Frontiers in Computing and Intelligent Systems 
ISSN: 2832-6024 | Vol. 4, No. 3, 2023 

 

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Exploration	on	the	Construction	of	County	Medical	
Community	Information	Platform	
Luyao Zhang 1, 2, *, Lecai Cai 2 

1 School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong, China 
2 Sanjiang Research Institute of Artificial Intelligence and Robotics, Yibin University, Yibin, China 

 

Abstract: By combing the development status and key problems of the county medical community model, analyzing the 
practical scenarios of the county medical community, combining Big data, cloud computing, artificial intelligence related 
technologies, designing the architecture model of the county medical community information platform, and exploring the 
construction methods of the county medical community information platform that meet the social development status and meet 
the people's health needs. It provides reference for the landing and development of future medical informatization, medical Big 
data platform and county medical community information platform. 

Keywords: County Medical Community; Big Data; Artificial Intelligence; Informatization. 
 

1. Introduction 
The contradiction between the insufficient and unbalanced 

development of medical resources in China and the increasing 
health needs of people has become a major contradiction that 
affects the quality of life of the people. To address this issue, 
in May 2019, the National Health Commission issued a notice 
on promoting the construction of a compact county-level 
medical and health community [1]. Since then, China has 
officially started the construction of a medical community. 
The so-called "medical community", also known as the 
medical community, refers to the integration of medical and 
health resources at the district and township levels, led by 
district level hospitals, to form a medical system, maximizing 
resource and technological advantages, gradually improving 
the quality of medical and health services at the county level, 
constructing a new order of graded diagnosis and treatment, 
reasonable diagnosis and treatment, and orderly medical 
treatment, and focusing on enhancing the public's sense of 
health gain, happiness, and security [2]. The construction of 
the medical community will fundamentally solve the 
problems of people's "difficulty, annoyance, and high cost of 
medical treatment". Using cloud computing, Big data, 
artificial intelligence and other related technologies to build a 
reasonable and advanced Big data cloud service platform to 
provide services and assistance for patients, medical 
personnel, and scientific researchers will become an 
important research and work direction of hospital 
informatization in the future. 

2. Current Situation Analysis 

2.1. Proposal of County Medical Community 
The Guiding Opinions of the General Office of the State 

Council on Promoting the Construction and Development of 
Medical Federations (Guo Ban Fa [2017] No. 32) issued in 
April 2017 clarified the pilot tasks for exploring the 
framework and forms of medical consortium construction, 
and requested the formation of a relatively complete policy 
system for medical consortium by 2020, with specific goals 
for comprehensively promoting the construction of medical 
consortium [1]. Among the four modes of establishing 

medical communities, the concept of "mainly establishing 
medical communities in counties" is proposed [3]. At this 
point, the county-level medical service community was 
officially launched in national policies for the first time, 
becoming an important lever to enhance the capacity of 
grassroots medical services and assist in the construction of 
graded diagnosis and treatment systems. In April 2018, the 
Opinions of the General Office of the State Council on 
Promoting the Development of "Internet plus Medical 
Health" (Guo Ban Fa [2018] No. 26) was released, which 
proposed the requirements of promoting the gradual 
extension of telemedicine services to community health 
service institutions, township hospitals and village clinics, 
and improving the capacity and efficiency of primary medical 
services [4]. In August of the same year, the "Notice on 
Further Improving the Key Work Related to the Construction 
of Graded Diagnosis and Treatment System" (Guo Wei Yi Fa 
[2018] No. 28) was issued, emphasizing the need for 
coordinated planning and accelerated promotion of the 
construction of medical consortia [4]. The formation of 
county-level medical communities and urban medical groups 
was proposed for the first time, and the capacity building of 
county-level hospitals became a top priority in promoting 
urban-rural separation and county-level graded diagnosis and 
treatment. 

2.2. Concept of County Medical Community 
2.2.1. Medical Community 

The so-called medical community refers to the organic 
integration of regional health resources in a region, mainly 
referring to grassroots hospitals and health institutions within 
the county. With county-level medical institutions as the main 
leaders, they contact several township or village health 
centers to establish and promote mutual cooperation and 
integration of interests, responsibilities, and services, and 
promote the optimization, integration, rational utilization, and 
sinking of medical and health resources to the grassroots level, 
A medical and health service community that guides the 
public to receive initial diagnosis and treatment at nearby 
grassroots medical institutions in the early stages of illness[5]. 
Try to provide basic and effective solutions for the diagnosis 
and treatment of most common and frequently occurring 



 

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diseases within the county. 

2.2.2. County Medical Community 
County level medical community is a county township 

integrated management system led by county-level hospitals, 
with township health centers as hubs, and village clinics as 
the foundation, effectively connecting with rural integrated 
management [6]. Fully leverage the urban-rural linkage and 
leading role of county-level hospitals, form a division of labor 
and cooperation mechanism for county-level and rural 
medical and health institutions, and build a three-level linkage 
county-level medical service system [7]. 

2.3. Current Situation of the Construction of 
County Medical Community 

In 2019, the National Health Commission launched a pilot 
project for the construction of a compact county-level medical 
community nationwide, identifying 754 counties in Shanxi 
and Zhejiang provinces, as well as 567 counties (cities, 
districts, etc.) in other provinces [8]. In 2020, out of the 754 
pilot counties, 535 met the standards of a compact county-
level medical community, accounting for 71% [8]. The 
construction of county-level medical communities has 
reached consensus in most provinces and is showing a trend 
of comprehensive promotion [8]. In 2021, Xinjiang will be 
added as a pilot province. As of April 2023, the National 
Health Commission reported that China has piloted the 
construction of a "compact county-level medical community" 
in over 800 counties and cities. More than 70% of the more 
than 800 "tight county medical community" pilot projects 
nationwide have implemented unified management of 
personnel and drugs, over 90% have achieved mutual 
recognition of inspection and testing results within the 
medical community, and the county level medical treatment 
rate in the pilot areas has exceeded 90%. 

3. Requirement Analysis 

3.1. User Requirements 
Government: Through the construction of county-level 

medical communities, achieve the optimization, integration, 
and rational utilization of medical and health resources within 
the county; Real time supervision of hospitals at all levels 
within the medical community, timely understanding of the 
needs of the people within the county, guiding them to seek 
timely treatment nearby, and enabling the diagnosis and 
treatment of common and frequently occurring diseases to be 
addressed within the county. 

Hospitals: County level hospitals efficiently and 
reasonably utilize existing resources, establish health records 
for residents within the county, comprehensively grasp patient 
information, assist grassroots doctors in diagnosing difficult 
diseases, and play a leading role in providing health 
protection for residents within the county; Grassroots 
hospitals achieve daily diagnosis, collect health information 
from residents within the county, and create a solid foundation 
for the construction of medical communities. 

Doctor: Specialized doctors provide personalized diagnosis 
and treatment plans for patients, assisting grassroots doctors 
in completing disease diagnosis and follow-up; Grassroots 
doctors complete disease diagnosis and information input 
with the assistance of specialized doctors. 

Residents: The demand for medical treatment has been 
improved, mainly reflected in convenient and efficient 
medical services, uninterrupted health information, and full 

lifecycle health management, achieving the goal of not 
leaving the county for major illnesses and not leaving the 
countryside for minor illnesses [9]. 

3.2. Functional Requirement 
3.2.1. Resource Sharing 

Utilizing existing medical resources, establish an open and 
shared resource sharing center within the medical community 
for medical testing, imaging diagnosis, electrocardiogram 
diagnosis, pathological diagnosis, clinical medical records, 
and medical data. Relying on the medical community 
platform, connect medical and health institutions and 
business systems at all levels, break down information 
barriers between different levels, and fully utilize existing 
medical resources. 

3.2.2. Data Management 
Establish a medical big data center to integrate the 

"heterogeneous" data of different systems, data types, and 
data structures in existing medical institutions. At the same 
time, establish a unified standard to ensure information 
interoperability, reduce repeated information entry, and 
achieve one-time entry and multiple use. Establish multiple 
databases to classify and store different business data, 
facilitating data analysis and mining. 

3.2.3. Intelligent Diagnosis and Treatment 
According to the disease diagnosis process, help collect 

diagnostic and treatment information standards, provide 
standardized diagnostic assistance, push disease related 
knowledge to help grassroots doctors comprehensively 
understand, recommend personalized diagnosis and treatment 
plans based on patient conditions, and recommend 
corresponding specialized doctors to use remote medical 
technology to assist grassroots medical personnel in 
providing professional treatment for patients. 

3.2.4. Information Security 
Due to the unique nature of medical data, it is necessary to 

ensure its privacy, availability, and controllability. While data 
is open and shared, it is necessary to ensure that patients' 
privacy rights are not violated; While collecting and 
transmitting data, it is necessary to ensure the accuracy of the 
data to avoid uncontrollable errors and diagnostic biases in 
subsequent analysis; When accessing data, different 
permissions should be designed based on different roles to 
ensure data controllability. 

4. Construction Plan 
The overall framework of the system is the county medical 

community model, the bottom is the Cloud computing# 
Private cloud resource pool, and the top is the platform 
construction. The first is to open up the hospital led by the 
medical community, and then the hospital led by each medical 
community and the county health platform, to achieve 
information exchange and data sharing. Further up, it is the 
business system in the whole county, including the data center, 
Business support system and various application systems, 
such as family doctor service management, various 
businesses of medical community, clinical auxiliary diagnosis 
and treatment decision-making, medical management 
decision-making, comprehensive management, Internet 
based online services and other applications. The overall 
architecture of the county medical community is shown in 
Figure 1. 

 



 

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Figure 1. Overall Architecture of County Medical Community 

 

4.1. Medical Big Data Center 
In order to solve the problems such as information barriers, 

data islands, and data structures that exist in current medical 
data, the medical informatization industry coincidentally 
began to build medical Big data centers. It aims to integrate 

the "heterogeneous" data of different systems, different data 
types and different data structures in medical institutions 
through the medical Big data center. The medical Big data 
center consists of six parts: data resources, data collection and 
conversion, data storage, data analysis, data value mining and 
application, and user subjects, as shown in Figure 2. 

 

 
Figure 2. Framework of medical Big data center 

 

4.2. Family Doctor Service Management 
System 

The county-level medical community model proposes the 
signing service for family doctors. The family doctor service 
management system designed in this project focuses on the 
daily work of family doctors, and revolves around the signing 
management and establishment of health records for residents 
by family doctors, conducting a series of public health work 

such as health assessment, diagnosis and treatment 
intervention, disease tracking, follow-up, and basic medical 
services, By using mobile internet and intelligent mobile 
terminals to provide data feedback to specialized doctors, 
doctors can provide timely remote diagnosis and treatment of 
patients, thereby further optimizing personal body testing and 
health management services [11]. The architecture of the 
family doctor service management system is shown in Figure 
3. 



 

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Figure 3. Architecture diagram of family doctor service management system 

4.3. Diagnosis and Treatment Auxiliary 
Decision-making System 

The diagnosis and treatment assistant decision-making 
system based on deep learning takes the intelligent decision-
making engine and medical knowledge base as the core, 
follows the essential principle of disease occurrence and 
development, and uses the ontology based Semantic network, 
artificial intelligence, deep learning neural network algorithm 
and other cutting-edge technologies to "learn" the guidelines, 
literature, relevant medical records and other data related to 
disease diagnosis and treatment, self-improve the knowledge 
base, rule base and decision-making engine model to achieve 
accuracy Efficient and intelligent comprehensive analysis and 
judgment, providing precise solution push for basic 
examinations, diagnosis, and treatment services involved in 
the diagnosis and treatment process of doctors. 

The service process of the diagnosis and treatment 
assistance decision-making system based on deep learning is 

mainly used by grassroots doctors, and is oriented towards the 
diagnosis and treatment process, achieving applications in 
different scenarios such as initial diagnosis and follow-up 
diagnosis. Its auxiliary functions mainly include: assisting in 
the standard collection of diagnosis and treatment information 
(patient symptoms, signs, and auxiliary examinations); 
Provide standardized diagnostic assistance to avoid 
misdiagnosis and missed diagnosis; Assist grassroots medical 
personnel and patients to have a comprehensive 
understanding of disease knowledge; Recommend 
personalized diagnosis and treatment plans for grassroots 
doctors to facilitate disease diagnosis and treatment; At the 
same time, when encountering difficult cases, corresponding 
specialized doctors can also be recommended to assist 
grassroots medical personnel in providing professional 
treatment for patients. The service process of the diagnosis 
and treatment assistance decision-making system is shown in 
Figure 4. 

 

 
Figure 4. Diagnosis and treatment assistance decision-making flowchart 

4.3.1. Medical Knowledge Base 
The medical knowledge base is mainly to informationize 

the medical data issued by authoritative departments such as 
the constantly updated clinical diagnosis and treatment 
guidelines for diseases, Clinical pathway, medical textbooks, 
and the historical medical record data of the hospital, and then 
to organize the contents of the medical knowledge base 
structurally and elementarily through the combination of 
machine learning, data mining, and manual sorting and review. 

4.3.2. AI Inference Engine 
AI reasoning engine is mainly used to collect the data in the 

guide literature, clinical medical records and other original 
texts, use Big data processing, medical terminology 
annotation and other technologies to extract the key feature 
information related to diagnosis and treatment, form a 
Knowledge graph, and generate multiple medical logic 
reasoning models of different applications through neural 
network training, for auxiliary diagnosis and treatment, 



 

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intelligent guidance/triage Intelligent push for scenarios such 
as interpreting anomaly detection indicators. 

4.3.3. Matching Rules 
The system needs to design matching rules from multiple 

aspects, such as accuracy and utility, to ensure that patients 
can effectively seek medical treatment while also fully 
utilizing medical resources at different levels. Design 
corresponding symptom weighting rules, with doctors being 
skilled in prioritizing, pre-condition principles (such as 
pregnant women, young children, and other special 
populations), and rules for symptom severity, to provide 
patients with specialized doctors while ensuring efficient use 
of medical resources. 

4.4. Information Security Guarantee 
4.4.1. Data Privacy Security 

Data privacy refers to information that individuals, 
organizations, and other entities are unwilling to disclose or 
do not want to be known to the outside world. The unique 
nature of medical data requires greater attention to privacy 
protection. In the process of data collection, transmission, 
data open sharing, data analysis and other processes, privacy 
security protection strategies need to be adopted for data, such 
as fuzzification, Data anonymization and encryption of 
medical data. 

4.4.2. Data Trusted Control 
Data credibility control refers to the control of data 

authenticity and accuracy. The source of medical Big data is 
complex and diverse, and there are high requirements for data 
accuracy and authenticity. Data credibility control is 
particularly important. The reasons for the credibility problem 
of medical Big data can be understood from the following two 
perspectives: first, the original error, that is, when the data is 
generated, it is the wrong data; The second is process error, 
which refers to errors generated during the data dissemination 
process [12]. Therefore, in the process of data collection and 
processing, attention should be paid to the authenticity of data 
collection and the accuracy of data processing. 

4.4.3. Data Access Control 
Data access is a prerequisite for healthcare data 

applications, and the services provided by medical service 
platforms are based on data communication. The process of 
data access is inevitable. To ensure the security of data access, 
appropriate control measures need to be taken for data access 
and communication processes. In addition to privacy 
processing of data at the source, different permission controls 
need to be implemented according to different user roles to 
ensure the security of medical data while sharing. 

5. Conclusion 
Respond to the call of national policies and use Big data 

technology to effectively solve the imbalance of domestic 
medical resources. The development of a medical community 
information platform is beneficial for individuals to manage 
their own health. With the help of the medical community 
information platform, comprehensive tracking of personal 
electronic medical records can be achieved, and medical 
treatment is no longer limited by geographical location. Rural 
residents can also enjoy professional assistance from county-
level medical experts. On the other hand, it is conducive to 
assisting grassroots medical staff in making diagnosis and 
treatment decisions. Medical staff can use Big data 
technology to conduct scenario analysis on medical data 

associated with medical staff, so as to specify or select 
personalized diagnosis, treatment and care programs based on 
Big data analysis results. In addition, when encountering 
difficult and miscellaneous diseases, grassroots medical staff 
can timely seek help from county and city level medical 
experts, which can reduce the difficulty of patients' treatment 
to a certain extent. 

Through the study of the county medical community model 
and Big data technology, this paper has made a preliminary 
exploration of the construction of the medical community 
information platform, which provides a theoretical basis for 
the future medical business needs of grass-roots medical 
personnel, the implementation, development and application 
of the medical Big data platform, and also provides some 
theoretical reference for the development of related industries. 

Acknowledgments 
Supported by The Innovation Fund of Postgraduate, 

Sichuan University of Science & Engineering. 

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