一、多元數據來源與采集方式?

（1） Diversified data sources and collection methods

公共衛生數據采集系統的數據源廣泛且繁雜。醫療機構作為重要的數據輸出端，源源不斷地貢獻著門診就診記錄、住院病歷、疾病診斷信息等。例如，在日常門診中，患者的癥狀描述、初步診斷結果以及所接受的治療方案等信息，都能通過醫院信息管理系統（HIS）無縫對接至公共衛生數據采集系統。社區衛生服務中心則側重于收集居民的健康檔案信息，包括個人基本信息、家族病史、日常健康行為（如吸煙、飲酒、運動習慣等）。通過定期的社區健康體檢活動，還能獲取居民的身體指標數據，如血壓、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. ?

疾病預防控制中心在傳染病監測方面發揮著核心作用，其采集的數據涵蓋傳染病的發病時間、地點、病例數、傳播途徑等關鍵信息。此外，環境監測部門提供的空氣質量、水質狀況、噪聲水平等環境衛生數據，以及氣象部門的氣溫、濕度、降水等氣象數據，也都與公共衛生緊密相關，一并納入采集范疇。?

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. ?

為了高效獲取這些數據，系統采用了多樣化的采集方式。對于醫療機構和社區衛生服務中心的數據，多借助信息化系統實現自動化采集。以電子病歷系統為例，患者就診時產生的醫療數據會自動按照既定的數據接口標準，傳輸至公共衛生數據采集系統，極大地提高了數據采集的效率和準確性。在一些基層醫療機構，還配備了便攜式健康檢測設備，如智能血壓計、血糖儀等，居民在進行健康檢測時，數據可通過藍牙等無線通信技術實時上傳至系統。對于疾病預防控制中心的傳染病數據，部分通過法定傳染病報告系統進行上報，各級醫療機構一旦發現法定傳染病病例，需在規定時間內通過該系統進行網絡直報，確保疫情信息的及時性。同時，系統也支持人工錄入方式，針對一些特殊情況或尚未完全實現信息化的數據，專業工作人員可手動將數據錄入系統，保障數據的完整性。?

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、強大的技術支撐體系?

（2） Powerful technical support system

先進的信息技術是公共衛生數據采集系統高效運行的基石。物聯網技術在其中扮演著重要角色，通過在各類醫療設備、環境監測設備等終端嵌入傳感器，實現數據的自動采集與實時傳輸。例如，在醫院的重癥監護病房，患者身上連接的各種生命體征監測設備，如心電監護儀、呼吸機等，可通過物聯網技術將患者的心率、呼吸頻率、血氧飽和度等數據實時傳輸至公共衛生數據采集系統，醫生能隨時掌握患者病情變化，同時也為公共衛生領域對重癥疾病的研究提供了豐富的數據資源。在社區，智能垃圾桶可通過內置傳感器監測垃圾量，從而間接反映社區居民的生活活躍度等健康相關信息，并上傳至系統。?

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. ?

大數據與云計算技術則為海量數據的存儲、管理和分析提供了有力保障。公共衛生數據采集系統每天會收集到來自各個渠道的大量數據，大數據技術能夠對這些數據進行高效的清洗、整合和存儲，去除重復、錯誤或不完整的數據，確保數據質量。云計算技術憑借其強大的計算能力，可快速對數據進行分析處理，挖掘數據背后隱藏的規律和趨勢。例如，通過對多年來的傳染病發病數據進行云計算分析，能夠預測不同季節、不同地區傳染病的發病風險，為疾病防控提前做好準備。此外，數據安全技術也是系統不可或缺的一部分，通過加密技術對傳輸和存儲的數據進行加密處理，防止數據泄露；采用訪問控制技術，嚴格限制不同用戶對數據的訪問權限，確保數據的安全性和隱私性。?

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、標準化的數據管理規范?

（3） Standardized data management standards

為了確保采集到的數據能夠在公共衛生領域得到有效應用，建立標準化的數據管理規范至關重要。首先是數據格式的標準化，規定了不同類型數據的存儲格式和編碼方式，使來自不同機構、不同設備的數據能夠在系統中實現無縫對接。例如，對于患者的身份信息，統一采用身份證號碼作為唯一標識，并規定其數據格式為 18 位數字字符。在疾病診斷編碼方面，遵循國際通用的 ICD（國際疾病分類）編碼標準，確保全球范圍內疾病診斷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. ?

數據質量控制也是數據管理規范的重要內容。建立了嚴格的數據審核機制，在數據采集環節，對錄入的數據進行實時校驗，如檢查數據的邏輯合理性、數值范圍是否正確等。對于通過自動化方式采集的數據，也會定期進行抽樣檢查，確保數據的準確性。同時，對數據的完整性進行評估，及時發現并補充缺失的數據。此外，還制定了數據更新機制，根據不同數據的特點和應用需求，確定合理的數據更新頻率，如醫療機構的門診就診數據每天更新，傳染病疫情數據則要求實時更新，以保證數據的時效性。

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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