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预防结算Prevention Settlement 可结算证据Billable Evidence PSM 因果归因Causal Attribution
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💊 药企解决方案Pharma Solution

带干预轨迹的真实世界数据直通NMPA RWEReal-World Data with Intervention Trajectories Direct to NMPA RWE

开放带干预轨迹的动态患者数据集，价值远超静态历史数据。PSM因果分析可直接复用于临床试验设计优化，产出符合NMPA要求的真实世界证据。Dynamic patient datasets with intervention trajectories — far more valuable than static historical data. PSM analysis directly applicable to clinical trial design, producing NMPA-compliant real-world evidence.

申请合作Get in Touch → RWE方法论指南RWE Methodology Guide
RWE
符合NMPA真实世界证据标准NMPA RWE compliant
PSM
因果分析可复用于临床设计Reusable for clinical trial design
4类
数据交付方式可选Data delivery modes
合规
PIPL · 伦理审批 · 审计日志PIPL · IRB · Audit Log
核心挑战Core Challenges

真实世界研究的三个数据困境Three Data Barriers in Real-World Research

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静态数据价值有限Limited Value of Static Data
传统医疗数据库多为静态历史记录，缺乏干预轨迹和动态随访数据，无法支持需要纵向数据的真实世界有效性研究。Traditional medical databases are mostly static historical records, lacking intervention trajectories needed for longitudinal real-world efficacy studies.
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因果证据难以获取Causal Evidence Hard to Obtain
监管机构越来越要求真实世界证据具有因果解释力，而非简单相关性。但在非RCT场景下获得可信的因果证据，方法论壁垒极高。Regulators increasingly require causal interpretability in real-world evidence, not just correlation. But generating credible causal evidence outside RCTs has high methodological barriers.
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数据合规壁垒Compliance Barriers
《个人信息保护法》《数据安全法》对医疗数据处理提出严格要求，药企难以直接获取和使用患者数据，合规成本极高。PIPL and Data Security Law impose strict requirements on healthcare data processing, making it difficult and costly for pharma to directly access patient data.
解决方案Solution

四类研究用途的数据与分析支持Data & Analysis Support for Four Research Use Cases

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靶点发现与生物标志物研究Target Discovery & Biomarker Research
开放带SHAP特征归因的真实世界患者数据，支持药企识别心脑血管疾病的关键风险因子和潜在靶点。动态数据集包含干预前后的生物标志物变化轨迹。Real-world patient data with SHAP feature attribution for identifying key cardiovascular risk factors and potential targets. Dynamic datasets include pre/post-intervention biomarker trajectories.
SHAP纵向轨迹Longitudinal生物标志物Biomarkers
02
患者分层建模Patient Stratification Modeling
基于16维临床特征和风险评分，支持药企对目标适应症人群进行精细化分层，识别最可能从特定干预获益的患者亚群，优化临床试验的入组标准。16-dimensional clinical features and risk scores for fine-grained stratification of target indication populations, identifying patient subgroups most likely to benefit from specific interventions.
16维特征16 Features患者亚群Subgroups入组优化Enrollment Optimization
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临床试验设计优化Clinical Trial Design Optimization
ReHealth Core的PSM因果分析框架可直接为临床试验提供历史对照数据和倾向评分模型，支持单臂试验设计的外部对照构建，降低试验样本量需求，缩短试验周期。PSM causal analysis framework directly provides historical controls and propensity score models for clinical trials, supporting external control construction for single-arm trials to reduce sample size and shorten timelines.
外部对照External Control样本量优化Sample Optimization历史对照Historical Control
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NMPA RWE注册申报支持NMPA RWE Regulatory Submission Support
生成符合NMPA 2020年真实世界证据指导原则的PSM因果报告，包含完整的方法说明、匹配质量评估、效应量报告和适用范围声明，可直接作为新适应症申报的真实世界证据支撑材料。NMPA 2020 RWE guideline-compliant PSM causal reports with full methodology, matching quality, effect size, and scope declaration — directly usable for new indication submissions.
NMPA RWE新适应症New Indication完整方法文档Full Documentation
数据交付方式Data Delivery Modes

四种灵活的数据获取模式Four Flexible Data Access Modes

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脱敏CSV数据集De-identified CSV Dataset
按适应症、样本量、随访时长定制的脱敏患者数据集，含干预轨迹和结局指标，适合内部建模和分析。Customized de-identified patient datasets by indication, sample size, and follow-up duration with intervention trajectories and outcome metrics.
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安全API接口Secure API Access
通过加密API实时查询患者风险评分和特征归因，适合需要将ReHealth数据集成到药企内部分析平台的场景。Encrypted API for real-time queries of patient risk scores and feature attribution, ideal for integrating ReHealth data into pharma internal analytics platforms.
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分析报告Analysis Report
由ReHealth Core研究团队完成PSM因果分析，输出符合监管要求的完整分析报告，适合直接用于申报材料。PSM causal analysis completed by the ReHealth Core research team, producing regulatory-compliant complete analysis reports directly usable for submissions.
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数据沙箱Data Sandbox
在ReHealth Core安全计算环境中分析数据，数据不出域，药企研究人员可在沙箱内运行自有分析代码，适合合规要求最严格的场景。Analyze data within ReHealth Core's secure computing environment. Data never leaves. Pharma researchers can run their own analysis code in the sandbox.
数据合规保障Data Compliance

六重合规保障，满足最严格要求Six-Layer Compliance for the Strictest Requirements

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全部数据脱敏处理Fully De-identified
所有数据在进入分析流程前完成本地脱敏处理。All data is de-identified locally before entering the analysis pipeline.
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符合个人信息保护法PIPL Compliant
满足《个人信息保护法》的全部数据处理合规要求。Compliant with all PIPL data processing requirements.
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伦理委员会审批IRB Approved
数据收集和使用均通过机构伦理委员会审查批准。Data collection and use approved by institutional review board.
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数据使用协议保护DUA Protected
所有数据授权均通过正式数据使用协议（DUA）约束。All data licensing governed by formal Data Use Agreements (DUA).
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禁止再识别条款Re-identification Prohibited
协议明确禁止任何形式的个人数据再识别尝试。Agreements explicitly prohibit any attempt to re-identify individual data.
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审计日志全程留存Full Audit Trail
所有数据访问和分析操作均有完整审计日志留存。Complete audit logs retained for all data access and analysis operations.
常见问题FAQ

药企合作常见问题Pharma Partnership FAQ

ReHealth Core的数据如何支持NMPA真实世界证据申报？How does ReHealth Core data support NMPA RWE submissions?
ReHealth Core生成的因果报告符合NMPA 2020年真实世界证据指导原则的方法论要求，包含PSM匹配质量评估、ATT效应量、置信区间和适用范围声明等完整要素，可直接作为新适应症申报的真实世界证据支撑材料。ReHealth Core generates causal reports compliant with NMPA 2020 RWE guidelines, including PSM matching quality, ATT, confidence intervals, and scope declaration — directly usable as supporting evidence for new indication submissions.
带干预轨迹的数据相比静态数据有什么优势？What advantages do intervention trajectory datasets have over static data?
静态历史数据只能支持横截面分析，无法观察干预前后的动态变化。带干预轨迹的纵向数据可以支持：①干预有效性的真实世界验证；②基于干预响应的患者亚群发现；③外部对照组构建（用于单臂试验设计）。Static data only supports cross-sectional analysis. Longitudinal data with intervention trajectories enables: ① Real-world efficacy validation; ② Patient subgroup discovery based on intervention response; ③ External control construction for single-arm trial designs.
目前数据库覆盖哪些适应症？Which indications does the database currently cover?
当前主要覆盖心脑血管相关适应症，包括高血压、高血脂、2型糖尿病（作为心血管风险因子）和综合心血管疾病风险。随着接入机构的扩展，覆盖范围将持续增加。Currently focused on cardiovascular-related indications including hypertension, dyslipidemia, type 2 diabetes (as cardiovascular risk factors), and composite cardiovascular disease risk. Coverage expands as more institutions connect.

准备好了解更多？Ready to Learn More?

联系我们，了解ReHealth Core如何为您的真实世界研究提供数据和因果分析支持。Contact us to learn how ReHealth Core can support your real-world research with data and causal analysis.

申请合作Get in Touch →阅读RWE指南Read RWE Guide
相关解决方案Related Solutions
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核心结论Key Takeaway

NMPA RWE路径已明确：带干预轨迹的真实世界数据可以支持医疗器械注册申报。关键不在于数据量，而在于干预轨迹的完整性和PSM归因的质量——这是从数据资产到注册资产的核心跨越。The NMPA RWE pathway is clear: real-world data with intervention trajectories can support medical device registration. The key is not data volume, but the completeness of intervention trajectories and quality of PSM attribution — this is the critical leap from data asset to registration asset.