Factors influencing contracted services of Chinese family doctors from the perspective of medical staff and consumers: A cross-sectional study

Background: The family doctor system has gained rapid ground worldwide. In recent years, China has been actively exploring family doctor-type contracted services. This study aimed to explore the factors influencing Contracted Family Doctors Services (CFDS) from the perspectives of medical staff and consumers, and to provide a stronger basis for the development and promotion of CFDS. Methods: This study used a combination of quantitative and qualitative methods. A self-reported questionnaire was designed through a literature analysis, group discussions, expert consultations and a pre-survey. A cross-sectional survey was conducted among community health service providers and administrators in 12 community health service centres across four provinces of China. A total of 389 people participated, and 320 valid questionnaires were obtained, with an effective response rate of 82.3%. A total of 36 consumers participated in in-depth interviews, and the total effective rate was 100.0%. Exploratory factor analysis, confirmatory factor analysis, inductive methods, and expert consultations were used to analyse the factors influencing CFDS. Results: The factors influencing CFDS from the perspectives of medical staff were divided into four dimensions, with the following weighting coefficients: national government (31.87%), community health service agency factors (24.73%), consumer-related factors (22.58%), and contracted doctor-related factors (20.82%). The factors influencing CFDS from the perspectives of patients/consumers were national policy factors, contracted team factors, and consumer-related factors. Conclusions: National governments, community health agencies, community health workers, and consumers play important roles in the advancement of CFDS. Therefore, the development of CFDS is based on the rights and interests of all stakeholders involved.


Background
The family doctor system, as an important policy measure to realize the Alma-Ata Declaration's grand goal of primary health care for all, has gained rapid ground worldwide [1]. According to a World Health Organization report, the proportion of all patients with diseases requiring specialized medical treatment was only about 5%, and more than 90% of health problems could be effectively solved by professionally trained general practitioners [2]. The role of general practitioners/family doctors, according to the World Organization of Family Doctors, is to provide comprehensive health care services to every person seeking them, and to arrange for other health professionals to provide related services when necessary [3]. So far, this concept of family doctors has been accepted by many countries. The family doctor system has been implemented in more than 50 countries and regions in the world and has achieved gratifying results in various respects, which has attracted the attention of governments and medical circles [4]. There exist obvious differences in the specific service modes and operating mechanisms in various countries. Moreover, there is no doubt that the family doctor system plays an important role in medical and health service systems across countries. The family doctor system originated in the United States in the 1960s [5], when the government integrated health management into the community general practitioner service model [6]. At the same time, active follow-up observations were performed on patients with chronic diseases and during the rehabilitation period [6].
In the UK, the National Health Service was established in 1948 and adopted the national management model, which requires citizens to register their family doctors and sign contracts with them [7]. Cuba established the family doctor program, in which a family physician and at least one nurse were each responsible for providing primary disease prevention and medical treatment services to 120-150 families [8]. The family doctor system in Denmark is a form of health care developed from the private doctor system, and each family doctor is responsible for a maximum of 1,780 health care-registered patients/consumers [9].
However, in China, the idea of 'family doctors' was introduced in the 1980s, when 'family doctor-type contracted services' were proposed. In 2003, the Chinese government put forward the family doctor system as the goal of the development of community health services, so that everyone can enjoy family doctor services [10]. The promulgation of the Guiding Opinions of the State Council on the Establishment of the General Practitioner System in 2011 indicates that the establishment of the family doctor system has risen to becoming a national strategy [11]. Subsequently, the training objectives, training methods, training contents, and ability requirements of general practitioners were further clarified [12]. The Guideline on Promoting Contracted Family Doctor Services was issued to accelerate family doctor contract services [12]. The National Health and Family Planning Commission also clearly pointed out that the work of family doctors has changed to improve quality and efficiency [13]. So far, the contracted family doctor services (CFDS) have been promoted in a systematic way across China.
In recent years, China has been actively exploring family doctor-type contracted services.
At present, on the basis of summing up practical experience, five typical contracted service models have been formed, for example, the '1+1+1' contracted service model in Shanghai [14], 'basic package+personalized package' contracted service model in Yancheng, Jiangsu Province [15], 'integrated Medical treatment and nursing care system' contracted service model in Hangzhou, Zhejiang Province [16], 'co-management of doctors of three kinds' contracted service model in Xiamen, Fujian Province [17], 'capitation prepayment' contracted service model in Dingyuan, Anhui Province [15].
The research on family doctors in China has mainly focused on the significance and difficulties of establishing a family doctors system, or explored the policy effects of family doctors in terms of service utilization, management of non-communicable diseases, medical expenses and satisfaction [18][19][20][21]. Some researchers have also noticed the importance of establishing stable relationships with family doctors, as well as contract status and contract behaviour [22][23]. Previous studies on CFDS in China focused on five aspects: allocation of work resources, optimization of contracted service content, exploration of team performance appraisal mechanisms, change of payment mode of medical insurance for contracted services, willingness and satisfaction of contracted services, and work competency and stability [14,[24][25][26].
Relevant scholars have proposed some suggestions and directions for the development of CFDS and have also analysed certain factors CFDS [27][28]. Although there have been studies on the factors influencing CFDS in China [27][28], this study differs from them in several respects. First, this study examined the factors influencing factors CFDS as a whole, rather than just from a single point of view such as that of patients/consumers or medical staff. Second, the investigation area of this study is from typical areas of CFDS, rather than a single city from a certain area. Third, the most important difference is that the study is set against the backdrop of 'Healthy China 2030' (the 'Healthy China 2030' plan was released by the central government in 2016 and is an important long-term national strategic plan; family doctor service has been regarded as an important tool to achieve Health China 2030.) [29], China improves health services for senior citizens (this project aims at preventing and controlling chronic diseases and promoting good health among senior citizens, and calls on family doctors to play a more important role) [30], and a circular on finishing CFDS in 2019 (which aims to improve the quality of CFDS and promote the quality and efficiency of CFDS) [31].
Based on the new policy environment, this study explored the factors influencing CFDS from the perspectives of community health service providers, administrators, and medical staff, which can help to improve the contracting rate of family doctors, promote the health of consumers, and provide a basis for the government and health administration to formulate policies for CFDS.

Study design and population
A cross-sectional survey was used to select 12 community health service centres in four regions of Zhejiang, Anhui, Beijing, and Shanghai provinces in a typical area survey. First, a typical city with CFDS in each province was purposively selected. Then, according to the development of CFDS (high, medium, and low), three community health service centres were selected by purposive sampling. The cluster sampling method was used to conduct a questionnaire survey on the directors of the community health service centres, the CFDS team from community health service agencies, and the administrative staff. Moreover, convenience sampling was used to conduct in-depth interviews with patients/consumers in the same regions. This study mainly combined qualitative (interview) and quantitative (questionnaire) methods, although only at the methodological level. A questionnaire survey was used to explore the factors influencing CFDS from the perspective of medical staff, and interviews were conducted to explore the factors influencing CFDS from the perspective of patients/consumers.

Data collection
Data were collected from July through September 2017. Respondents filled out an anonymous questionnaire after providing informed consent. A total of 389 questionnaires were distributed. While all of them were returned, 320 questionnaires were valid, and the total effective rate was 82.3%. The reasons for elimination of questionnaires included missing data or incomplete questionnaires, multiple omissions, and multiple choices. We interviewed three patients/consumers in each community health service centre. A total of 36 patients/ consumers were interviewed. The total effective rate was 100.0%.

Questionnaire
The questionnaire was designed based on the following steps. First, we tried to select as many items as possible by searching the relevant literature [32][33]. Next, five experts (including health service management experts, public health experts, and contracted services researchers) were consulted to revise and improve the questionnaire. Finally, a pilot study was conducted at four community health service centres (120 responses were not included in the final data analysis), and the questionnaire was further refined and finalized (Cronbach's α was 0.865). The final questionnaire comprised the following sections: 1.

2.
The factors influencing CFDS, including 29 items. Using a five-point Likert scale, each item was divided into five levels (very important, important, neutra, unimportant, and very unimportant) according to the influence degree. Participants rated each item according to their own experiences.

Data analysis
Data were analysed using IBM SPSS Statistics 20.0 and AMOS 21.0. Descriptive statistics were used to analyse the demographic characteristics of participation in community health service agencies. Factor analysis (both exploratory and confirmatory factor analyses) was used to determine the factors influencing CFDS [34][35]. Data is suitable for exploratory factor analysis if Kaiser-Meyer-Olkin is greater than 0.7. The number of factors can be assumed beforehand according to the actual conditions, or the number of dimensions can be determined according to the criterion of eigenvalues greater than 1 or by examining the scree plot. The principal components method was chosen to extract the common factors. Items were excluded according to the following criteria: a factor loading of <0.40; high loadings on multiple factors; and a factor with less than three items included.
Orthogonal rotation was used to determine simple structure. The method of calculating factor weights was as follows: ω i = [(m∑ j ) β ij ×e j ] / [(n∑ i )(m∑ j ) β ij ×e j ]; F j = β 1j ×X 1 + β 2j ×X 2 + β 3j ×X 3 + ……+ β nj ×Xn; F j is the principal component (j=1, 2,……m), X 1 , X 2 , X 3 , …… Xn for each index, β 1j , β 2j , β 3j , ……β nj for coefficient score of each index in principal component F j , e j is used to express the equation contribution rate of F j . The confirmatory factor analysis model was considered to have a good fit when all path coefficients were significant at the level of 0.05; χ2/df was below 5; root mean square error of approximation (RMSEA) was below 0.08; root mean square residual (RMR) was below 0.10; and the goodness of fit index (GFI), normed fit index (NFI), Tucker-Lewis incremental (TLI) fit, and comparative fit index (CFI) were ≥0.90. A p-value of <0.05 was considered statistically significant.

Results of in-depth interviews
Through in-depth interviews with patients/consumers, their reasons for being more willing to accept CFDS were identified and summarized. Examples included patients/consumers' understanding level of CFDS, benefits of CFDS, concern about one's own health; the degree of family doctors' protection of patients/consumers' privacy, cost and process of signing a contract, satisfaction with the community, and advocacy of contracting services, etc. The keywords extracted from the interviews were counted and we found that the factors affecting CFDS from the perspective of consumers were as follows: national policy factors, contracted team factors, and consumer-related factors.

Demographic characteristics of community medical staff
Of the 320 respondents,72.2% were women, 56.6% had received an undergraduate education, and 43.4% had intermediate titles. Among respondents, general practitioners were the main occupations, with 57.5% of respondents working longer than 10 years. The demographic characteristics of the medical personnel are shown in Table 1.

Exploratory factor analysis
The Kaiser-Meyer-Olkin value was 0.836, which showed that the data could be used for factor analysis (if the Kaiser-Meyer-Olkin value is close to 1, the variable group is suitable for factor analysis).
After finishing the orthogonal rotation of the factor loading matrix, the remaining 25 items had eigenvalues >1, orthogonal rotation explained the maximum amount of variance, seven factors were extracted from the system, and the cumulative variance contribution rate of 67.613% is shown in the Supplementary form. Table 2 shows that 25 observational variables were clearly classified into seven common factors. Based on the results of the group discussion among project team members, seven factors were named according to the characteristics of the variables observed. F1 was 'national policy factor', the combination of F2 and F3 was 'resident factors', the combination of F4 and F5 was 'contract doctor factors', and the combination of F6 and F7 was 'community factors'.

Confirmatory factor analysis
The results of the confirmatory factor analysis were as follows: the RMSEA was 0.059, and thus was less than the 0.08 cutoff that indicates a good fit; the RMR was 0.05; The TLI, NFI, GFI, and CFI were 0.913, 0.902, 0.905, and 0.917, respectively.

Results of expert consultations
The health and family planning commission, contracted services researchers, and administrators were selected to carry out an expert consultation. The above results were modified according to their inputs, and the final versions of the predisposing factors are listed below.
In the first round of consultation, the name of each dimension in the model was modified: 'national policy factors' was revised to 'national government factors', 'resident factors' was revised to 'consumers-related factors', 'contracted doctor factors' was revised to 'contracted doctor-related factors', and 'community factors' was revised to 'community health service agency factors'.
The second round of consultation integrated the dimensions of the model. The experts deemed that the 'situation of the first diagnosis of the patients/consumers' should be incorporated into the resident-related factors dimension rather than contracted doctorrelated factors.

The final determinants of the factors of CFDS
The final determinants of the factors of CFDS are shown in Table 3. The factors influencing CFDS from the perspective of medical staff were divided into four dimensions and 25 items. The four dimensions were named national government factors, community health service agency factors, consumer-related factors and contracted doctor-related factors, respectively.
Indicator weight = composite score/model coefficient.
Based on the above results, we merged similar factors into four dimensions. Finally, the weight coefficients of the common factors were 0.319, 0.247, 0.226, and 0.208, respectively (see Table 4 for specific details).

Discussion
Comparing the factors affecting CFDS from the perspectives of patients and of medical staff, we found that the factors of national policy and community health service agency are two important common factors affecting CFDS based on the results of interviews, surveys and expert consultations.
The results showed that the national government factor is the main trigger that affects CFDS. The government, as the dominant force in the establishment and operation of contracted services, is obligated to ensure the smooth progress of CFDS, to guarantee the fairness and accessibility of services, and to be the conductor of the contracted services policy.
The government has developed universal health insurance coverage, basic public health service plans, and the national essential drug system, all of which have improved access to and affordability of primary health care [36]. From the perspective of the national government, it is very important to increase financial support for primary health facilities and special funds. At the same time, the government should play its part in macroeconomic regulation and control and combine related departments such as finance and social security to increase support for inclination to support of community medical institutions, and guide the insured personnel to give priority to primary clinics. In The results showed that the community health service institutions factor ranked second.
The community health service agency is the executor of the contracted services policy.
The extent of its power of execution directly determines the direction of CFDS, and the completeness of the hardware and software facilities of a community health service organization is the basis of its power of execution. Therefore, the degree of development is also a way of recognizing and respecting employee performance and contributions [38].
Therefore, community health agencies should establish a sound supporting performance appraisal system and incentive mechanism, to increase employees' enthusiasm toward their work, helping them better serve patients/consumers. full play to the role of media such as television, internet and newspapers, and to create a good atmosphere of public opinion for understanding and supporting the services of family doctors. In addition, the level of fees is also a key issue of concern to residents; formulating reasonable contracting fees is also the key to promoting family doctor-style services. It is suggested that residents can buy medical insurance that meets their own needs and use medical insurance to pay for the contract fees. This not only attracts more residents who have purchased insurance to sign contracts, but also reduces patients/consumers' concerns about the level of fees to a certain extent, which is similar to the results of Wu Jun and other colleagues [39]. At the same time, we also recommend that patients/consumers respect, support and cooperate with family doctors, which may University. Before the survey, we received approval from the community health centre.

Consent for publication
Not applicable.

Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests
The authors declare no conflicts of interest.

Authors' contributions
HW, LS and XH conducted calculations, analyzed results, drafted the manuscript, and contributed equally to this work. HW, LS and LF were responsible for the overall design of the research, organized and conducted the survey, and designed the analyses framework.
HW, LS and XH revised the paper. HW, LS, XH and JZ assisted with the literature review and data collection. All authors approval of the current version of this manuscript for publications.