传统随访困境重重

Traditional follow-up faces numerous challenges

　　数据管理杂乱易错

Data management is messy and prone to errors

　　在中型以上医院，每日随访数据众多，来源分散，有纸质病历、电话记录、科室表格等。像某三甲医院心内科，每月要对 500 多出院患者随访，医护手动录入 Excel，不仅耗时，还常出错，错误率达 10% - 15%，影响后续诊断与患者管理。

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.

　　医患沟通低效不畅

Inefficient and poor communication between doctors and patients

　　传统随访沟通工具多样，医护需在电话、短信、微信等间切换。社区卫生服务中心家庭医生团队随访慢性病患者时，常遇患者电话难接通，平均联系一位患者需拨打 3 - 5 次。而且因沟通平台多，信息易遗漏，影响随访效果。

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.

　　病情监测反馈滞后

Delayed feedback on disease monitoring

　　传统随访缺乏实时监测，医护依赖患者反馈，而患者描述可能不准确。如糖尿病、高血压患者，两次复查间病情变化难以及时掌握。同时，传统方式缺乏数据分析，无法为治疗调整提供有力依据。

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.

　　智能系统破局有方

Intelligent systems have a way to break through

　　数据智能整合管理

Intelligent integration and management of data

　　随访一体机智能系统能与医院 HIS、LIS、PACS 等系统对接，自动获取患者医疗数据。番禺区妇幼保健院引入相关系统后，患者出院时数据自动同步。系统还运用 OCR 和自然语言处理技术，将非结构化病历数据结构化，为患者建立动态电子健康档案，方便医护随时查阅，提升数据管理效率与准确性。

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.

　　多渠道沟通提效

Multi channel communication improves efficiency

　　智能随访系统整合电话、微信、APP 等沟通渠道，依患者偏好自动选择沟通方式。年轻患者优先微信推送随访问卷，老年患者安排电话随访并配智能语音导航。沟通内容支持图文、视频等，患者能拍照、语音反馈病情，提升沟通效率与信息传达准确性。

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.

　　实时监测精准分析

Real time monitoring and precise analysis

　　一体机搭配智能手环、便携式血糖仪等设备，实时监测患者生理参数并传输至系统。系统利用大数据、机器学习算法分析数据，识别异常并预警。如心脏病患者心率异常时系统及时通知医护。还能预测病情，像分析糖尿病患者数据，预测并发症风险，助力医护提前干预。

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.

　　康策医院智能随访系统以 AI 和大数据为核心，构建全周期患者管理体系。通过多渠道触达患者，降低护士 / 医生人工操作量 60%，危急值患者 30 分钟内响应预警。整合多系统数据构建患者画像，自动解析医患对话，生成随访分析报告，为临床决策提供数据支撑。

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.

　　传统随访在数据管理、医患沟通、病情监测上问题突出，随访一体机及智能系统凭借智能管理、高效沟通、实时监测分析等功能解决难题，推动医疗服务向精准、高效发展，守护患者健康 。

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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