系统构建的关键要素

一、多元数据来源与采集方式​

（1） Diversified data sources and collection methods

公共卫生数据采集系统的数据源广泛且繁杂。医疗机构作为重要的数据输出端，源源不断地贡献着门诊就诊记录、住院病历、疾病诊断信息等。例如，在日常门诊中，患者的症状描述、初步诊断结果以及所接受的治疗方案等信息，都能通过医院信息管理系统（HIS）无缝对接至公共卫生数据采集系统。社区卫生服务中心则侧重于收集居民的健康档案信息，包括个人基本信息、家族病史、日常健康行为（如吸烟、饮酒、运动习惯等）。通过定期的社区健康体检活动，还能获取居民的身体指标数据，如血压、血糖、血脂等。​

The data sources of the public health data collection system are extensive and complex. Medical institutions, as important data output terminals, continuously contribute outpatient visit records, inpatient medical records, disease diagnosis information, and so on. For example, in daily outpatient visits, patients' symptom descriptions, preliminary diagnosis results, and treatment plans received can be seamlessly integrated into the public health data collection system through the Hospital Information Management System (HIS). Community health service centers focus on collecting residents' health record information, including personal basic information, family medical history, and daily health behaviors (such as smoking, drinking, exercise habits, etc.). Through regular community health check ups, residents' physical indicators such as blood pressure, blood sugar, and blood lipids can also be obtained. ​

疾病预防控制中心在传染病监测方面发挥着核心作用，其采集的数据涵盖传染病的发病时间、地点、病例数、传播途径等关键信息。此外，环境监测部门提供的空气质量、水质状况、噪声水平等环境卫生数据，以及气象部门的气温、湿度、降水等气象数据，也都与公共卫生紧密相关，一并纳入采集范畴。​

The Center for Disease Control and Prevention plays a core role in infectious disease monitoring, collecting data covering key information such as the onset time, location, number of cases, and transmission routes of infectious diseases. In addition, environmental health data such as air quality, water quality, and noise levels provided by environmental monitoring departments, as well as meteorological data such as temperature, humidity, and precipitation from meteorological departments, are closely related to public health and are included in the collection scope. ​

为了高效获取这些数据，系统采用了多样化的采集方式。对于医疗机构和社区卫生服务中心的数据，多借助信息化系统实现自动化采集。以电子病历系统为例，患者就诊时产生的医疗数据会自动按照既定的数据接口标准，传输至公共卫生数据采集系统，极大地提高了数据采集的效率和准确性。在一些基层医疗机构，还配备了便携式健康检测设备，如智能血压计、血糖仪等，居民在进行健康检测时，数据可通过蓝牙等无线通信技术实时上传至系统。对于疾病预防控制中心的传染病数据，部分通过法定传染病报告系统进行上报，各级医疗机构一旦发现法定传染病病例，需在规定时间内通过该系统进行网络直报，确保疫情信息的及时性。同时，系统也支持人工录入方式，针对一些特殊情况或尚未完全实现信息化的数据，专业工作人员可手动将数据录入系统，保障数据的完整性。​

In order to efficiently obtain this data, the system adopts diverse collection methods. For medical institutions and community health service centers, automated data collection is often achieved through information systems. Taking the electronic medical record system as an example, the medical data generated by patients during their visits will be automatically transmitted to the public health data collection system according to the established data interface standards, greatly improving the efficiency and accuracy of data collection. In some grassroots medical institutions, portable health monitoring devices such as smart blood pressure monitors and blood glucose meters are also equipped. When residents undergo health checks, data can be uploaded to the system in real time through wireless communication technologies such as Bluetooth. For the infectious disease data of the Centers for Disease Control and Prevention, some are reported through the statutory infectious disease reporting system. Once medical institutions at all levels discover cases of statutory infectious diseases, they need to report them directly through the system within the prescribed time to ensure the timeliness of epidemic information. At the same time, the system also supports manual input. For some special situations or data that has not yet been fully informatized, professional staff can manually input the data into the system to ensure the integrity of the data. ​

二、强大的技术支撑体系​

（2） Powerful technical support system

先进的信息技术是公共卫生数据采集系统高效运行的基石。物联网技术在其中扮演着重要角色，通过在各类医疗设备、环境监测设备等终端嵌入传感器，实现数据的自动采集与实时传输。例如，在医院的重症监护病房，患者身上连接的各种生命体征监测设备，如心电监护仪、呼吸机等，可通过物联网技术将患者的心率、呼吸频率、血氧饱和度等数据实时传输至公共卫生数据采集系统，医生能随时掌握患者病情变化，同时也为公共卫生领域对重症疾病的研究提供了丰富的数据资源。在社区，智能垃圾桶可通过内置传感器监测垃圾量，从而间接反映社区居民的生活活跃度等健康相关信息，并上传至系统。​

Advanced information technology is the cornerstone of efficient operation of public health data collection systems. The Internet of Things technology plays an important role in it, by embedding sensors in various medical devices, environmental monitoring devices, and other terminals to achieve automatic data collection and real-time transmission. For example, in the intensive care unit of a hospital, various vital sign monitoring devices connected to patients, such as electrocardiogram monitors, ventilators, etc., can transmit real-time data on patients' heart rate, respiratory rate, blood oxygen saturation, etc. to the public health data collection system through IoT technology. Doctors can keep track of changes in patients' conditions at any time, and also provide rich data resources for research on critical illnesses in the public health field. In the community, smart trash cans can indirectly reflect health-related information such as the activity level of community residents by monitoring the amount of garbage through built-in sensors, and upload it to the system. ​

大数据与云计算技术则为海量数据的存储、管理和分析提供了有力保障。公共卫生数据采集系统每天会收集到来自各个渠道的大量数据，大数据技术能够对这些数据进行高效的清洗、整合和存储，去除重复、错误或不完整的数据，确保数据质量。云计算技术凭借其强大的计算能力，可快速对数据进行分析处理，挖掘数据背后隐藏的规律和趋势。例如，通过对多年来的传染病发病数据进行云计算分析，能够预测不同季节、不同地区传染病的发病风险，为疾病防控提前做好准备。此外，数据安全技术也是系统不可或缺的一部分，通过加密技术对传输和存储的数据进行加密处理，防止数据泄露；采用访问控制技术，严格限制不同用户对数据的访问权限，确保数据的安全性和隐私性。​

Big data and cloud computing technology provide powerful guarantees for the storage, management, and analysis of massive amounts of data. The public health data collection system collects a large amount of data from various channels every day. Big data technology can efficiently clean, integrate, and store this data, remove duplicate, erroneous, or incomplete data, and ensure data quality. Cloud computing technology, with its powerful computing capabilities, can quickly analyze and process data, and uncover hidden patterns and trends behind the data. For example, cloud computing analysis of infectious disease incidence data over the years can predict the risk of infectious disease occurrence in different seasons and regions, and prepare for disease prevention and control in advance. In addition, data security technology is also an indispensable part of the system, which encrypts the transmitted and stored data through encryption technology to prevent data leakage; Adopting access control technology to strictly limit the access permissions of different users to data, ensuring the security and privacy of the data. ​

三、标准化的数据管理规范​

（3） Standardized data management standards

为了确保采集到的数据能够在公共卫生领域得到有效应用，建立标准化的数据管理规范至关重要。首先是数据格式的标准化，规定了不同类型数据的存储格式和编码方式，使来自不同机构、不同设备的数据能够在系统中实现无缝对接。例如，对于患者的身份信息，统一采用身份证号码作为唯一标识，并规定其数据格式为 18 位数字字符。在疾病诊断编码方面，遵循国际通用的 ICD（国际疾病分类）编码标准，确保全球范围内疾病诊断信息的一致性和可比性。​

It is crucial to establish standardized data management standards to ensure that the collected data can be effectively applied in the field of public health. Firstly, the standardization of data formats specifies the storage formats and encoding methods for different types of data, enabling seamless integration of data from different institutions and devices in the system. For example, for the patient's identity information, the ID card number is uniformly used as the unique identification, and its data format is specified as 18 digit characters. In terms of disease diagnosis coding, we follow the internationally recognized ICD (International Classification of Diseases) coding standards to ensure consistency and comparability of disease diagnosis information worldwide. ​

数据质量控制也是数据管理规范的重要内容。建立了严格的数据审核机制，在数据采集环节，对录入的数据进行实时校验，如检查数据的逻辑合理性、数值范围是否正确等。对于通过自动化方式采集的数据，也会定期进行抽样检查，确保数据的准确性。同时，对数据的完整性进行评估，及时发现并补充缺失的数据。此外，还制定了数据更新机制，根据不同数据的特点和应用需求，确定合理的数据更新频率，如医疗机构的门诊就诊数据每天更新，传染病疫情数据则要求实时更新，以保证数据的时效性。

Data quality control is also an important aspect of data management standards. We have established a strict data review mechanism and conducted real-time verification of the entered data during the data collection process, such as checking the logical rationality of the data and whether the numerical range is correct. For data collected through automated methods, regular sampling checks are also conducted to ensure the accuracy of the data. At the same time, evaluate the integrity of the data, promptly identify and supplement missing data. In addition, a data update mechanism has been established to determine a reasonable frequency of data updates based on the characteristics and application needs of different data. For example, outpatient data from medical institutions is updated daily, while infectious disease epidemic data requires real-time updates to ensure data timeliness.

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