🏥 医院解决方案Hospital Solution

让预防科室产生可结算的临床价值Making Prevention Billable for Hospitals

提前1-3年预测心脑血管风险，辅助医生在疾病窗口期主动干预，用PSM因果证据向支付方证明预防的真实价值。HIS无缝对接，两周内接入。Predict cardiovascular risk 1-3 years early, enable proactive intervention, and prove prevention value to payers with PSM causal evidence. HIS integration in under 2 weeks.

申请合作Get in Touch → 了解方法论Read Insights

0.839

心血管预测 AUCCardiovascular AUC

1-3年

提前识别高危窗口Early risk identification

临床特征维度Clinical features

<2周

HIS平均接入周期HIS integration time

核心挑战Core Challenges

预防医学的三个结构性困境Three Structural Barriers in Preventive Medicine

⏰

发现太晚Too Late to Detect

传统体检一年一次，心脑血管事件往往在"正常"体检后数月内发生。医院缺乏对高危患者的持续动态监测能力。Traditional checkups happen once a year. Cardiovascular events often occur within months of a "normal" exam. Hospitals lack continuous monitoring for high-risk patients.

📊

效果无法量化Unquantifiable Outcomes

预防科室做了大量工作，但无法向院方、医保和商业保险证明干预真实降低了多少风险——没有因果证据，预防科室永远是成本部门。Prevention departments work hard but cannot prove intervention value to management, insurers, or payers. Without causal evidence, prevention stays a cost center.

🔗

数据孤岛Data Silos

HIS、可穿戴设备、院外随访数据割裂，无法形成患者完整的健康轨迹，风险预测模型缺乏动态输入。HIS, wearables, and outpatient follow-up data are fragmented. No complete patient health trajectory means no dynamic risk prediction input.

ReHealth Core 解决方案ReHealth Core Solution

四步打通预防医学完整闭环Four Steps to Close the Prevention Loop

多维心脑血管风险预测Multi-Dimensional Cardiovascular Risk Prediction

基于16个临床特征（血压、血脂、血糖、BMI、生活行为习惯、家族史等），AUC 0.839的风险模型实时输出风险评分和SHAP特征重要性分析，支持实时推理（<200ms响应）。风险信号直接推送至医生工作站。16 clinical features, AUC 0.839 risk model with real-time SHAP attribution. Risk signals push directly to physician workstations with <200ms response.

AUC 0.83916维特征 FeaturesSHAP归因 Attribution<200ms

个性化干预方案生成Personalized Intervention Generation

基于患者风险因子自动生成针对性干预建议，覆盖药物、生活方式、数字健康工具三类干预路径，并记录完整干预轨迹。干预轨迹是后续PSM因果归因的原始数据基础。Auto-generate targeted interventions covering medication, lifestyle, and digital health tools. Full trajectory logging feeds directly into PSM attribution.

药物干预Medication生活方式Lifestyle轨迹记录Tracking

PSM因果归因分析PSM Causal Attribution

通过倾向评分匹配（PSM）消除选择偏倚，量化干预的真实因果效应，输出ATT（平均处理效应）及95%置信区间。方法论符合NMPA真实世界证据（RWE）指南。PSM eliminates selection bias and quantifies true causal effect (ATT) with 95% CI. Methodology complies with NMPA Real-World Evidence guidelines.

PSMATT95% CINMPA RWE

可结算证据报告生成Billable Evidence Report Generation

生成标准化可结算证据报告（PDF + 结构化JSON），包含方法说明、匹配质量评估、效应量报告和适用范围声明，可直接提交给医保部门、商业保险公司作为预防项目申报依据。Generate standardized billable evidence reports (PDF + JSON) with full methodology, matching quality, effect size, and scope declaration — directly submittable to insurers and regulators.

PDF + JSON医保申报Insurance Submission标准化Standardized

接入流程Integration Process

两周内完成HIS接入HIS Integration in Under 2 Weeks

📋

需求对接Requirements

确认数据字段和接入方式Confirm data fields and integration method

🔌

API接入API Integration

标准RESTful，支持HIS/HL7Standard RESTful, HIS/HL7 support

✅

测试验证Testing

沙箱环境，数据质量评估Sandbox environment, data quality assessment

🚀

正式上线Go Live

风险信号推送至工作站Risk signals push to workstation

📊

归因结算Attribution

定期生成PSM因果证据报告Regular PSM causal evidence reports

价值对比Value Comparison

ReHealth Core vs. 传统预防体系ReHealth Core vs. Traditional Prevention

维度Dimension	传统体检/预防科室Traditional Prevention	ReHealth Core
风险监测频率Risk Monitoring	每年1次Once a year	实时连续监测Real-time continuous
高危识别窗口Early Detection	事件发生后After the event	提前1-3年1-3 years early
干预效果量化Effect Quantification	无法量化Cannot quantify	PSM因果证据PSM causal evidence
支付方可接受证据Payer-Accepted Evidence	无标准化证据No standard evidence	可结算证据报告Billable evidence report
HIS对接HIS Integration	数据孤岛Data silos	标准API，<2周接入Standard API, <2 weeks
数据安全合规Data Compliance	依赖院内措施Relies on internal measures	联邦学习，数据不出域Federated learning, data on-premise

常见问题FAQ

医院合作常见问题Hospital Partnership FAQ

ReHealth Core 如何与医院现有HIS系统对接？How does ReHealth Core integrate with existing HIS systems?

提供标准 RESTful API，支持与主流HIS系统对接。平均接入周期不超过2周，无需医院自建数据团队。支持JSON/HL7 FHIR数据格式，可实时推送风险信号至医生工作站。We provide standard RESTful APIs compatible with major HIS systems. Average integration under 2 weeks, no in-house data team needed. Supports JSON/HL7 FHIR with real-time risk alerts to physician workstations.

患者数据如何保障安全？How is patient data kept secure?

采用医疗级联邦学习架构，患者原始数据始终留存于医院本地服务器，仅上传经差分隐私处理的模型梯度。满足《个人信息保护法》《数据安全法》《网络安全法》三法合规要求。Medical-grade federated learning: raw patient data stays on-premise at all times. Only differentially-private model gradients are uploaded. Compliant with PIPL, Data Security Law, and Cybersecurity Law.

医院如何用PSM证据向医保申报预防项目？How can hospitals use PSM evidence for insurance submissions?

ReHealth Core 生成符合NMPA真实世界证据（RWE）指南标准的PSM因果报告，包含ATT效应量、置信区间、匹配质量评估等完整要素，可直接作为医保谈判和预防项目申报的证据支撑材料。ReHealth Core generates PSM causal reports compliant with NMPA RWE guidelines, including ATT, confidence intervals, and matching quality assessment — directly usable for insurance negotiations and prevention program submissions.

准备好了解更多？Ready to Learn More?

联系我们，了解ReHealth Core如何帮助您的医院打通预防结算闭环。Contact us to learn how ReHealth Core can close the prevention-to-settlement loop for your hospital.

申请合作Get in Touch → 阅读InsightsRead Insights