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Development of Chinese Version of Hospital Autonomy
Questionnaire
Journal:
BMJ Open
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Manuscript ID
Article Type:
Date Submitted by the Author:
Complete List of Authors:
bmjopen-2015-010504
Research
17-Nov-2015
Secondary Subject Heading:
Medical management
HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Health policy <
HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Organisational
development < HEALTH SERVICES ADMINISTRATION & MANAGEMENT
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Keywords:
Health policy
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<b>Primary Subject
Heading</b>:
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Liu, Zifeng; Sun Yat-sen University, Guanghua School of Stomotology,
Hospital of Stomatology; Sun Yat-sen University, Guangdong Provincial
Key Laboratory of Stomatology
Yuan, Lianxiong; Sun Yat-sen University, School of Public Health
Huang, Yixiang; Sun Yat-sen University, School of Public Health;
Guangdong Health Economics Association
Zhang, Lingling; Clemson University, Department of Public Health Sciences
Luo, Futian; Sun Yat-sen University, School of Public Health
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Development of Chinese Version of Hospital Autonomy Questionnaire
Zifeng Liu 1,2, Lianxiong Yuan 2, Yixiang Huang 2,3,*, Lingling Zhang4, *, Futian Luo 2
1
Guanghua School of Stomotology, Hospital of Stomatology, Sun Yat-sen University, Guangdong
Provincial Key Laboratory of Stomatology, 56 Linyuan west Rd, 510055, Guangzhou, China; EMail: sumsliu@hotmail.com
2
School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, 510080, Guangzhou,
China; E-mails: Yixiang Huang, huangyx@mail.sysu.edu.cn; Futian Luo, luoft@mail.sysu.edu.cn
3
Guangdong Health Economics Association, 17 Xianlienan Road, 510060, Guangzhou, China
4
Department of Public Health Sciences, Clemson University, 515 Edwards Hall, Clemson, 296340745, SC, USA; E-Mail: lingliz@clemson.edu
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* Corresponding Authors: huangyx@mail.sysu.edu.cn; lingliz@clemson.edu
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Abstract
Objective: We aimed to develop an questionnaire to quantitatively evaluate the autonomy of public
hospitals in China.
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Method: An extensive literature review was conducted to select possible items into the questionnaire,
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which was then reviewed by experts. Based on the reviewers’ comments from a pilot, the final
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questionnaire, including 6 subscales and 24 questions, was distributed to 404 secondary and tertiary
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hospitals in Guangdong China, and 379 completed questionnaires were collected.
Results: Analysis suggests all internal consistency reliabilities exceeded the minimum reliability
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standard of 0.70 for alpha coefficient. The overall scale coefficient is 0.88, and six subscale
coefficients are 0.92(strategic management), 0.83(budget and expenditure), 0.86 (financing), 0.77
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(financing, medical management), 0.85(human resource), and 0.86(accountability), respectively.
Correlation coefficients between items and their hypothesized subscales were higher than those with
other subscales, except for question 5. The model fit indexes are all acceptable (CMIN/DF=1.62,
GFI=0.92, AGFI=0.90, PGFI=0.74, CFI=0.97, RMSEA=0.04).
Conclusions: This study demonstrates the reliability and validity of a CVHAQ, and provides a
quantitative method to assess the hospital autonomy.
Keywords: Hospital Autonomy; Questionnaire; Development; Reliability; Validity; China
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Strengths and limitations of this study
This is, to the best of our knowledge, the first study to quantitatively investigate the public
hospital autonomy in China.
By surveying more than 400 public hospitals, we developed the first version of a hospital
autonomy questionnaire, which helps to understand the relationship between public hospital and the
government.
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We verified the reliability and validity of our questionnaire, which provided a quantitative method
to assess the hospital autonomy in China.
The survey was conducted only in Guangdong Province, thus the generalization of the results to
other provinces should be with caution.
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This study is a cross-sectional study that could not illustrate the underlying trend of hospital
autonomy. In the future, panel data are needed to be collected.
1. Introduction
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Hospital autonomy is a key issue in health sector reform, which is considered to be an effective
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way to improve the efficiency of public hospitals [1], and this is a particularly important issue in China
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as over 80% hospitals are run by the state. Studies on hospital autonomy evaluation have yielded
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mixed results. This may be due to small sample sizes and quality of data in these studies, which
prevented researchers from conducting more elaborate statistical analyses and reaching robust
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conclusions. Govindaraj and Chawla [2], using the evaluation toolkit by Chawla et al., carried out five
country case studies (Ghana, Kenya, Zimbabwe, India, and Indonesia). However, the results varied by
countries, probably because of the short period of time that elapsed since the granting of autonomy.
Bossert, et al. measured hospital autonomy in Indonesia, and found little evidence on achieving its
objectives [3]. Ssengooda, et al. performed another case study of 8 hospitals in Uganda. Given the
nature of the case study, the authors did not think their results should be used as the sole basis for
national or international level policy development [4]. In addition, different authors carried out eight
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country or area case studies (United Kingdom, New Zealand, Australia, Hong Kong, Malaysia,
Singapore, Indonesia, and Tunisia) based on Harding and Preker’s conceptual framework on hospital
autonomy[5]. Hawkins and Ham summarized these studies and found that autonomization might
improve efficiency in some areas, but not in others [6]. McPake et al. made a positive conclusion by
studying five hospitals in Bogotá, Colombia [7], while Aamir Saeed, et al. studied the autonomization
in teaching hospitals of Punjab, and found that hospital autonomization has not yet yielded hoped-for
benefits [8]. London investigated impacts of hospital autonomization in Viet Nam and also found
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mixed results, mainly due to the small sample size and data constraint [9]. Therefore, there’s an urgent
need to develop a new evaluation tool which can be used to quantify the extent of hospital autonomy.
In China, public hospitals play a critical role in the healthcare system. According to the China
Health Statistics Yearbook (2014), they provide medical services to 89.5% population, and the
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proportion of spending in public hospitals is 95.5% of the total national health expenditure in China.
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Currently, public hospitals are considered as less efficient and a serious financial burden to the
government [10]. In order to address these problems, China has granted autonomy to some public
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hospitals since the early 1980s, including shifting partial decision-making control from the government
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to hospital managers, which allowed public hospitals to earn profit from medical services they
provided. However, after more than 20 years, some research suggested that healthcare reform in China
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has not made significant progress [11]. In 2012, China’s State Council announced a new phase of
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health care reform with the allowance of more private hospitals and better public hospital autonomy
such as the separation of hospital operation from the administrative order to improve efficiency [12].
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The objective of this study was to develop a Chinese version of a hospital autonomy questionnaire
(CVHAQ), which can be used to evaluate the current effort of hospital autonomy, which will help
policy makers better understand the extent of policy implementation and formulate specific policies to
redefine the relationship between health authorities and hospitals.
2. Experimental Section
Theoretical model and questionnaire development
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A systematic approach was used to generate a 32-item questionnaire [13-14]. The development of
items for inclusion in the CVHAQ was carried out in five phases. First, an extensive literature review
was conducted to select possible subscales for the questionnaire. The theoretical basis on hospital
autonomy used the conceptual framework from Harding and Preker [6], which contained five
subscales: 1) decision rights; 2) residual claimant; 3) market exposure; 4) accountability; 5) social
functions. Based on three related measuring tools, the subscale of decision rights was refined and 9
subscales were created to evaluate the extent of Chinese hospital autonomy, which were strategic
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management, budget and expenditure, finance, human resource, day-to-day management, residual
claimant, market exposure, accountability, and social functions. Second, a large number of potential
sub-items were developed under each subscale. Third, a group of experts in law, hospital management,
government, and statistics were invited to discuss relevant items for inclusion or exclusion. Fourth, a
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pilot survey was performed to assess the reliability and validity of the questionnaire. Fifth, based on
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the findings of the pilot, three subscales were removed from the questionnaire. After some necessary
linguistic revisions, the questionnaire, including 6 subscales (24 items) as indicators of Chinese
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hospital autonomy, was finalized and used to build a Chinese hospital autonomy model in this study.
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The questions mainly examine the extent to which the surveyed hospitals were affected by the
government in the year of 2013. Responses were rated on a 5-point Likert scale for hospital autonomy
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(1 = totally constrained, 2 = very constrained, 3 = partly constrained, 4 = little constrained, 5 = no
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constrained). Items were forward scored on a 1-5 point (1=1, 2=2, 3=3, 4=4, 5=5), and subscale scores
were computed as the sum of each item score. That is, the higher scores indicate higher autonomy.
Data collection
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The survey process consisted of two parts. Firstly, an electronic version of the questionnaire was
sent to 404 secondary and tertiary hospitals in Guangdong province with the support of the Health
Bureau of Guangdong province. Secondly, senior management staff of these hospitals were contacted
and asked to organize persons familiar with these issues to fill in the questionnaire. Then the
completed questionnaires were collected by the research team who were responsible for answering
questions and conducting quality control.
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Data analysis
SPSS 19.0 for Windows was used for data analysis. First, Cronbach’s alpha coefficients were
calculated to evaluate the internal consistency reliability of the total questionnaire and each subscale.
Cronbach’s alpha coefficient ≧0.70 was considered as acceptable. Second, multi-trait scaling analysis
(using Pearson correlation analysis) was conducted to determine the item-subscale correlations. If he
analysis shows a stronger correlation (correlation coefficient ≧0.70) between an item and its
hypothesized subscale and that the coefficient is larger than those with other subscales, the
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questionnaire is considered as well defined. In order to test the structure of CVHAQ, an exploratory
factor analysis was conducted and the extracted factors were rotated to simple structure using the
Varimax procedure (orthogonal rotation) to see if the result would be consistent with the theoretical
model. Varimax rotation can enhance the interpretability of the factors with high loadings on a factor.
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Then a confirmatory factor analysis was performed based on structure equation model with AMOS
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19.0 to assess the fitness of our modified theory model. The fit index of χ2/df (<2), GFI (>0.90),
AGFI(>0.90), NNFI(>0.90), CFI(>0.90), RMSEA(<0.05), and SRMR(<0.08) were used to assess the
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fitness of the model. Missing item values from individual surveys were imputed with the mean
value[15].
3. Results and Discussion
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Respondent Groups
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379 questionnaires were collected in this study. The response rate was 93.81%. The percentage of
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missing value for each item of the subscale varied from 0.00% to 3.63%. The mean scores and
standard deviations for the 6 subscales and 24 items were presented in Table 1.
Reliability
Cronbach’s alpha coefficient was presented in Table 1. All internal consistency reliabilities
exceeded the minimum reliability standard of 0.70. Among which the overall scale coefficient was
0.88, and 6 subscale coefficients were 0.92 (strategic management), 0.83(budget and expenditure),
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0.86 (financing), 0.77 (financing, medical management), 0.85 (human resource), and 0.86
(accountability), respectively.
Table 1: CVHAQ Reliability for questionnaire and subscales
Scale/Item
overall scale
Subscale 1:strategic management
Q1:The extent of your hospital to develop a vision and
purpose constrained by government departments
Q2:The extent of your hospital's development goals
constrained by government departments
Q3:The extent of your hospital to develop a short-term
development plan constrained by government departments
Q4:The extent of the hospital to develop a long-term
development plan constrained by government departments?
Subscale 2: budget and expenditure
Q5:The extent of your hospital to set up financial budget
constrained by government departments?
Q6:The extent of your hospital's drug procurement
constrained by government departments?
Q7:The extent of your hospital's equipment procurement
constrained by government departments
Q8:Except drugs and equipments, the extent of your
hospital's other spending constrained by government
departments
Subscale 3: financing
Q9:The extent of your hospital's short-term liabilities
constrained by government departments
Q10:The extent of your hospital's long-term liabilities
constrained by government departments
Q11:The extent of your hospital's Other liabilities (finance
leases, bills, etc.) constrained by government departments
Q12:The degree of your hospital accept donations
intervention by the government departments
Subscale 4: day-to-day management
Q13:The degree of your hospital medical business
management direct intervention by the government
departments
Q14:The degree of your hospital logistics service
management direct intervention by the government
departments
Q15:The degree of your hospital carry convenience
services direct intervention by the government departments
Q16:The degree of your hospital acting style construction
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Cronbach's
Alpha
0.88
73.21 12.97
0.92
11.97 3.57
mean
std
3.05
1.11
2.95
0.95
3.08
0.93
2.87
0.98
9.55
3.33
2.71
1.13
1.94
1.08
2.09
0.97
2.80
0.90
11.25 4.22
2.94
0.83
0.86
1.22
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2.61
1.26
2.63
1.33
3.08
1.23
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12.38 2.96
3.04
0.95
3.52
0.89
3.29
0.96
2.52
1.03
0.77
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direct intervention by the government departments
11.54 3.47
Subscale 5: human resource
Q17:The extent of your hospital recruiting employees
2.67 1.03
constrained by government departments
Q18:The extent of your hospital staff rewards and
3.29 0.98
punishment constrained by government departments
Q19:The extent of your hospital employee's salary
2.7 1.05
constrained by government departments
Q20:The extent of your hospital dismissed employees
2.87 1.12
constrained by government departments
16.32 2.94
Subscale 6: accountability
Q21:Whether the purpose of the inspection which the
4.22 0.88
government departments set for your hospital are clear
Q22:Whether the assessment indicators which the
4.07 0.98
government departments set for your hospital are clear
Q23:Whether the assessment indicators which the
government departments set for your hospital are
3.69 0.90
reasonable
Q24:The degree of recognition of government departments
4.34 0.73
inspection result to your hospital
0.85
0.86
Item-subscale correlations
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Pearson correlation coefficients between subscales and its items were presented in Table 2. The
results showed that items had higher correlation coefficients with its theoretical subscale than other
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subscales.
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Table 2: Item-subscale correlations of CVHAQ
subscale2
0.36
0.36
0.36
0.39
0.79
0.79
0.86
0.82
0.56
0.55
0.53
0.46
0.37
0.32
0.24
subscale3 subscale4 subscale5 subscale6
0.21
0.35
0.22
-0.04
0.22
0.32
0.26
0.06
0.24
0.36
0.22
0.07
0.21
0.34
0.24
0.08
0.56
0.31
0.39
-0.03
0.41
0.32
0.32
-0.02
0.52
0.33
0.46
-0.04
0.55
0.33
0.37
0.01
0.32
0.32
0.02
0.84
0.30
0.29
0.05
0.88
0.28
0.31
0.01
0.87
0.32
0.26
0.01
0.71
0.37
0.31
-0.02
0.82
0.28
0.24
0.08
0.70
0.14
0.20
0.04
0.78
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Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13
Q14
Q15
subscale1
0.85
0.94
0.90
0.92
0.33
0.31
0.35
0.28
0.20
0.16
0.20
0.26
0.32
0.19
0.33
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Q16
Q17
Q18
Q19
Q20
Q21
Q22
Q23
Q24
0.30
0.24
0.30
0.17
0.17
0.03
0.03
0.07
0.07
0.29
0.40
0.42
0.42
0.33
-0.06
-0.05
0.00
-0.02
0.27
0.27
0.31
0.32
0.24
-0.02
-0.03
-0.01
0.00
0.74
0.32
0.35
0.28
0.26
0.03
0.03
0.03
0.01
0.35
0.80
0.81
0.86
0.86
-0.01
-0.06
0.05
-0.03
0.01
0.04
-0.05
-0.04
-0.01
0.86
0.89
0.86
0.75
Factor analysis and model evaluation
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To test the validity of the hypothesis, the exploratory factor analysis with Varimax factor rotation
was conducted, and 6 components were extracted, which accounted for 70.63% of the total variance.
Results were presented in Table 3. The results were consistent with the study hypothesis, except that
Q5 had a little higher factor loading to subscale3 than to subscale2, which we think more suitable for
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scale2 according to its content (budget related). In addition, the fitness of the theory model to hospital
autonomy were presented in Table 4.
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Table 3: Exploratory factor analysis with Varimax factor rotation
subscale2
subscale3 subscale4 subscale5 subscale6
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0.85
0.84
0.84
0.64
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0.82
0.74
0.60
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Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13
Q14
Q15
Q16
Q17
Q18
Q19
Q20
subscale1
0.81
0.92
0.87
0.89
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0.79
0.69
0.78
0.65
0.74
0.74
0.82
0.86
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Q21
Q22
Q23
Q24
0.87
0.88
0.86
0.75
Note. Only factor loadings of 0.40 or greater are reported.
Total variance explained=70.63%.
Table 4: fitness of fit of CVHAQ
χ2/df
GFI
AGFI
NNFI
CFI
RMSEA(95%CI)
SRMR
1.62
0.92
0.90
0.96
0.97
0.04(0.03,0.05)
0.07
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Note. χ2 =Minimum Fit Function Chi-square; df = degree of freedom;
RMSEA = Root Mean Square Error of Approximation; CI = Confidence Interval;
CFI = Comparative Fit Index; NNFI = Non-Normed Fit Index;
AGFI = Adjusted Goodness of Fit Index; SRMR = Standardized Root Mean Square Residual.
Discussion
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In this study, the hospital autonomy survey was modified to adapt to Chinese health policiesand
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the CVHAQ was developed based on Harding and Preker’s conceptual framework on hospital
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autonomy and learning from previous experience of case studies. The reliability and factor structure of
CVHAQ were assessed. Results of this study showed that the CVHAQ was a reliable and valid
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questionnaire to measure hospital autonomy. The confirmatory factor analysis confirmed the
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theoretical model, which reflected the extent of hospital autonomy. The higher scores the hospitals
had, the more autonomy the hospitals have. In the pilot survey, no significant variance was identified
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in the following three subscales: residual claimant, market exposure, and social functions. Under the
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current Chinese health policies, almost all secondary and tertiary hospitals can earn profit from
medical services they provide, so that there is little variance of residual claimant and market exposure
between sample hospitals that could be captured by the questionnaire. Social function is a
complementary reform to ensure that services, which were previously cross-subsidized, continue to be
delivered, and does not belong to the scope of hospital autonomy. The six theoretical subscales
included in the final CVHAQ well reflected the status of hospital autonomization, which explained a
large amount of the variance (>70%). The internal consistency reliability of the questionnaire was
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evaluated by Cronbach’s alpha coefficients. All Cronbach’s alpha coefficients exceeded the
recommended standard of 0.70, indicating adequate reliability of CVHAQ. Results of confirmatory
factor analysis showed that all indicators of fitness were acceptable [16-18]. This study showed that
the subscale2 and subscale3 were correlated( r=0.71), which can be explained by the fact that the
autonomy of the two subscales commonly depends on the economic policy for a hospital which is set
by the government, thus the finance, budget, and expenditure are synergistically affected Subscale5
and subscale2 were also correlated(r=0.55). The underlying reason could be that Chinese government
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spent little on human resources, and the cost was borne by the hospital [19-20].
Characteristics of local hospitals in Guangdong made it very suitable for conducting this large
scale quantitative study. China traditionally managed its public hospitals as administrative units.
However, in the past 30 years, Chinese government has been reforming its health system by reducing
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the health department’s direct control on public hospitals and shifting the day-to-day decision making
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from the government authority to hospital managers in order to reduce the government’s financial
burden and improve hospitals’ efficiency [21]. Guangdong, the frontier of China’s economic reform,
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has about 400 secondary and tertiary public hospitals. They are divided into national hospitals,
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provincial hospitals and municipal or county hospitals. Because of different amount of government
inputs and degree of decentralization, these hospitals vary greatly in autonomy, which created a unique
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opportunity for conducting this study in Guangdong. In addition, this study was supported by the
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Health Bureau of Guangdong Province, which resulted in a higher response rate of local hospitals. This
really helped overcome the issues of small sample size and data constraint existed in previous studies.
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Although many studies have been conducted on hospital autonomization, there is little quantitative
study. Most previous studies were based on case study, and the results were subject to potential
confounding factors, such as hospital size, management style, and geographic location, etc [22].
Empirical literature to date has done little to explore a hospital autonomy questionnaire to quantify the
extent of hospital autonomy. The CVHAQ developed in this helped fill this gap in published literature
and provided a validated tool that can help overcome these shortcomings to draw reliable conclusions.
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In addition, the CVHAQ would lay a strong foundation for quantitatively analyzing the
relationship between hospital autonomy and its effect on healthcare reform. For example, a regression
analysis can be done to explore the relationship between hospital autonomy and efficiency by using the
hospital autonomy scores as independent.
Some limitations must be mentioned regarding our findings. The survey was conducted only in
Guangdong Province, thus the generalization of the results to other provinces should be with caution.
In our future study, we will investigate hospital autonomy in other provinces in China to further
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validate our questionnaire. In addition, we conducted a cross-sectional study that could not illustrate
the underlying trend of hospital autonomy. In future studies, we plan to collect panel data to monitor
hospital autonomy and explore the relationship between hospital autonomy and efficiency in order to
provide decision basis for China’s health care reform.
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4. Conclusions
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This study demonstrated the reliability and validity of a Chinese version of a hospital autonomy
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questionnaire, and provided a quantitative method to assess the hospital autonomy in China.
Acknowledgments
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We thank Qiumao Cai, Lina Yu and Chunxiao Wang at the Health Department of Guangdong
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Province for their assistance. This research was funded by the Guangdong Medical Fundation of China
(C2013016).
Author Contributions
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ZFL, FTL and YXH obtained the funding. ZFL, YXH and LLZ conceived the idea and the
designed the study. ZFL, LXY collected and cleaned the data. ZFL, YXH and LLZ draft the
manuscript. All authors critically revised the manuscript for important intellectual content. All authors
read and approved the final version of the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
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Data sharing statement
Consent was not obtained but the presented data are anonymised and risk of identification is low.
References
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Resource Centre, London, United Kingdom of England,1998; pp.34-40.
2. Govindaraj, R.; Chawla, M. Recent Experiences with Hospital Autonomy in Developing Countries
—What Can We Learn? Harvard School of Public Health, , United States of America, 1996; pp. 1-66.
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Public Health, United States of America, 1997; pp. 1-38.
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greater public hospital autonomy? Comparison of public and PNFP hospitals in Uganda. Public
Administration and Development 2002, 22, 415-428.
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5. Harding, April L; Preker, Alexander S. Understanding Organizational Reforms: The Corporatization
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Public Hospitals.The World Bank, Washington, DC, United States of America, 2003; pp.79-104.
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improving the performance of public hospitals in Bogota? Health Policy and Planning 2003, 18, 182194.
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8. Saleem M.; Saeed A.; Ahmad S.; Ch A.Q. Measuring Extent of Autonomy in Teaching Hospitals of
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Punjab: A Case of Services Hospital, Lahore. European Journal of Business and Management 2013, 5,
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9. London, J. D. The promises and perils of hospital autonomy Reform by decree in Viet Nam. Social
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10. Bank, W. Fixing the public hospital system in China. Washington, DC: World Bank. Available
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online: http://documents.worldbank.org/curated/en/2010/06/13240557/fixing-public-hospital-systemchina-vol-2-2-main-report. (assessed on 8 September 2015).
11. Yanfeng, G. Evaluation and suggestion on China medical health system reform (Summary and
emphasis). China Health Policy 2005, A01, 1-14.
12. Hipgrave D.; Guo S.; Mu Y.; Guo Y.; Yan F.; et al. Chinese-style decentralization and health
system reform. PLoS Medicine 2012, 9, e1001337.
13. KELLEY, K. Good practice in the conduct and reporting of survey research. International Journal
for Quality in Health Care 2003, 15, 261-266.
14. Burns KE, Duffett M, Kho ME, Meade MO, Adhikari NK, Sinuff T, Cook DJ; ACCADEMY
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Group. A guide for the design and conduct of self-administered surveys of clinicians. Canadian
Medical Association Journal. 2008;179(3):245-52
15. Wagstaff, A; Sarah, B. The Impacts of Public Hospital Autonomization: Evidence from a QuasiNatural Experiment: The World Bank, Washington, DC, United States of America, 2012; pp.30-44.
16. Steiger J.H. Structural model evaluation and modification: An interval estimation approach.
Multivariate Behavioral Research 1990, 25, 173-180.
17. Hu L.; Peter B. Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized
Model Misspecification. Psychological Methods 1998, 3, 424-453.
18. McDonald R.P.; Marsh. Choosing a Multivariate Model: Noncentrality and Goodness of Fit.
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Psychological Bulletin 1990, 107, 247-255.
19. Hu S.; Tang S.; Liu Y.; Zhao Y.; Escobar M.L.; et al. Reform of how health care is paid for in
China: challenges and opportunities. The Lancet 2008, 372, 1846-1853.
20. Allen, P.; Cao, Q.; Wang, H. Public hospital autonomy in China in an international context. The
International Journal of Health Planning and Management, 2014, 29, 141-159.
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21. Yip, W.; Hsiao, W. China's health care reform: A tentative assessment. China Economic Review
2009, 20, 613-619.
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22. Saunders J.A.; Morrow-Howell N.; Spitznagel E.; Dore P.; Proctor E.K.; et al. Imputing Missing
Data: A Comparison of Methods for Social Work Researchers. Social Work Research 2006, 30, 19-31.
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STROBE 2007 (v4) checklist of items to be included in reports of observational studies in epidemiology*
Checklist for cohort, case-control, and cross-sectional studies (combined)
Section/Topic
Title and abstract
Item #
1
Recommendation
(a) Indicate the study’s design with a commonly used term in the title or the abstract
Reported on page #
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(b) Provide in the abstract an informative and balanced summary of what was done and what was found
Introduction
Background/rationale
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Explain the scientific background and rationale for the investigation being reported
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State specific objectives, including any pre-specified hypotheses
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Methods
Study design
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Present key elements of study design early in the paper
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Setting
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Participants
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Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data
collection
(a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe
methods of follow-up
Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control
selection. Give the rationale for the choice of cases and controls
Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants
Variables
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Data sources/ measurement
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Bias
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(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed
Case-control study—For matched studies, give matching criteria and the number of controls per case
Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic
criteria, if applicable
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For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe
comparability of assessment methods if there is more than one group
Describe any efforts to address potential sources of bias
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Study size
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Explain how the study size was arrived at
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Quantitative variables
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Statistical methods
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Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen
and why
(a) Describe all statistical methods, including those used to control for confounding
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(b) Describe any methods used to examine subgroups and interactions
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(c) Explain how missing data were addressed
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(d) Cohort study—If applicable, explain how loss to follow-up was addressed
Case-control study—If applicable, explain how matching of cases and controls was addressed
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Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy
5
(e) Describe any sensitivity analyses
Results
Participants
13*
(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,
confirmed eligible, included in the study, completing follow-up, and analysed
(b) Give reasons for non-participation at each stage
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(c) Consider use of a flow diagram
Descriptive data
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(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and
potential confounders
(b) Indicate number of participants with missing data for each variable of interest
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(c) Cohort study—Summarise follow-up time (eg, average and total amount)
Outcome data
15*
Cohort study—Report numbers of outcome events or summary measures over time
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Case-control study—Report numbers in each exposure category, or summary measures of exposure
Main results
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Cross-sectional study—Report numbers of outcome events or summary measures
5
(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95%
confidence interval). Make clear which confounders were adjusted for and why they were included
(b) Report category boundaries when continuous variables were categorized
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(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period
Other analyses
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Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses
Key results
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Limitations
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Interpretation
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Generalisability
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Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction
and magnitude of any potential bias
Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results
from similar studies, and other relevant evidence
Discuss the generalisability (external validity) of the study results
Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on
which the present article is based
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Other information
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*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.
Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE
checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at
http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.
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Development of Chinese Version of Hospital Autonomy
Questionnaire: a Cross-sectional Study in Guangdong
Province
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Journal:
Manuscript ID
Article Type:
Date Submitted by the Author:
Complete List of Authors:
BMJ Open
bmjopen-2015-010504.R1
Research
14-Jan-2016
Secondary Subject Heading:
Medical management
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Keywords:
Health policy
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<b>Primary Subject
Heading</b>:
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Liu, Zifeng; Sun Yat-sen University, Guanghua School of Stomotology,
Hospital of Stomatology; Sun Yat-sen University, Guangdong Provincial
Key Laboratory of Stomatology
Yuan, Lianxiong; Sun Yat-sen University, School of Public Health
Huang, Yixiang; Sun Yat-sen University, School of Public Health;
Guangdong Health Economics Association
Zhang, Lingling; Clemson University, Department of Public Health Sciences
Luo, Futian; Sun Yat-sen University, School of Public Health
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HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Health policy <
HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Organisational
development < HEALTH SERVICES ADMINISTRATION & MANAGEMENT
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Page 1 of 21
Development of Chinese Version of Hospital Autonomy Questionnaire: a
Cross-sectional Study in Guangdong Province
ZifengLiu1,2, LianxiongYuan2, Yixiang Huang2,3,*, Lingling Zhang4,*, FutianLuo2
1
Guanghua School of Stomotology, Hospital of Stomatology, Sun Yat-sen University, Guangdong
Provincial Key Laboratory of Stomatology, 56 Lingyuan west Rd,510055, Guangzhou, China; EMail: sumsliu@hotmail.com
2
School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, 510080, Guangzhou,
China; E-mails: Yixiang Huang, huangyx@mail.sysu.edu.cn; Futian Luo, luoft@mail.sysu.edu.cn
3
Guangdong Health Economics Association, 17 Xianlienan Road, 510060, Guangzhou, China
4
Department of Public Health Sciences, Clemson University, 515 Edwards Hall, Clemson, 296340745, SC, USA; E-Mail: lingliz@clemson.edu
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* Corresponding Authors: huangyx@mail.sysu.edu.cn;lingliz@clemson.edu
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Abstract
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Objective: We aimed to develop a questionnaire to quantitatively evaluate the autonomy of public
hospitals in China.
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Method: An extensive literature review was conducted to select possible items included into the
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questionnaire, which was then reviewed by five experts. After a two-round Delphi method, we
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distributed the questionnaire to 404 secondary and tertiary hospitals in Guangdong Province, China,
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and 379 completed questionnaires were collected. The final questionnaire was then developed on the
basis of the results of both exploratory factor analysis and confirmatory factor analysis.
Results: Analysis suggests all internal consistency reliabilities exceeded the minimum reliability
standard of 0.70 for alpha coefficient. The overall scale coefficient is 0.87, and six subscale
coefficients are 0.92(strategic management), 0.81(budget and expenditure), 0.85 (financing), 0.75
(financing, medical management), 0.86(human resource), and 0.86(accountability), respectively.
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Correlation coefficients between items and their hypothesized subscales were higher than those with
other subscales, except for question 5. The model fit indexes are all acceptable (χ2/DF=1.73,
GFI=0.93, AGFI=0.91, NNFI=0.96, CFI=0.97, RMSEA=0.04, SRMR=0.07).
Conclusions: This study demonstrates the reliability and validity of a Chinese version of hospital
autonomy questionnaire, and provides a quantitative method to assess the hospital autonomy.
Keywords: Hospital Autonomy; Questionnaire; Development; Reliability; Validity; China
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Strengths and limitations of this study
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This is, to the best of our knowledge, the first study to quantitatively investigate the public
hospital autonomy in China.
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By surveying more than 400 public hospitals, we developed the first version of a hospital
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autonomy questionnaire, which helps to understand the relationship between public hospital and the
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government.
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We verified the reliability and validity of our questionnaire, which provided a quantitative method
to assess the hospital autonomy in China.
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The survey was conducted only in Guangdong Province, thus the generalization of the results to
other provinces should be with caution.
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This study is a cross-sectional study that could not illustrate the underlying trend of hospital
autonomy. In the future, panel data needs to be collected.
1. Introduction
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In developing countries, public hospitals are normally characterized by inefficient resource
management low productivity for their rigid hierarchical structures, and ineffective administrative and
financial controls by the government1. Turning public hospitals into autonomous entities is thought to
be able to improve their performance2. In China, because over 80% hospitals are run by the state3,
enlarging public hospital autonomy, which is also considered to be an effective way to improve the
efficiency of public hospitals, is a key issue in health sector reform4, and this is a particularly important
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issue in China. Studies on hospital autonomy evaluation have also yielded mixed results in other
countries. Govindaraj and Chawla5, using the evaluation toolkit by Chawla et al., carried out five
country case studies (Ghana, Kenya, Zimbabwe, India, and Indonesia). However, the results varied by
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country, probably because of the short period of time that elapsed since the granting of autonomy.
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Bossert, et al. measured hospital autonomy in Indonesia and found little evidence on achieving its
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objectives6. Ssengooda, et al. performed another case study of 8 hospitals in Uganda. Given the nature
of the case study, the authors did not think their results should be used as the sole basis for national or
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international level policy development7. In addition, different authors carried out eight country or area
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case studies (United Kingdom, New Zealand, Australia, Hong Kong, Malaysia, Singapore, Indonesia,
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and Tunisia) based on Harding and Preker’s conceptual framework on hospital autonomy8. Hawkins
and Ham summarized these studies and found that autonomization might improve efficiency in some
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areas, but not in others9. McPake et al. made a positive conclusion by studying five hospitals in
Bogotá, Colombia10, while AamirSaeed, et al. studied the autonomization in teaching hospitals of
Punjab, and found that hospital autonomization has not yet yielded hoped-for benefits11. London
investigated impacts of hospital autonomization in Vietnam and also found mixed results, mainly due
to the small sample size and data constraints12. Some researchers attributed the mixed results to small
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sample sizes and quality of data in these studies, which prevented researchers from conducting more
elaborate statistical analyses and reaching robust conclusions2.
In China, public hospitals play a critical role in the healthcare system. According to the China
Health Statistics Yearbook (2015)3, they provide medical services to 89.5% population, and the
proportion of spending in public hospitals is 95.5% of the total national health expenditure in China.
Currently, public hospitals are considered less efficient and a serious financial burden to the
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government13. In order to address these problems, China has granted autonomy to some public
hospitals since the early 1980s, including shifting partial decision-making control from the government
to hospital managers, which has allowed public hospitals to earn profit from medical services they
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have provided. However, after more than 20 years, some research suggests that health care reform in
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China has not made significant progress14. In 2012, China’s State Council announced a new phase of
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health care reform with the allowance of more private hospitals and greater public hospital autonomy
to improve efficiency15. Therefore, studying the impact of hospital autonomy on efficiency is
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important, and developing an effective tool for measuring the public hospital autonomy will be the first
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problem. With the Chinese government’s support and enough sample hospitals in Guangdong province,
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we carried out this study. The objective of this study was to develop a Chinese version of hospital
autonomy questionnaire(CVHAQ), which can be used to evaluate the level of hospital autonomy,
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help policy makers better understand the extent of policy implementation and formulate specific
policies to redefine the relationship between health authorities and hospitals.
2. Experimental Section
Theoretical model and questionnaire development
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Although many researchers have proposed evaluation tools for hospital autonomy, there are three
generic tools, namely Chawla et al. (1996), Over and Watanabe (2003), and Harding and Preker (2003)2. The
Chawla et al. (1996) tool proposes three key areas to analyze the existing level of autonomy: administration,
financing and inputs. The Over and Watanabe (2003) tool considers five elements of hospital structure: residual
claimant status, decision right, degree of market exposure, availability of accountability mechanisms and extent
of unfunded mandates. The Harding and Preker (2003) tool proposes the five dimensions to analyze the extent
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of hospital autonomy: decision right, market exposure, residual claimant, accountability and social functions.
We modified the three existing tools to develop the Chinese version of hospital autonomy
questionnaire.
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Figure 1: Flowchart of Questionnaire Development16-
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A systematic approach was used to generate the final questionnaire16-17. The development of items
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for inclusion in the CVHAQ was carried out in five phases. First, an extensive literature review was
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conducted to select possible subscales for the questionnaire. The theoretical basis on hospital
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autonomy was the conceptual framework from Harding and Preker, which contained five subscales: 1)
decision rights, 2) residual claimant, 3) market exposure, 4) accountability, 5) social functions8. At the
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same time, we also learned from the other two related measuring tools, and 9 subscales were created to
evaluate the extent of Chinese hospital autonomy: strategic management, budget and expenditure,
finance, human resource, day-to-day management, residual claimant, market exposure, accountability,
and social functions. Second, after in-depth interviews and focus-group sessions,40 potential sub-items
were developed. Third, ten carefully selected experts, including two lawyers, four hospital
management experts, two government officers, and two professors versed in statistics were invited to
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discuss relevant items for inclusion or exclusion. We used the Delphi method to evaluate the items.
The experts gave their scores in term of linguistic expression, importance, clarity, and correlation for
each item for two rounds, and then we deleted the items whose importance score was less than 7.5. The
questionnaire had then taken its initial shape with 32 items. Fourth, a pilot testing with 26 selected
hospitals was conducted to judge the appropriateness of each included question. Fifth, in a formal
survey, six undergraduate students were selected as questionnaire interviewers, and two researchers in
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our focus-group were designated as quality controllers. After investigation, we used the method of
exploratory factor analysis to screen the items, and finally we deleted 10 items and extracted 6 factors
(All loading are acceptable). In order to enhance the interpretability of the factors with high loadings
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on a factor, we rotated the extracted factors to simple structure using the Varimax procedure
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(orthogonal rotation) to see if the result was consistent with our theoretical model. Furthermore, we
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evaluated the reliability of the questionnaire and the Cronbach’s alpha coefficients in all subscales
higher than 0.70. After some necessary linguistic revisions, the questionnaire, including 6 subscale
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(22items) indicators of Chinese hospital autonomy, was finalized and used to build a Chinese hospital
autonomy model.
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Responses were rated on a 5-point Likert scale for hospital autonomy (1 = totally constrained, 2 =
very constrained, 3 = partly constrained, 4 =hardly constrained, 5 = not constrained). Items were
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forward scored on a 1-5 point (1=1, 2=2, 3=3, 4=4, 5=5), and subscale scores were computed as the
sum of each item score. That is, the higher scores indicate higher autonomy.
Figure 2: Steps of questionnaire development8
Quality control measures
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Item generation
The items in this questionnaire were generated through literature reviews, in-depth interviews,
and focus-group sessions. Firstly, we reviewed PubMed, and CNKI to collect the pertinent literature.
Secondly, we set up a focus group to analyze the pertinent literature and preliminarily determined the
subscales of our questionnaire. Finally, the members of the focus group reviewed the potential
respondents or experts in-depth. When item generation continues until no new items emerge, we
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established an item pool with 40 items.
Then, the Delphi method was used for two rounds.
Pilot testing
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The questionnaire was tested for how well respondents comprehend and correctly answer crucial
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questions, or to confirm that the questionnaire is user-friendly for the respondent, and ascertain how
long it will take to complete.
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Formal survey
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All of the questionnaire interviewers and quality controllers were systemically trained by the
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project investigator(PI) before the investigation. During the investigation, the questionnaire
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interviewers were responsible for connecting respondents who were familiar with questions, and
reported to the PI regularly. The quality controllers are responsible for checking the integrity and
accuracy of the finished questionnaire.
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Data entry
Double blind input, consistency check, and logical check was used to control the quality of data
entry.
Data collection
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This survey was conducted in 2013, and the data collection process consisted of two parts. First,
an electronic version of the questionnaire was sent to 404 secondary and tertiary hospitals in
Guangdong province with the support of the Health Bureau of Guangdong province. Second, senior
management staff of these hospitals were contacted and asked to organize persons familiar with these
issues to fill in the questionnaire. Then the completed questionnaires were collected by the research
team.
Data analysis
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SPSS 19.0 for Windows was used for data analysis. First, Cronbach’s alpha coefficients were
calculated to evaluate the internal consistency reliability of the total questionnaire and each subscale.
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Cronbach’s alpha coefficient ≧0.70 was considered as acceptable. Second, multi-trait scaling analysis
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(using Pearson correlation analysis) was conducted to determine the item-subscale correlations. If the
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analysis shows a stronger correlation (correlation coefficient ≧0.70) between an item and its
hypothesized subscale and that the coefficient is larger than those with other subscales, the
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questionnaire is considered as well defined. In order to test the validity of CVHAQ, an exploratory
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factor analysis was conducted and the extracted factors were rotated to simple structure using the
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Varimax procedure (orthogonal rotation) to see if the result would be consistent with the theoretical
model. Varimax rotation can enhance the interpretability of the factors with high loadings on a factor.
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The value of average variance extracted (AVE) and that of construct reliability (C.R.) were calculated
to test convergent validity and discriminant validity. Then a confirmatory factor analysis was
performed with AMOS 19.0 to assess the fitness of our modified theory model. The fit index ofχ2/df
(<2), GFI (>0.90), AGFI (>0.90), NNFI (>0.90), CFI (>0.90), RMSEA (<0.05), and SRMR (<0.08)
were used to assess the fitness of the model. Missing item values from individual surveys were
imputed with the mean value18.
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3. Results and Discussion
Respondent Groups
A total of 379 questionnaires were collected in this study. The characteristics of the participating
hospitals are listed in Table 1. The response rate was 93.81%. The percentage of missing value for
each item of the subscale varied from 0.00% to 3.63%.
Table 1 Characteristics of the participating hospitals
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variables
classification
grade
specialized hospital
general hospital
secondary
tertiary
Pearl River Delta (PRD)
other areas in Guangdong province
location
Exploratory factor analysis
N
%
110
269
293
86
164
215
29.0
71.0
77.3
22.7
43.3
56.7
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To test the validity of the hypothesis, the exploratory factor analysis with Varimax factor rotation
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was conducted, and 6 components were extracted, which accounted for 73.05% of the total variance.
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Results are presented in Table 2.
Table 2: Exploratory factor analysis with Varimax factor rotation
subscale1
0.82
Q2
0.92
Q3
0.87
Q4
0.89
subscale3 subscale4 subscale5 subscale6
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Q5
0.83
Q6
0.73
Q7
0.59
Q8
0.84
Q9
0.84
Q10
0.85
Q11
0.65
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Q12
0.77
Q13
0.79
Q14
0.76
Q15
0.77
Q16
0.76
Q17
0.83
Q18
0.85
Q19
0.86
Q20
0.88
Q21
0.85
Q22
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0.76
Note. Only factor loadings of 0.50 or greater are reported.
Total variance explained=73.05%.
Reliability
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The mean scores, standard deviations, and Cronbach’s alpha coefficient were presented in Table
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3. All internal consistency reliabilities exceeded the minimum reliability standard of 0.70. Among
which the overall scale coefficient was 0.87, and 6 subscale coefficients were 0.92 (strategic
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management), 0.81(budget and expenditure), 0.85(financing), 0.75(financing, medical management),
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0.86(human resource), and 0.86 (accountability), respectively.
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Table 3: CVHAQ Reliability for questionnaire and subscales
Scale/Item
Subscale1:strategic management
Q1:The extent of your hospital to develop a vision and
purpose constrained by government departments
Q2:The extent of your hospital's development goals
constrained by government departments
Q3:The extent of your hospital to develop a short-term
development plan constrained by government departments
Q4:The extent of the hospital to develop a long-term
development plan constrained by government departments?
Subscale2: budget and expenditure
Cronbach's
Alpha
67.47 11.68
0.87
mean
std
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11.92
3.53
3.04
1.10
2.94
0.94
3.07
0.92
2.87
0.96
6,80
2,52
0.92
0.81
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Q5:The extent of your hospital's drug procurement
constrained by government departments?
Q6:The extent of your hospital's equipment procurement
constrained by government departments
1.94
1.08
2.09
0.98
2.78
0.90
11.16
4.20
2.92
1.23
2.59
1.26
2.60
1.32
3.05
1.23
9.83
2.28
3.03
0.95
3.51
0.89
3.28
0.96
11.46
3.54
Q7:Except drugs and equipment, the extent of your
hospital's other spending constrained by government
departments
Subscale3: financing
Q8:The extent of your hospital's short-term liabilities
constrained by government departments
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Q9:The extent of your hospital's long-term liabilities
constrained by government departments
Q10:The extent of your hospital's Other liabilities (finance
leases, bills, etc.) constrained by government departments
Q11:The degree of your hospital accept donations
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intervention by the government departments
Subscale4: day-to-day management
0.85
0.75
Q12:The degree of your hospital medical business
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management direct intervention by the government
departments
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Q13:The degree of your hospital logistics service
management direct intervention by the government
departments
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Q14:The degree of your hospital carry convenience
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services direct intervention by the government departments
Subscale5: human resource
Q15:The extent of your hospital recruiting employees
constrained by government departments
punishment constrained by government departments
Q17:The extent of your hospital employee's salary
constrained by government departments
Q18:The extent of your hospital dismissed employees
constrained by government departments
Subscale6: accountability
Q19:Whether the purpose of the inspection which the
government departments set for your hospital are clear
Q20:Whether the assessment indicators which the
government departments set for your hospital are clear
2.66
1.05
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Q16:The extent of your hospital staff rewards and
0.86
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3.26
0.99
2.69
1.07
2.85
1.12
16.30 2.95
4.22
0.89
4.06
0.99
0.86
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Q21:Whether the assessment indicators which the
government departments set for your hospital are
3.68
0.89
4.34
0.73
reasonable
Q22:The degree of recognition of government departments
inspection result to your hospital
Item-subscale correlations
Pearson correlation coefficients between subscales and its items are presented in Table 4. The
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results showed that items had higher correlation coefficients as compared to its theoretical subscale
than the other subscales.
Table 4: Item-subscale correlations of CVHAQ
subscale1
subscale2
Q1
0.86
0.33
0.22
0.32
0.22
-0.04
Q2
0.94
0.33
0.23
0.32
0.27
0.07
Q3
0.90
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0.33
0.25
0.37
0.23
0.07
Q4
0.91
0.36
0.21
0.32
0.25
0.07
Q5
0.33
0.86
0.40
0.29
0.31
-0.04
Q6
0.35
0.89
0.55
0.30
0.47
-0.05
Q7
0.28
0.81
0.58
0.34
0.37
0.01
Q8
0.21
0.52
0.87
0.29
0.35
-0.04
Q9
0.18
0.54
0.88
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0.29
0.31
-0.00
Q10
0.19
0.48
0.87
0.24
0.30
-0.06
Q11
0.26
0.43
0.72
0.28
0.27
-0.00
Q12
0.34
0.36
0.37
0.85
0.32
Q13
0.21
0.29
0.28
0.80
Q14
0.34
0.23
0.15
0.80
Q15
0.24
0.38
0.28
Q16
0.31
0.40
Q17
0.19
Q18
subscale3 subscale4 subscale5 subscale6
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-0.03
0.25
0.06
0.22
0.02
0.26
0.81
0.04
0.33
0.34
0.82
-0.05
0.41
0.34
0.27
0.86
-0.01
0.18
0.33
0.28
0.21
0.86
0.01
Q19
0.01
-0.05
-0.03
0.02
0.01
0.86
Q20
0.01
-0.04
-0.06
0.01
-0.05
0.89
Q21
0.07
0.01
0.01
0.02
0.04
0.85
Q22
0.06
-0.04
-0.01
0.01
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Convergent validity and discriminant validity
If the value of AVE is higher than 0.5 and the value of C.R. is higher than 0.70, we consider the
convergent validity acceptable. In order to test discriminant validity, we calculated the square root of
every AVE value belonging to each latent subscale. The outcomes (in Table5) show that the square
roots of the AVE of each subscale are larger than the correlation of the specific subscale with any of
the other subscales.
Table 5 Convergent and discriminant validity for questionnaire and subscales
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Variable
AVE
strategic
C.R.
budget and
expenditure
financing
day-to-day
management
resource
management
resource
0.76
0.93
0.87
0.58
0.81
0.38
0.76
0.61
0.86
0.25
0.59
0.78
0.51
0.75
0.37
0.33
0.60
0.85
0.27
0.61
0.86
0.04
0.36
accountability
0.71
0.45
0.37
0.32
0.77
-0.04
-0.03
0.02
0.00
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accountability
human
ev
human
expenditure
rr
management
day-to-day
financing
management
strategic
budget and
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0.78
Note: on the diagonal we insert the square roots of every AVE value in order to compare it with the other correlation coefficients.
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Confirmatory factor analysis
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We use AMOS19.0 to conduct confirmatory factor analysis, and 7 indicators were calculated to
test the fitness of our theory model. The results were presented in Table 6.
Table 6: fitness of fit of CVHAQ
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χ2/df
GFI
AGFI
NNFI
CFI
RMSEA(95%CI)
SRMR
1.73
0.93
0.91
0.96
0.97
0.04(0.03,0.05)
0.07
Note. χ2 =Minimum Fit Function Chi-square; df= degree of freedom;
RMSEA = Root Mean Square Error of Approximation; CI = Confidence Interval;
CFI = Comparative Fit Index; NNFI = Non-Normed Fit Index;
AGFI = Adjusted Goodness of Fit Index; SRMR = Standardized Root Mean Square Residual.
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Discussion
In this study, the hospital autonomy survey was modified to adapt to Chinese health policies and
the CVHAQ was developed based on Harding and Preker’s conceptual framework on hospital
autonomy and also learning from the Chawla et al. (1996) tool and Over and Watanabe(2003) tool.
The reliability and factor structure of CVHAQ were assessed. Results of this study showed that the
CVHAQ was a reliable and valid questionnaire to measure hospital autonomy. The confirmatory factor
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analysis confirmed the theoretical model, which reflected the extent of hospital autonomy. The higher
scores the hospitals had, the more autonomy they had. According to Chinese Health Statistics Report
(2015)3, and government fiscal budgets provide about 7 percent of total revenue for public hospitals.
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Almost all secondary and tertiary hospitals have to earn profit from medical services they provide, so
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that there is little variance of residual claimant and market exposure between sample hospitals that
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could be captured by the questionnaire. Social function is a complementary reform to ensure that
services, which were previously cross-subsidized, continue to be delivered, and does not belong to the
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scope of hospital autonomy. The six theoretical subscales included in the final CVHAQ well reflected
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the status of hospital autonomization. Results of confirmatory factor analysis showed that all indicators
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of fitness were acceptable19-21. This study showed that the subscale2 and subscale3 were correlated(
r=0.59), which can be explained by the fact that the autonomy of the two subscales commonly depends
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on the economic policy for a hospital which is set by the government, thus the finance, budget, and
expenditure are synergistically affected Subscale5 and subscale2 were also correlated(r=0.45). The
underlying reason could be that Chinese government spent little on human resources and the cost was
borne by the hospital22-23.
Characteristics of local hospitals in Guangdong made it very suitable for conducting this large
scale quantitative study. China traditionally managed its public hospitals as administrative units.
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However, in the past 30 years, Chinese government has been reforming its health system by reducing
the health department’s direct control on public hospitals and shifting the day-to-day decision making
from the government authority to hospital managers in order to reduce the government’s financial
burden and improve hospitals’ efficiency24. Guangdong, the frontier of China’s economic reform, has
more than 400 secondary and tertiary public hospitals. They are divided into national hospitals,
provincial hospitals and municipal or county hospitals. Because of different amount of government
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inputs and degree of decentralization, these hospitals vary greatly in autonomy, which created a unique
opportunity for conducting this study in Guangdong. In addition, this study was supported by the
Health Bureau of Guangdong Province, which resulted in a higher response rate of local hospitals. This
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really helped overcome the issues of small sample size and data constraint existing in previous studies.
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Although many studies have been conducted on hospital autonomization, a small amount of them
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are quantitative. Most previous studies were based on case study, and the results were subject to
potential confounding factors, such as hospital size, management style, and geographic location, etc25.
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Empirical literature to date has done little to explore a hospital autonomy questionnaire to quantify the
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extent of hospital autonomy. The CVHAQ developed in this helped fill this gap in published literature
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and provided a validated tool that can help overcome these shortcomings to draw reliable conclusions.
In addition, the CVHAQ would lay a strong foundation for quantitatively analyzing the
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relationship between hospital autonomy and its effect on healthcare reform. For example, a regression
analysis can be done to explore the relationship between hospital autonomy and efficiency by using the
hospital autonomy scores as independent.
Some limitations must be mentioned regarding our findings. The survey was conducted only in
Guangdong Province, thus the generalization of the results to other provinces should be with caution.
In our future study, we will investigate hospital autonomy in other provinces in China to further
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validate our questionnaire. In addition, we conducted a cross-sectional study that could not illustrate
the underlying trend of hospital autonomy. In future studies, we plan to collect panel data to monitor
hospital autonomy and explore the relationship between hospital autonomy and efficiency in order to
provide decision basis for China’s health care reform.
4. Conclusions
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This study demonstrated the reliability and validity of a Chinese version of a hospital autonomy
questionnaire, and provided a quantitative method to assess the hospital autonomy in China.
Acknowledgments
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We thank Qiumao Cai, Lina Yu and Chunxiao Wang at the Health Department of Guangdong
Province for their assistance. This research was funded by the Guangdong Medical Fund of China
(C2013016).
Author Contributions
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ZFL, FTL and YXH obtained the funding. ZFL, YXH and LLZ conceived the idea and the
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designed the study. ZFL, LXY collected and cleaned the data. ZFL, YXH and LLZ drafted the
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manuscript. All authors critically revised the manuscript for important intellectual content. All authors
read and approved the final version of the manuscript.
Conflicts of Interest
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The authors declare no conflict of interest.
Ethics approval
Medical Ethics Committee, Guanghua School of Stomotology, Hospital of Stomatology, Sun Yatsen University.
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Data sharing
No additional data available.
References
1. Abdullah MT, Shaw J. A review of the experience of hospital autonomy in Pakistan. International
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Journal of Health Planning and Management. 2007;22(1):45-62.
2. Castaño R, Bitran R, Giedion. U, Monitoring and Evaluating Hospital Autonomization and Its
Effects on Priority Health Services, Abt Associates Inc: 2004. http://pdf.usaid.gov/pdf_docs/PNADA
591.pdf.
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3. National Health and Family Planning Commission of the People's Republic of China. National
Health and Family Planning Statistical Year Book. 2015:104-136.
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Resource Centre, London, United Kingdom of England,1998; pp.34-40.
5.Govindaraj R, Chawla M. Recent Experiences with Hospital Autonomy in Developing Countries—
What Can We Learn?
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Harvard School of Public Health, , United States of America, 1996; pp. 1-66.
6. Bossert T, Kosen S, Harsono B, Gani A. Hospital Autonomy in Indonesia, Harvard School of Public
Health, United States of America, 1997; pp. 1-38.
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7. Ssengooba F, Atuyambe L, McPake B, Hanson K, Okuonzi S. What could be achieved with greater
public hospital autonomy? Comparison of public and PNFP hospitals in Uganda. Public
Administration and Development. 2002;22(5):415-428.
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8. Harding AL, Preker AS. Understanding Organizational Reforms: The Corporatization of Public
Hospitals, The World Bank, Washington, DC, United States of America, 2000; pp. 1-29.
9. Harding AL, Preker AS. Innovations in Health Service Delivery: the Corporatization of Public
Hospitals. The World Bank, Washington, DC, United States of America, 2003; pp.79-104.
10. McPake B, Yepes FJ, Lake S, Sanchez LH. Is the Colombian health system reform improving the
performance of public hospitals in Bogota? Health Policy and Planning. 2003;18(2):182-194.
11. Saleem M, Saeed A, Ahmad S, Ch AQ. Measuring Extent of Autonomy in Teaching Hospitals of
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Punjab: A Case of Services Hospital, Lahore. European Journal of Business and Management.
2013;5(8):83-91.
12. London JD. The promises and perils of hospital autonomy Reform by decree in Viet Nam.Social
Science & Medicine 2013, 96, 232-240.
13. World Bank. Fixing the public hospital system in China. Washington, DC: World Bank. Available
online: http://documents.worldbank.org/curated/en/2010/06/13240557/fixing-public-hospital-systemchina-vol-2-2-main-report. (assessed on 8 September 2015).
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emphasis).China Health Policy 2005, A01, 1-14.
15. Hipgrave D, Guo S, Mu Y, Guo Y, et al. Chinese-style decentralization and health system reform.
PLoS Med. 2012;9(11): e1001337.
16. KELLEY K, CLARK B, BROWN V, SITZIA J. Good practice in the conduct and reporting of
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survey research. International Journal for Quality in Health Care. 2003;15(3):261-266.
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17. Burns KE, Duffett M, Kho ME, Meade MO, et al. A guide for the design and conduct of selfadministered surveys of clinicians. CMAJ. 2008;179(3):245-252.
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18. Wagstaff A, Sarah B. The Impacts of Public Hospital Autonomization: Evidence from a QuasiNatural Experiment: The World Bank, Washington, DC, United States of America, 2012; pp.30-44.
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19.Steiger JH. Structural model evaluation and modification: An interval estimation approach.
Multivariate Behavioral Research 1990;25(2):173-180.
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20. Hu L, Peter BM. Fit Indices in Covariance Structure Modeling: Sensitivity to Under Parameterized
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Model Misspecification. Psychological Methods 1998, 3(4): 424-453.
21. McDonald RP, Marsh HW. Choosing a Multivariate Model: Noncentrality and Goodness of Fit.
Psychological Bulletin 1990; 107(2): 247-255.
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22. Hu S, Tang S, Liu Y, Zhao Y, Escobar ML, et al. Reform of how health care is paid for in China:
challenges and opportunities. The Lancet 2008; 372: 1846-1853.
23. Allen P, Cao Q, Wang H. Public hospital autonomy in China in an international context. The
International Journal of Health Planning and Management, 2014; 29(2): 141-159.
24. Yip W, Hsiao W. China's health care reform: A tentative assessment. China Economic Review
2009;20(4): 613-619.
25. Saunders JA, Morrow-Howell N, Spitznagel E. Dore P, Proctor EK, et al. Imputing Missing Data:
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A Comparison of Methods for Social Work Researchers. Social Work Research 2006; 30(1):19-31.
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Development of the Chinese version of the Hospital
Autonomy Questionnaire: a cross-sectional study in
Guangdong Province
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Journal:
Manuscript ID
Article Type:
Date Submitted by the Author:
Complete List of Authors:
BMJ Open
bmjopen-2015-010504.R2
Research
02-Feb-2016
Secondary Subject Heading:
Medical management
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Keywords:
Health policy
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<b>Primary Subject
Heading</b>:
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Liu, Zifeng; Sun Yat-sen University, Guanghua School of Stomotology,
Hospital of Stomatology; Sun Yat-sen University, Guangdong Provincial
Key Laboratory of Stomatology
Yuan, Lianxiong; Sun Yat-sen University, School of Public Health
Huang, Yixiang; Sun Yat-sen University, School of Public Health;
Guangdong Health Economics Association
Zhang, Lingling; Clemson University, Department of Public Health Sciences
Luo, Futian; Sun Yat-sen University, School of Public Health
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HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Health policy <
HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Organisational
development < HEALTH SERVICES ADMINISTRATION & MANAGEMENT
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Page 1 of 21
Development of the Chinese version of the Hospital Autonomy
Questionnaire: a cross-sectional study in Guangdong Province
Zifeng Liu1,2, Lianxiong Yuan2, Yixiang Huang2,3,*, Lingling Zhang4,*, Futian Luo2
1
Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangdong
Provincial Key Laboratory of Stomatology, 56 Lingyuan West Rd, 510055, Guangzhou, China
2
School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, 510080, Guangzhou,
China
3
Guangdong Health Economics Association, 17 Xianlienan Road, 510060, Guangzhou, China
4
Department of Public Health Sciences, Clemson University, 515 Edwards Hall, Clemson, SC 296340745, USA
*Correspondence to
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Dr. Yixiang Huang: huangyx@mail.sysu.edu.cn
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Dr. Lingling Zhang: lingliz@clemson.edu
ABSTRACT
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Objective: We aimed to develop a questionnaire for quantitative evaluation of the autonomy of public
hospitals in China.
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Method: An extensive literature review was conducted to select possible items for inclusion in the
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questionnaire, which was then reviewed by five experts. After a two-round Delphi method, we
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distributed the questionnaire to 404 secondary and tertiary hospitals in Guangdong Province, China,
and 379 completed questionnaires were collected. The final questionnaire was then developed on the
basis of the results of both exploratory factor analysis and confirmatory factor analysis.
Results: Analysis suggested that all internal consistency reliabilities exceeded the minimum reliability
standard of 0.70 for the alpha coefficient. The overall scale coefficient was 0.87, and six subscale
coefficients were 0.92 (strategic management), 0.81 (budget and expenditure), 0.85 (financing), 0.75
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(financing, medical management), 0.86 (human resources) and 0.86 (accountability). Correlation
coefficients between and among items and their hypothesised subscales were higher than those with
other subscales. The value of average variance extracted (AVE) was higher than 0.5, the value of
construct reliability (C.R.) was higher than 0.7, and the square roots of the AVE of each subscale were
larger than the correlation of the specific subscale with the other subscales, supporting the convergent
and discriminant validity of the Chinese version of the Hospital Autonomy Questionnaire (CVHAQ).
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The model fit indices were all acceptable: χ2/degrees of freedom (χ2/df) =1.73, Goodness of Fit Index
(GFI) =0.93, Adjusted Goodness of Fit Index (AGFI) =0.91, Non-Normed Fit Index (NNFI) =0.96,
Comparative Fit Index (CFI) =0.97, Root Mean Square Error of Approximation (RMSEA) =0.04,
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Standardised Root Mean Square Residual (SRMR) =0.07.
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Conclusions: This study demonstrated the reliability and validity of a Chinese version of the Hospital
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Autonomy Questionnaire and provides a quantitative method for the assessment of hospital autonomy.
Keywords: Hospital Autonomy; Questionnaire; Development; Reliability; Validity; China
Strengths and limitations of this study
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▪ To the best of our knowledge, this is the first study to carry out a quantitative investigation of public
hospital autonomy in China.
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▪ By surveying more than 400 public hospitals, we developed the first version of a Hospital Autonomy
Questionnaire, designed to improve our understanding of the relationship between public hospitals and
the government.
▪ We verified the reliability and validity of our questionnaire, which provides a quantitative method for
the assessment of hospital autonomy in China.
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▪ The survey was conducted only in Guangdong Province; thus, the generalisation of the results to
other provinces should be made with caution.
▪ This was a cross-sectional study that could not illustrate the underlying trend of hospital autonomy.
In the future, panel data should be collected.
INTRODUCTION
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In developing countries, public hospitals are normally characterised by inefficient resource
management, by low productivity due to their rigid hierarchical structures and by ineffective
governmental administrative and financial controls.1 Turning public hospitals into autonomous entities
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is thought to be able to improve their performance.2 In China, because over 80% of hospitals are run by the
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state,3 increasing public hospital autonomy, which is also considered to be an effective way to improve
the efficiency of public hospitals, is a key issue in health sector reform4 and is a particularly important
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issue in China. Studies on the evaluation of hospital autonomy in other countries have yielded mixed
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results. Govindaraj and Chawla,5 using the evaluation toolkit by Chawla et al, carried out five country
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case studies (Ghana, Kenya, Zimbabwe, India and Indonesia). However, the results varied by country,
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probably because of the short period of time that elapsed since the granting of autonomy in each
country. Bossert et al measured hospital autonomy in Indonesia and found little evidence that its
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objectives were achieved.6 Sengooba et al performed another case study of eight hospitals in Uganda.
Given the nature of the case study, the authors did not believe that their results should be used as the
sole basis for national- or international-level policy development.7 In addition, different authors carried
out eight country or area case studies (United Kingdom, New Zealand, Australia, Hong Kong,
Malaysia, Singapore, Indonesia and Tunisia) based on Harding and Preker’s conceptual framework on
hospital autonomy.8 Hawkins and Ham summarised these studies and found that autonomisation might
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improve efficiency in some areas but not in others.9 McPake et al reached a positive conclusion by
studying five hospitals in Bogotá, Colombia,10 whilst Amir Saeed et al studied the autonomisation of
teaching hospitals in Punjab and found that it has not yet yielded the hoped-for benefits.11 London
investigated the impact of hospital autonomisation in Vietnam and also found mixed results, mainly
due to the small sample size and data constraints.12 Some researchers attributed the mixed results to
small sample sizes and quality of data in these studies, which prevented researchers from conducting
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more elaborate statistical analyses and reaching robust conclusions.2
In China, public hospitals play a critical role in the healthcare system. According to the China
Health Statistics Yearbook (2015),3 they provide medical services to 89.5% of the population, and the
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proportion of spending in public hospitals is 95.5% of the total national health expenditure in China.
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Currently, public hospitals are considered less efficient and a serious financial burden to the
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government.13 To address these problems, China began granting autonomy to some public hospitals in
the early 1980s, including shifting partial decision-making control from the government to hospital
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managers, which has allowed public hospitals to make a profit from the medical services they provide.
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However, after more than 20 years, some research suggests that healthcare reform in China has not
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made significant progress.14 In 2012, China’s State Council announced a new phase of healthcare
reform, allowing for more private hospitals and granting greater public hospital autonomy to improve
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efficiency.15 Therefore, studying the impact of hospital autonomy on efficiency is important, and
developing an effective tool for measuring such autonomy is the first challenge. With the Chinese
government’s support, and sufficient hospitals in Guangdong province to provide a good sample, we
carried out this study. The objective of this study was to develop a Chinese version of the Hospital
Autonomy Questionnaire (CVHAQ), which can be used to evaluate the level of hospital autonomy and
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to help policy-makers better understand the extent of policy implementation and formulate specific
policies to redefine the relationship between health authorities and hospitals.
METHODS
Theoretical model and questionnaire development
Although many researchers have proposed evaluation tools for hospital autonomy, there are three generic
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tools, namely, Chawla et al.(1996), Over and Watanabe (2003) and Harding and Preker (2003).2 The Chawla et
al (1996) tool proposes three key areas to be analysed to determine the existing level of autonomy:
administration, financing and inputs. The Over and Watanabe (2003) tool considers five elements of hospital
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structure: residual claimant status, decision right, degree of market exposure, availability of accountability
mechanisms and extent of unfunded mandates. The Harding and Preker (2003) tool proposes five dimensions
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for analysis of the extent of hospital autonomy: decision right, market exposure, residual claimant,
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accountability and social functions. We modified the three existing tools to develop a Chinese version of
the Hospital Autonomy Questionnaire.
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Figure 1 Flowchart of questionnaire development16 17
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A systematic approach was used to generate the final questionnaire.16 17 The development of items
for inclusion in the CVHAQ was carried out in five phases. First, an extensive literature review was
conducted to select possible subscales for the questionnaire. The theoretical basis for hospital
autonomy was the conceptual framework from Harding and Preker, which contained five subscales:
decision rights, residual claimant, market exposure, accountability and social functions.8 At the same
time, we also learned from the other two related measuring tools, and nine subscales were created to
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evaluate the extent of Chinese hospital autonomy: strategic management, budget and expenditure,
finance, human resources, day-to-day management, residual claimant, market exposure, accountability
and social functions. Second, after in-depth interviews and focus-group sessions, 40 potential subitems were developed. Third, ten carefully selected experts — including two lawyers, four hospital
management experts, two government officers, and two professors versed in statistics — were invited
to discuss relevant items for inclusion or exclusion. We used the Delphi method to evaluate the items.
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In two rounds, the experts gave their scores in terms of linguistic expression, importance, clarity and
correlation for each item, and then we deleted the items whose importance score was less than 7.5. The
remaining 32 items constituted the initial shape of the questionnaire. Fourth, a pilot test with 26
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selected hospitals was conducted to judge the appropriateness of each included question. Fifth, in a
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formal survey, six undergraduate students were selected as questionnaire interviewers, and two
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researchers in our focus-group were designated as quality controllers. After investigation, we used the
method of exploratory factor analysis to screen the items, and finally we deleted 10 items and extracted
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6 factors. (All loadings were larger than 0.5) To enhance the interpretability of the factors with high
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loadings, we rotated the extracted factors to a simple structure using the Varimax procedure
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(orthogonal rotation) to see if the result was consistent with our theoretical model. Furthermore, we
evaluated the reliability of the questionnaire and the Cronbach’s alpha coefficients in all subscales
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higher than 0.70. Meanwhile, we calculated the value of AVE (>0.5) and square roots of AVE to test
the validity of the questionnaire. After some necessary linguistic revisions, the questionnaire, including
six subscale (22 items) indicators of Chinese hospital autonomy, was finalised and used to build a
Chinese hospital autonomy model.
Responses were rated on a 5-point Likert scale for hospital autonomy (1 = totally constrained, 2 =
very constrained, 3 = partly constrained, 4 = hardly constrained, 5 = not constrained). Items were
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forward-scored on a 1- to 5-point scale (1=1, 2=2, 3=3, 4=4, 5=5), and subscale scores were computed
as the sum of each item score; that is, the higher scores indicated higher autonomy.
Figure 2
Steps of questionnaire development8
Quality control measures: item generation
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The items in this questionnaire were generated through literature reviews, in-depth interviews and
focus-group sessions. First, we reviewed PubMed and the China National Knowledge Infrastructure
(CNKI) to collect pertinent literature. Second, we set up a focus group to analyse the pertinent
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literature and preliminarily determined the subscales of our questionnaire. Finally, the members of the
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focus group reviewed the potential respondents or experts in-depth. When item generation continued
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until no new items emerged, we established an item pool with 40 items.
Then, the Delphi method was used for two rounds.
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Pilot testing
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The questionnaire was tested for how well respondents comprehended and correctly answered crucial
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questions, or to confirm that the questionnaire was user-friendly for the respondent and to ascertain
how long it took to complete.
Formal survey
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All of the questionnaire interviewers and quality controllers were systematically trained by the project
investigator (PI) before the investigation. During the investigation, the questionnaire interviewers were
responsible for connecting with respondents who were familiar with questions and reported regularly
to the PI. The quality controllers were responsible for checking the integrity and accuracy of the
finished questionnaire.
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Data entry
Double-blind input, consistency check and logical check were used to control the quality of data entry.
Data collection
This survey was conducted in 2013, and the data collection process consisted of two parts. First, an
electronic version of the questionnaire was sent to 404 secondary and tertiary hospitals in Guangdong
Province with the support of the Health Bureau of Guangdong Province. Second, senior management
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staff of these hospitals were contacted and asked to organise persons familiar with these issues to
complete the questionnaire. The completed questionnaires were then collected by the research team.
Data analysis
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SPSS 19.0 for Windows was used for data analysis. First, Cronbach’s alpha coefficients were
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calculated to evaluate the internal consistency reliability of the total questionnaire and each subscale.
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Cronbach’s alpha coefficient ≧0.70 was considered as acceptable. Second, multi-trait scaling analysis
(using Pearson correlation analysis) was conducted to determine the item-subscale correlations. If the
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analysis showed a stronger correlation (correlation coefficient ≧0.70) between an item and its
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hypothesised subscale, and with a coefficient larger than those of other subscales, the questionnaire
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was considered well-defined. To test the validity of the CVHAQ, an exploratory factor analysis was
conducted, and the extracted factors were rotated to simple structure by the Varimax procedure
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(orthogonal rotation) to see if the result would be consistent with the theoretical model. Varimax
rotation can enhance the interpretability of the factors with high loadings. The values of AVE and C.R.
were calculated to test convergent validity and discriminant validity. A confirmatory factor analysis
was then performed with AMOS 19.0 to assess the fitness of our modified theory model. The fit
indices of χ2/df (<2), GFI (>0.90), AGFI (>0.90), NNFI (>0.90), CFI (>0.90), RMSEA (<0.05) and
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SRMR (<0.08) were used to assess the fitness of the model. Missing item values from individual
surveys were imputed with the mean value.18
RESULTS
Responding groups
In total, 379 questionnaires were collected in this study. The characteristics of the participating
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hospitals are listed in table 1. The response rate was 93.81%. The percentage of missing values for
each item of the subscale varied from 0.00% to 3.63%.
Table 1 Characteristics of the participating hospitals
Variables
Classification
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specialised hospital
general hospital
secondary
tertiary
Pearl River Delta (PRD)
other areas in Guangdong Province
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Grade
Location
N
%
110
269
293
86
164
215
29.0
71.0
77.3
22.7
43.3
56.7
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Exploratory factor analysis
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To test the validity of the hypothesis, the exploratory factor analysis with Varimax factor rotation was
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conducted, and six components were extracted, which accounted for 73.05% of the total variance.
Results are presented in table 2.
Table 2
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Exploratory factor analysis with Varimax factor rotation
Subscale 1
Q1
0.82
Q2
0.92
Q3
0.87
Q4
0.89
Subscale 2 Subscale 3 Subscale 4 Subscale 5 Subscale 6
Q5
0.83
Q6
0.73
Q7
0.59
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Q8
0.84
Q9
0.84
Q10
0.85
Q11
0.65
Q12
0.77
Q13
0.79
Q14
0.76
Q15
0.77
Q16
0.76
Q17
0.83
Q18
Q19
Q20
Q21
Q22
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0.85
0.86
0.88
0.85
0.76
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Note: Only factor loadings of 0.50 or greater are reported.
Total variance explained=73.05%.
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Reliability
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The mean scores, standard deviations and Cronbach’s alpha coefficients are presented in table 3. All
internal consistency reliabilities exceeded the minimum reliability standard of 0.70, among which the
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overall scale coefficient was 0.87, and six subscale coefficients were 0.92 (strategic management),
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0.81 (budget and expenditure), 0.85 (financing), 0.75 (financing, medical management), 0.86 (human
resources) and 0.86 (accountability).
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Table 3 CVHAQ reliability for questionnaire and subscales
Mean
SD
Overall scale
Cronbach's
alpha
67.47 11.68
0.87
Subscale 1: strategic management
11.92
3.53
3.04
1.10
2.94
0.94
Scale/Item
0.92
Q1: The extent to which your hospital’s desire to develop a
vision and purpose is constrained by government
departments
Q2: The extent to which your hospital's development goals
are constrained by government departments
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Q3: The extent to which your hospital’s attempt to develop
a short-term development plan is constrained by
3.07
0.92
2.87
0.96
6.80
2.52
1.94
1.08
2.09
0.98
your hospital's other spending is constrained by government 2.78
0.90
government departments
Q4: The extent to which the hospital’s attempt to develop a
long-term development plan is constrained by government
departments
Subscale 2: budget and expenditure
Q5: The extent to which your hospital's drug procurement is
constrained by government departments
Q6: The extent to which your hospital's equipment
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procurement is constrained by government departments
0.81
Q7: Except for drugs and equipment, the extent to which
departments
Subscale 3: financing
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Q8: The extent to which your hospital's short-term
liabilities are constrained by government departments
Q9: The extent to which your hospital's long-term liabilities
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are constrained by government departments
11.16
4.20
2.92
1.23
2.59
1.26
2.60
1.32
3.05
1.23
9.83
2.28
3.03
0.95
3.51
0.89
0.85
Q10: The extent to which your hospital's other liabilities
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(finance leases, bills, etc.) are constrained by government
departments
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Q11: The degree to which government departments
intervene in your hospital’s ability to accept donations
Subscale 4: day-to-day management
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Q12: The degree to which government departments directly
intervene in your hospital’s medical business management
Q13: The degree to which government departments directly
Q14: The degree to which government departments directly
intervene in your hospital’s ability to provide convenience
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0.75
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3.28
0.96
11.46
3.54
2.66
1.05
3.26
0.99
2.69
1.07
services
Subscale 5: human resources
Q15: The extent to which your hospital’s ability to recruit
employees is constrained by government departments
0.86
Q16: The extent to which your hospital’s policies for staff
rewards and punishment are constrained by government
departments
Q17: The extent to which your hospital employee salary
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structure is constrained by government departments
Q18: The extent to which your hospital’s ability to dismiss
employees is constrained by government departments
Q19: Whether the purposes of the inspection set by
government departments for your hospital are clear
Q20: Whether the assessment indicators set by government
departments for your hospital are clear
Q21: Whether the assessment indicators set by government
departments for your hospital are reasonable
Q22: The degree to which recognition of government
departments’ inspection affects your hospital
1.12
4.22
0.89
4.06
0.99
3.68
0.89
4.34
0.73
0.86
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Item-subscale correlations
2.85
16.30 2.95
Subscale 6: accountability
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Pearson correlation coefficients between subscales and items are presented in table 4. The results
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showed that items had higher correlation coefficients as compared with the theoretical subscale than
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with the other subscales.
Table 4
Subscale 1
Item-subscale correlations of CVHAQ
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Subscale 2 Subscale 3 Subscale 4 Subscale 5 Subscale 6
0.32
0.22
-0.04
0.32
0.27
0.07
0.25
0.37
0.23
0.07
0.36
0.21
0.32
0.25
0.07
0.33
0.86
0.40
0.29
0.31
-0.04
Q6
0.35
0.89
0.55
0.30
Q7
0.28
0.81
0.58
0.34
Q8
0.21
0.52
0.87
Q9
0.18
0.54
Q10
0.19
Q11
Q1
0.86
0.33
0.22
Q2
0.94
0.33
0.23
Q3
0.90
0.33
Q4
0.91
Q5
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0.47
-0.05
0.37
0.01
0.29
0.35
-0.04
0.88
0.29
0.31
-0.00
0.48
0.87
0.24
0.30
-0.06
0.26
0.43
0.72
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-0.00
Q12
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0.36
0.37
0.85
0.32
-0.03
Q13
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0.29
0.28
0.80
0.25
0.06
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0.23
0.15
0.80
0.22
0.02
Q15
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0.38
0.28
0.26
0.81
0.04
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Q16
0.31
0.40
0.33
0.34
0.82
-0.05
Q17
0.19
0.41
0.34
0.27
0.86
-0.01
Q18
0.18
0.33
0.28
0.21
0.86
0.01
Q19
0.01
-0.05
-0.03
0.02
0.01
0.86
Q20
0.01
-0.04
-0.06
0.01
-0.05
0.89
Q21
0.07
0.01
0.01
0.02
0.04
0.85
Q22
0.06
-0.04
-0.01
0.01
-0.01
0.75
Convergent validity and discriminant validity
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If the value of AVE was higher than 0.5 and the value of C.R. was higher than 0.70, we considered the
convergent validity acceptable. To test discriminant validity, we calculated the square root of every
AVE value belonging to each latent subscale. The outcomes (in table 5) show that the square roots of
the AVE of each subscale were larger than the correlation of the specific subscale with any of the other
subscales.
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Table 5 Convergent and discriminant validity for questionnaire and subscales
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Strategic
Variable
AVE
C.R.
management
Strategic
management
expenditure
Day-to-day
Human resources
Accountability
expenditure
management
resources
0.76
0.93
0.87
0.58
0.81
0.38
0.76
0.61
0.86
0.25
0.59
Accountability
0.51
0.75
0.37
0.36
0.60
0.85
0.27
0.45
0.37
0.32
0.77
0.61
0.86
0.04
-0.04
-0.03
0.02
0.00
0.78
0.33
0.71
0.78
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Financing
Day-to-day
Financing
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Budget and
Budget and
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Note: On the diagonal we inserted the square roots of every AVE value to compare it with the other correlation coefficients.
Confirmatory factor analysis
We used AMOS 19.0 to conduct confirmatory factor analysis, and seven indicators were calculated to
test the fitness of our theory model. RMSEA and SRMR were less than 0.08, whilst GFI, AGFI, NNFI
and CFI were higher than 0.9, leading to acceptable construct validity. The results are presented in
table 6.
Table 6 Fitness of fit of CVHAQ
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χ2/df
GFI
AGFI
NNFI
CFI
RMSEA (95%CI)
SRMR
1.73
0.93
0.91
0.96
0.97
0.04 (0.03,0.05)
0.07
Note. χ2=Minimum Fit Function Chi-square; df=degree of freedom; GFI=Goodness of Fit Index; AGFI = Adjusted Goodness of Fit
Index; NNFI = Non-Normed Fit Index; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation; CI =
Confidence Interval; SRMR = Standardised Root Mean Square Residual.
χ2/df<2.0, GFI>0.90, AGFI>0.90, NNFI>0.90, CFI>0.90, RMSEA<0.08, SRMR<0.08.
DISCUSSION
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In this study, the hospital autonomy survey was modified to adapt to Chinese health policies, and the
CVHAQ was developed based on Harding and Preker’s conceptual framework on hospital autonomy,
as well as the tools developed by Chawla et al (1996) and Over and Watanabe (2003)2. The reliability
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and factor structure of the CVHAQ were assessed. Results of this study showed that the CVHAQ was
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a reliable and valid questionnaire for the measurement of hospital autonomy. The confirmatory factor
analysis confirmed the theoretical model, which reflected the extent of hospital autonomy. The higher
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scores the hospitals had, the more autonomy they had. According to the Chinese Health Statistics
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Report (2015),3 government fiscal budgets provide about 7% of total revenue for public hospitals.
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Almost all secondary and tertiary hospitals must earn profit from the medical services they provide, so
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there is little variance in residual claimant and market exposure between sample hospitals that could be
captured by the questionnaire. Social function is a complementary reform to ensure that services that
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were previously cross-subsidised continue to be delivered, and does not belong to the scope of hospital
autonomy. The six theoretical subscales included in the final CVHAQ well reflected the status of
hospital autonomisation. Results of confirmatory factor analysis showed that all indicators of fitness
were acceptable.19-21 This study showed that subscale 2 and subscale 3 were correlated (r=0.59), which
can be explained by the fact that the autonomy of the two subscales commonly depends on the
economic policy for a hospital, which is set by the government; thus, finances, budget and expenditure
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are synergistically affected. Subscale 5 and subscale 2 were also correlated (r=0.45). The underlying
reason could be that the Chinese government spent little on human resources, and the cost was borne
by the hospital.22 23
Characteristics of local hospitals in Guangdong made it very suitable for conducting this largescale quantitative study. China has traditionally managed its public hospitals as administrative units.
However, in the past 30 years, the Chinese government has been reforming its health system by
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reducing the health department’s direct control over public hospitals and shifting the day-to-day
decision-making from government authority to hospital managers, to reduce the government’s
financial burden and improve hospital efficiency.24 Guangdong, the frontier of China’s economic
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reform, has more than 400 secondary and tertiary public hospitals. They are divided into national
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hospitals, provincial hospitals and municipal or county hospitals. Because of different levels of
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government input and degrees of decentralisation, these hospitals vary greatly in autonomy, which
created a unique opportunity for us to conduct this study in Guangdong. In addition, this study was
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supported by the Health Bureau of Guangdong Province, which resulted in a higher response rate from
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local hospitals. This helped overcome the issues of small sample size and data constraint existing in
previous studies.
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quantitative. Most previous studies were based on case study, and the results were subject to potential
confounding factors, such as hospital size, management style, geographic location, etc.25 Empirical
literature to date has done little to explore a hospital autonomy questionnaire to quantify the extent of
hospital autonomy. The CVHAQ developed in this study helped fill this gap in the published literature
and provided a validated tool that can help overcome these shortcomings and enable reliable
conclusions to be drawn.
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In addition, the CVHAQ could lay a strong foundation for quantitative analysis of the relationship
between hospital autonomy and its effect on healthcare reform. For example, a regression analysis can
be done to explore the relationship between hospital autonomy and efficiency by using the hospital
autonomy scores as independent factors.
Some limitations must be mentioned regarding our findings. The survey was conducted only in
Guangdong Province; thus, generalisation of the results to other provinces should be done with caution.
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In our future study, we will investigate hospital autonomy in other provinces in China to further
validate our questionnaire. In addition, we conducted a cross-sectional study that could not illustrate
the underlying trend of hospital autonomy. In future studies, we plan to collect panel data to monitor
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hospital autonomy and explore the relationship between hospital autonomy and efficiency, to provide
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an evidence-based foundation for China’s healthcare reform.
CONCLUSIONS
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This study demonstrated the reliability and validity of a Chinese version of a hospital autonomy
questionnaire, and provided a quantitative method for the assessment of hospital autonomy in China.
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Acknowledgements
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We thank Qiumao Cai, Lina Yu and Chunxiao Wang at the Health Department of Guangdong
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Province for their assistance. This research was funded by the Guangdong Medical Fund of China
(C2013016).
Author contributions
ZFL, FTL and YXH obtained the funding. ZFL, YXH and LLZ conceived the idea and designed the
study. ZFL and LXY collected and processed the data. ZFL, YXH and LLZ drafted the manuscript. All
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authors critically revised the manuscript for important intellectual content. All authors read and
approved the final version of the manuscript.
Conflicts of interest
The authors declare no conflicts of interest.
Ethics approval
rp
Fo
Medical Ethics Committee, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen
University.
Data sharing
No additional data available.
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Development of the Chinese version of the
Hospital Autonomy Questionnaire: a
cross-sectional study in Guangdong Province
Zifeng Liu, Lianxiong Yuan, Yixiang Huang, Lingling Zhang and Futian Luo
BMJ Open 2016 6:
doi: 10.1136/bmjopen-2015-010504
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