傳統隨訪困境重重

Traditional follow-up faces numerous challenges

　　數據管理雜亂易錯

Data management is messy and prone to errors

　　在中型以上醫院，每日隨訪數據眾多，來源分散，有紙質病歷、電話記錄、科室表格等。像某三甲醫院心內科，每月要對 500 多出院患者隨訪，醫護手動錄入 Excel，不僅耗時，還常出錯，錯誤率達 10% - 15%，影響后續診斷與患者管理。

In medium-sized and above hospitals, there are numerous daily follow-up data with scattered sources, including paper medical records, telephone records, department forms, etc. For example, in a certain tertiary hospital's cardiology department, more than 500 discharged patients need to be followed up every month. Medical staff manually input the data into Excel, which is not only time-consuming but also prone to errors, with an error rate of 10% -15%, affecting subsequent diagnosis and patient management.

　　醫患溝通低效不暢

Inefficient and poor communication between doctors and patients

　　傳統隨訪溝通工具多樣，醫護需在電話、短信、微信等間切換。社區衛生服務中心家庭醫生團隊隨訪慢性病患者時，常遇患者電話難接通，平均聯系一位患者需撥打 3 - 5 次。而且因溝通平臺多，信息易遺漏，影響隨訪效果。

Traditional follow-up communication tools are diverse, and medical staff need to switch between phone calls, text messages, WeChat, and other channels. When the family doctor team of the community health service center follows up with chronic disease patients, they often encounter difficulties in connecting the patient's phone, with an average of 3-5 calls required to contact each patient. Moreover, due to the multiple communication platforms, information is prone to omission, which affects the effectiveness of follow-up.

　　病情監測反饋滯后

Delayed feedback on disease monitoring

　　傳統隨訪缺乏實時監測，醫護依賴患者反饋，而患者描述可能不準確。如糖尿病、高血壓患者，兩次復查間病情變化難以及時掌握。同時，傳統方式缺乏數據分析，無法為治療調整提供有力依據。

Traditional follow-up lacks real-time monitoring, medical staff rely on patient feedback, and patient descriptions may be inaccurate. For example, in patients with diabetes and hypertension, it is difficult to grasp the changes of the condition in time between the two reexaminations. Meanwhile, traditional methods lack data analysis and cannot provide strong evidence for treatment adjustments.

　　智能系統破局有方

Intelligent systems have a way to break through

　　數據智能整合管理

Intelligent integration and management of data

　　隨訪一體機智能系統能與醫院 HIS、LIS、PACS 等系統對接，自動獲取患者醫療數據。番禺區婦幼保健院引入相關系統后，患者出院時數據自動同步。系統還運用 OCR 和自然語言處理技術，將非結構化病歷數據結構化，為患者建立動態電子健康檔案，方便醫護隨時查閱，提升數據管理效率與準確性。

The intelligent follow-up system can be integrated with hospital HIS, LIS, PACS and other systems to automatically obtain patient medical data. After the introduction of relevant systems, Panyu Maternal and Child Health Hospital automatically synchronizes patient data upon discharge. The system also utilizes OCR and natural language processing technology to structure unstructured medical record data, establishing dynamic electronic health records for patients, facilitating medical staff to access them at any time, and improving data management efficiency and accuracy.

　　多渠道溝通提效

Multi channel communication improves efficiency

　　智能隨訪系統整合電話、微信、APP 等溝通渠道，依患者偏好自動選擇溝通方式。年輕患者優先微信推送隨訪問卷，老年患者安排電話隨訪并配智能語音導航。溝通內容支持圖文、視頻等，患者能拍照、語音反饋病情，提升溝通效率與信息傳達準確性。

The intelligent follow-up system integrates communication channels such as phone, WeChat, and APP, and automatically selects communication methods based on patient preferences. Young patients are given priority in receiving follow-up questionnaires through WeChat, while elderly patients are arranged for telephone follow-up with intelligent voice navigation. Communication content supports text, images, videos, etc. Patients can take photos and provide voice feedback on their condition, improving communication efficiency and accuracy of information dissemination.

　　實時監測精準分析

Real time monitoring and precise analysis

　　一體機搭配智能手環、便攜式血糖儀等設備，實時監測患者生理參數并傳輸至系統。系統利用大數據、機器學習算法分析數據，識別異常并預警。如心臟病患者心率異常時系統及時通知醫護。還能預測病情，像分析糖尿病患者數據，預測并發癥風險，助力醫護提前干預。

The all-in-one machine is equipped with smart wristbands, portable blood glucose meters and other devices to monitor patients' physiological parameters in real time and transmit them to the system. The system utilizes big data and machine learning algorithms to analyze data, identify anomalies, and issue warnings. If the heart rate of a patient with heart disease is abnormal, the system will promptly notify the medical staff. It can also predict the condition, such as analyzing the data of diabetes patients, predicting the risk of complications, and helping doctors and nurses to intervene in advance.

　　康策醫院智能隨訪系統以 AI 和大數據為核心，構建全周期患者管理體系。通過多渠道觸達患者，降低護士 / 醫生人工操作量 60%，危急值患者 30 分鐘內響應預警。整合多系統數據構建患者畫像，自動解析醫患對話，生成隨訪分析報告，為臨床決策提供數據支撐。

Kangce Hospital's intelligent follow-up system is based on AI and big data, building a full cycle patient management system. By reaching patients through multiple channels, the workload of nurses/doctors can be reduced by 60%, and critical patients can respond to warnings within 30 minutes. Integrating multiple system data to construct patient profiles, automatically parsing doctor-patient conversations, generating follow-up analysis reports, and providing data support for clinical decision-making.

　　傳統隨訪在數據管理、醫患溝通、病情監測上問題突出，隨訪一體機及智能系統憑借智能管理、高效溝通、實時監測分析等功能解決難題，推動醫療服務向精準、高效發展，守護患者健康 。

Traditional follow-up has prominent problems in data management, doctor-patient communication, and disease monitoring. The follow-up all-in-one machine and intelligent system solve these problems with intelligent management, efficient communication, real-time monitoring and analysis, and promote the development of medical services towards precision and efficiency, safeguarding patient health.

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