, unused capacity, and unnecessary capital expenditures[^4]. So, should hospitals maintain maximum slack resources?

Organisational slack was first proposed by Cybert and March (1963) [⁵]. It has formed a relatively complete theoretical framework and has been widely used by scholars to explain the use behaviour of various organisations. Regarding the connotation of organisational slack, scholars have two views. The first is agency theory, which focuses on the principal–agent relationship, proposing that an organisation is a complex composed of principals and agents through contracts, and slack resources existence is beneficial only to the agent as a manager. For the entire organisation, slack means waste of resources and inefficiency [4,⁶,⁷], thus detrimental to organisational performance. The second view is the organisational theory, which holds that slack resources play a positive role, and when an organisation is faced with changes in the external environment they can act as a buffer and provide flexibility for the development of organisational strategies[⁸,⁹], and help improve organisational performance. So what role do slack resources play in hospitals?

Beijing, as the capital of China and one of the most developed regions in terms of medical care, needs to receive patients from all over the country, and the medical situation is grim. At the same time, the top medical resources in China are concentrated in Beijing, and it has the largest number of tertiary-care public hospitals. Are there slack resources in these hospitals? What role do slack resources play in hospitals? Do they improve the quality of medical care? These issues are the focus of this article. Diagnostic related groups (DRGs) are an important tool that enables the Beijing government to evaluate the performance of public hospitals in recent years. Therefore, hospitals participating in the evaluation of DRGs were selected as the research objects in this study.

2.1. Data

Since 2015, the Beijing Municipal Health Commission has used DRGs as a tool to conduct annual evaluations of medical services and key specialties in Beijing, so we selected 23 general hospitals participating in the DRGs evaluation for this study, with a time limit of 5 years and a total of 115 observations. The data are from the Beijing Municipal Public Health Information Center and were collected from the 2015–2019 Beijing discharge patients’ home page information database [¹⁰].

2.2 Method

2.2.1 Data Envelopment Analytics Model

In the previous literature, some scholars have used Data Envelopment Analytics (DEA) to measure slack resources in public hospitals. For example, Miller and Adam [¹¹] applied the DEA model to analyse the slack resources of U.S. hospitals and concluded that health care quality can increase when there is high slack. Valdmains et al. [¹²] found that high-quality hospitals tended to have slack on personnel by using DEA to assess 1,377 urban U.S. hospitals in 34 states. Mogha SK [¹³] chose 36 public hospitals in India as samples, and the slack analysis DEA results showed that the object or quantity of medical services can be expanded if all the inefficient hospitals operate at the level of efficient hospitals. Wei H and Wei L(2014) used DEA to measure slack resources of 10 hospitals in Shanghai, China, and found that slack resources declined from 2007 to 2011[^14]. We used DEA in this study to measure slack resources in public hospitals in Beijing.

DEA is one method to evaluate the efficiency maturity of medical institutions, and it is based on the observation of input and output indicators to estimate the frontiers of effective production so as to measure the productivity of medical institutions. To some extent, efficiency and slack are the opposite of each other, as defined by Jensen-Meekling (1976), slack is an X-inefficiency within an organisation, an underutilised resource, and inefficient performance[^15]. Therefore, the output-oriented DEA–BCC model is used to measure the relative efficiency of public hospitals and then to make inferences about slack resources in hospitals. Effective decision-making units with an efficiency score of 1 indicating a relatively high level of efficiency. Correspondingly, the lower the relative efficiency of other decision-making units, the lower the efficiency score. Referring to the idea of Miller and Adam[^16] in this study we set slack as 1 – DEA efficiency score and calculated it using DEAP 2.1 software.

We selected the variables on the basis of experts’ opinions and previous studies. The set of input and output variables we used are similar to those applied in other studies [¹⁷,¹⁸,¹⁹，^20]. The number of outpatient and emergency patients, the number of discharges, and surgical operations were selected as output indicators, and the number of practicing physicians, the number of registered nurses, and the number of beds were selected as input indicators in DEA.

2.2.2 Panel Regression

We used the slack resources (1 – DEA efficiency score) for 23 tertiary public hospitals in 2015–2019 as independent variables and quality of care (low-risk mortality) as dependent variables, and we used hospital size, per capita gross domestic product, proportion of the elderly population, urbanisation rate, density of resident population, the proportion of health expenditure in general public budgets as the control variables. We constructed a panel model to study the relationship between slack resources and quality of medical care. The model is set as follows:

where means the quality of care, which is mortality in the low-risk group, SLACK means hospital slack resources, C means control variable, and it represents data for i hospital at the time t.

If the quadratic model is significant compared with the linear model, and the change in R² and β2 is significant, then there may be a U-shaped or inverted U-shaped relationship. Similarly, if the cubic model is significant compared with the quadratic model and the linear model, and the change in R² and β3 is significant, then there may be an S-type (three-stage) relationship. We used Stata (Version 15) for the construction and analysis of panel models.

3.1 Slack Resource Status

The efficiency of 23 tertiary general public hospitals in 2015–2019 is analysed by DEAP 2.1 software. Based on Mehrtak et al.’s [²¹] method of dividing the DEA’s overall efficiency score into 0 to 0.6, 0.6 to 0.8, and 0.8 to 1 levels, the slack resources was divided from 0 to 0.2, 0.2 to 0.4, and 0.4 to 1, corresponding to low slack, medium slack, and high slack, as shown in Figure 1.

As can be seen from Figure 1, 23 tertiary general public hospitals have slack resources. Among them, slack resources were most significant in 2018, with 13.04% of hospitals in a state of high slack and 65.22% in a state of low slack. In 2015, there were no hospitals with high slack, and 78.26% of hospitals had low slack. Overall, slack in 23 hospitals was on the rise from 2015 to 2018, with significant reduction in 2019.

3.2 Impact of Slack Resources on the Quality of Medical Care

3.2.1 Descriptive Analysis

Descriptive statistics about slack resources, quality of medical care, and related control variables are shown in Table 1. To avoid the problem of heterogeneous variance, we treated the ratio, price, or same variables with large differences with a number conversion. Referring to the ‘ln(a – 1)’ approach to observation data of 0 in Woodridge [²²], we took .00001 for mortality in the low-risk group of 0 before a number conversion. Because the mortality data in the low-risk group ranged from 0 to 1, treating .00001 did not have a deviation effect on the overall results. Slack resources are valued at 0 or 1 and are not ratio variables, so we did not use number conversion. The standard deviation of each variable is below 10, indicating that the interference of outliers on the result is at a low level, so we did not have a tail-end treatment.

Table 1. Descriptive analysis of variables

Variable	Code	Calculation
Dependent variable
Mortality in the low-risk group[²³]	lnlrgm	Number of deaths in the low-risk group / Number of cases in the low-risk group×100%
Independent variable
Slack resources	slack	1- DEA efficiency score
Control variables
Hospital staff or beds[²⁴]	lnsta	The total number of staff.
Hospital staff or beds[²⁴]	lnbed	There is a real number of beds, fixed at the end of the year there are beds.
GDP per capita[²⁵]	lngdppc	the gross regional product / resident population of a region achieved within one year.
The proportion of the elderly population[²⁶]	lnaging	total number of persons / population over 65 years of age *100%
Resident population density[²⁷]	lndensi	the number of people actually living in an area for more than half a year / the area of land in the area *100%
The level of urbanization[19]	lnurban	urban population / Total population (including agriculture and non-agriculture) *100%
Health expenditure as a percentage of general public budget expenditure[19,²⁸]	lnexpen	government spending on health care / expenditures arranged by the financial departments at all levels for the planned allocation and use of centralized general budget revenues *100%

3.2.2 Correlation Analysis and Collinearity Test

In general, a correlation coefficient greater than .8 between two variables indicates a strong correlation. The Pearson correlation coefficients are shown in Table 2. In 2015–2019, there was a strong correlation between the actual number of beds and the total number of hospital employees (.942), indicating that there may be multiple collinear problems in subsequent regression models.

To test the relationship between the independent variables and dependent variables, we included the quadratic and cubic regression models. There is a high correlation between the quadratic and cubic regression models, but this did not hinder the results of the study [²⁹].

To further test the multicollinearity problem, we carried out a variance inflation factor (VIF) test. In the VIF1 column in Table 2, the VIF value of the total number of hospital staff is 10.48. After rejecting the variable, in the VIF2 column the VIF value of each variable is less than 10, and the average VIF value is 2.90, indicating that there are no longer multiple collinearities between the variables. Therefore, we could do the next regression model analysis, which was not expected to affect the model estimates.

Table 2. Correlation analysis of variables

	M±SD	lnlrgm	slack	lngdppc	lnaging	lndensi	lnurban	lnexpen	lnsta	lnbed	Mean VIF
lnlrgm	-4.971±3.275	1.000
slack	0.137±0.144	-0.13	1.000
lngdppc	12.070±0.619	-0.001	-0.109	1.000
lnaging	2.567±0.187	-0.021	-0.169*	0.732***	1.000
lndensi	9.118±1.134	-0.016	-0.017	0.775***	0.441***	1.000
lnurban	4.583±0.068	-0.025	0.051	0.513***	0.151	0.766***	1.000
lnexpen	1.852±0.260	0.169*	-0.147	0.318***	0.118	0.224**	0.187**	1.000
lnsta	7.767±0.530	0.010	-0.500***	0.536***	0.426***	0.499***	0.351***	0.163*	1.000
lnbed	7.016±0.440	0.017	-0.535***	0.469***	0.387***	0.417***	0.314***	0.106	0.942***	1.000
VIF1		-	1.60	5.36	2.67	4.97	2.79	1.22	10.48	9.88	4.87
VIF2		-	1.59	5.36	2.67	4.73	2.76	1.21	-	1.98	2.90

*p<0.05,**p<0.01,***p<0.001

3.2.3 Panel Regression Analysis

We analysed the linear, (inverted) U-type and transposed S-type relationship between slack resources and quality of medical care in public hospitals, and Panel Models 1 to 3 in turn have been built in this section. Based on the robust Hausman test results, Model 1 (p = .0579) selects the random effect panel model, and Model 2 (p = .0351) of Model 3 (p = .0451) selects the fixed-effect panel model.

Through the stray level regression of the robust standard, the model results are shown in Table 3; Models 2 and 3 are significant at the 1% level, indicating a good fit for the model.

The slack resources coefficient in Model 1 is not significant, which shows that there is no linear relationship between slack resources and low-risk mortality. The quadratic coefficient of slack resources in Model 2 is significant at the 10% level, and the coefficient is positive, which indicates that there may be a U-type relationship between slack resources and low-risk mortality. The cubic coefficient in Model 3 is not significant, which indicates that there is no transposed S-type relationship between slack resources and low-risk mortality. Therefore, there is a U-shaped relationship between slack resources and mortality in low-risk groups.

Table 3. Panel regression analysis

	(1)	(2)	(3)
VARIABLES	lnlrgm	lnlrgm	lnlrgm

slack	-3.305	-13.132	-28.158*
	(-1.44)	(-1.60)	(-2.07)
slack 2		28.253*	125.666*
		(1.74)	(1.76)
slack 3			-127.919
			(-1.49)
lngdppc	-0.257	9.771	10.040
	(-0.17)	(1.55)	(1.50)
lnaging	-0.346	28.028*	28.018*
	(-0.11)	(1.95)	(1.90)
lndensi	0.150	75.435**	77.617**
	(0.29)	(2.78)	(2.72)
lnurban	-1.879	-367.666**	-384.366**
	(-0.61)	(-2.55)	(-2.57)
lnexpen	1.765	2.073	2.331
	(1.35)	(0.57)	(0.62)
lnbed	-0.427	-0.227	0.309
	(-0.43)	(-0.06)	(0.08)
Constant	6.440	800.808**	849.989**
	(0.42)	(2.22)	(2.34)
Observations	115	115	115
R-squared	0.000	0.136	0.145
F	11.06	7.37(***)	6.62(***)
Number of hos	23	23	23

Robust z-statistics in parentheses；*** p<0.01, ** p<0.05, * p<0.1

To further explore the relationship between slack resources and quality of medical care, according to the panel regression model just described, the regression fit between slack resources and mortality in low-risk groups is plotted with Stata 15, as shown in Figure 2. By drawing a regression fit diagram and performing number conversion, we noted that as slack resources increased from 0 to about .225, low-risk mortality increased from .014% to .003%. However, the low-risk mortality rate increased to .086% when slack resources increased to about .577.

In this study, we found that the overall slack resources of 23 general tertiary public hospitals in Beijing increased in 2015–2018; however, they decreased in 2019. This may be attributable to a series of top-down reforms implemented by the Chinese government. Since 2015, the government has promoted a tiered diagnosis and treatment system and the construction of medical consortia. The purpose of these reforms is to guide patients with mild and common diseases to seek medical treatment in primary medical institutions and solve the imbalance of medical resources. Bai et al. found that the number of outpatients and hospitalisations in tertiary hospitals has decreased, and the utilisation rate of medical resources has decreased, especially since some assets of non-key disciplines are idle [³⁰]. Su also pointed out that the tertiary hospitals are facing the human resources slack [³¹]. The reform of prospective payment systems based on DRGs has been implemented in tertiary general hospitals in Beijing since 2018. Previous studies have proven that prospective payment systems paved the way for the introduction of market-based approaches to the allocation and management of resources in major health care systems, including the United Kingdom, the United States, Australia, and Canada, and intended to achieve the most productive and effective use of the resources available [³²]. Wang and Wang also provided empirical evidence that the DRG reform in the Xiangtan region of China improved the resource utilisation efficiency of public hospitals. So, in this study, the decline in resource slack in 2019 may stem from the reform of prospective payment systems [³³].

This study also found that there is an inverted U-shaped curve relationship between slack resources and the quality of medical care in public hospitals in Beijing. In the literature, three conclusions on the relationship between slack resources and performance have been formed. More previous studies have proposed a positive relationship between slack resources and quality of medical care; this is in line with the view of organisational theory. For example, Thompson [³⁴] argued that slack resources help managers to cushion the core of technology (the quality of care in hospitals) from the impact of the task environment, thereby optimising the performance of their medical services. Picone et al. [³⁵] found that slack resources are inputs to quality that improve patient survival and some dimensions of functional status among those who survive. Youn K I [³⁶] showed that slack in nonprofit hospital organisations in the United States is positively correlated with service quality. Later, Yamada et al. [³⁷] also indicated that tissue slack is beneficial in relieving pain in children. Other studies have found a positive correlation between resource accessibility and quality of care[³⁸,³⁹,⁴⁰]. However, in the study of the relationship between enterprise organisation slack and performance, scholars have proven the view of agency theory. Bourgeois [⁴¹] pointed out that further resources provide diminishing returns and may, at a certain point, even reduce quality. Cheng and Kesner[6]also argued that as firms allocate more resources to activities that enhance internal efficiency, increases in slack decrease the extent of their environmental response. Xue et al. [⁴²] found that there is an inverse linear correlation between organisational slack and enterprise performance. More recently, scholars have derived an inverted U-shaped relationship [4,⁴³,⁴⁴,⁴⁵], which is consistent with the results of this study. Mohr and Young [⁴⁶] also found a significant curvilinear effect of panel size per clinic capacity for influenza vaccinations and overall quality of care based on primary care clinics in the U.S. Veterans Health Administration. There may be three reasons for the development of these three different views. First, corporations and hospitals have different social functions and different definitions of slack and performance. Second, hospitals in different regions have significant differences in operating mechanisms. Third and finally, there are differences in the type and years of slack that have been studied.

The results of this study have important practical implications. First of all, our findings suggest that slack resources improve the quality of medical care, but only within a certain range. Xiong et al. [⁴⁷] proposed that it is necessary to maintain the slack of medical resources at the macro level from the perspective of fluctuations in medical demand. It is well documented that congestion costs tend to have especially severe consequences in the hospital industry[⁴⁸]. Congestion may not only have severe adverse impacts on important hospital outcomes such as mortality and morbidity but may also result in patients being turned away because of insufficient capacity, which can lead to litigation as well as reputation losses [⁴⁹]. As a result, prior evidence suggests that hospitals maintain a standby or reserve capacity to mitigate congestion costs and reduce the probability of having to turn patients away [⁵⁰]. The “Grade III General Hospital Evaluation Standards (2011 Edition)” require that the tertiary-care public hospitals maintain a bed occupancy rate of no more than 93% [⁵¹]; however, many hospitals in Beijing have exceeded 100% [⁵²], which means that the hospital beds are tight and overloaded [⁵³,⁵⁴]. In recent years, several hospitals have gradually set up branches in non-core areas, which has laid a good foundation for the reform [⁵⁵]. Second, because public hospital funds are allocated to the financial budget, and current medical expenses are increasing rapidly, which may cause managers to blindly expand medical resources for personal interests, and hospital managers should avoid such moral hazard. Finally, excessive resources may lead clinicians to induce patients to engage in unnecessary medical behaviours and affect the quality of medical services and patients’ health.

This study makes significant contributions to the slack theory and the development of public hospitals in China. First, scholars have studied the slack of enterprises in some countries and public hospitals in the United States and India; this study focused on public hospitals in Beijing, China, for the first time. China is in a transition period of rapid economic development, the development of public hospitals in Beijing has special and high-quality requirements, and the management and incentive mechanisms of these hospitals are constantly innovating, so this study has practical significance. Second, this study innovated the slack measurement method, which provides a new idea for future slack research. Finally, the study reveals new findings indicating that the slack and the quality of medical care of public hospitals in Beijing show an inverted U-shaped relationship, which can provide effective support for managers to improve hospital performance.

This study does have some limitations. First, because of limited access to data, the research period was short, and the control variables of panel regression analysis were not rich enough. Second, the selection of hospital performance indicators is single and cannot reflect the overall level. Although the quality of care may top the list, other performance dimensions should also be taken into account. In the future, we will try to analyse it from multiple dimensions.

In this study, we analysed the slack resources of 23 general tertiary-care public hospitals in Beijing from 2015 to 2019 and studied the relationship between the effects of slack resources and the quality of medical care. Through an analysis of the DEA model and panel model, the results show that in 2015–2019, 23 general tertiary-care public hospitals in Beijing had medical resources slack. Meanwhile, there is an inverted U-shaped relationship between this slack and the quality of care; that is, the existence of a small number of slack resources in hospitals is conducive to the improvement of the quality of medical care, but when slack resources increase to a certain extent it will have a negative impact on the quality of medical care. Therefore, public hospitals should increase or decrease medical resources on the basis of a full measurement of medical needs according to actual hospital operation, government reform policy, and future development plans.

1) Ethical Approval and consent to participate:

All methods were carried out in accordance with relevant guidelines and regulations.

All experimental protocols were approved by the Medical Ethics Committee of Capital Medical University(No.Z2020SY123).

Informed consent was obtained from all subjects and their legal guardian(s).

2) Consent to Publication: Not applicable.

3) Data Availability statement: The data that support the findings of this study are available from Beijing Municipal Public Health Information Center but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Beijing Municipal Public Health Information Center.

4) Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

5) Funding: This research was supported by the National Natural Science Foundation of China (Grant Nos. 71974133)

6) Acknowledgment：The authors thanks all research assistants for their works.

7) Author contribution

Junli Zhu: Conceptualization, Methodology, Supervision,Writing-Reviewing and Editing.

Xinrui Song: Investigation, Data curation, Software, Writing-Original draft preparation, Visualization.

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No competing interests reported.

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The more slack resources, the better the quality of medical care? The evidence from the public hospitals in Beijing

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