为什么普通医疗记录无法用于结算Why Ordinary Medical Records Can't Be Used for Settlement
传统医疗记录的逻辑是描述性的：记录发生了什么（诊断、处方、检查结果）。支付方据此核实服务是否发生，然后付款。Traditional medical records are descriptive: they document what happened (diagnoses, prescriptions, test results). Payers verify these records and pay accordingly.
预防干预的挑战在于：核心"服务结果"是没有发生疾病。支付方面对的问题不是"干预是否发生了"，而是"干预是否真的防止了疾病"——这是一个因果推断问题，不是记录核查问题。The challenge with preventive interventions is that the core "service outcome" is disease not occurring. The payer's question isn't "did the intervention happen?" but "did it actually prevent disease?" — a causal inference problem, not a record verification problem.
关键区别：Key Distinction:普通健康记录可以证明"参与者的血压在项目结束后下降了"。可结算证据需要证明"血压下降是因为干预，而不是因为参与者本来就是更注重健康的人"。这需要因果推断，不是描述性统计。Ordinary health records can prove "participants' blood pressure decreased after the program." Billable evidence must prove "the decrease was caused by the intervention, not because healthier people self-selected into the program." This requires causal inference, not descriptive statistics.
可结算证据的六个核心构成要素Six Core Elements of Billable Evidence
干预描述Intervention Description
干预类型（药物/生活方式/数字健康）、干预时长、参与人群基本特征、数据来源说明。Intervention type (medication/lifestyle/digital health), duration, population characteristics, data source description.
必需项Required基线特征对比Baseline Characteristics Comparison
匹配前干预组与对照组的基线差异，标准化均值差（SMD），证明两组可比性。Pre-matching baseline differences between treatment and control groups, SMD, proving group comparability.
必需项RequiredPSM匹配质量说明PSM Matching Quality
倾向评分模型说明、匹配方法（最近邻/核匹配等）、匹配后SMD改善、卡钳宽度。Propensity score model, matching method (nearest neighbor/kernel), post-matching SMD improvement, caliper width.
必需项Required平均处理效应（ATT）Average Treatment Effect (ATT)
干预组相对于配对对照组的平均因果效应量，附95%置信区间和p值。Average causal effect of the treatment group relative to matched controls, with 95% CI and p-value.
必需项Required健康结果指标Health Outcome Metrics
主要结局（如心血管风险评分）与次要结局（如血压、血脂、住院率）的变化量。Primary outcome (e.g., cardiovascular risk score) and secondary outcomes (blood pressure, lipids, hospitalization rate) changes.
必需项Required适用范围与局限性Scope and Limitations
证据适用的人群特征、随访时长限制、未测量混杂因素声明、AI生成内容免责说明。Applicable population characteristics, follow-up duration limits, unmeasured confounders declaration, AI-generated content disclaimer.
必需项Required可结算证据的三个等级Three Levels of Billable Evidence
群体趋势证据Group Trend Evidence
基于干预前后统计对比，未完全消除混杂因素。适用于企业健康管理年度汇报、内部绩效评估，不建议直接用于保险结算。Pre/post statistical comparison without fully eliminating confounders. Suitable for annual reports and internal performance, not recommended for insurance settlement.
PSM因果归因证据PSM Causal Attribution Evidence
通过倾向评分匹配消除可观测混杂因素，输出ATT及置信区间。适用于商业保险费率谈判、企业向保险方申请保费优惠、医院向医保部门的预防项目申报。PSM eliminates observable confounders, outputs ATT and CI. Suitable for commercial insurance rate negotiations, employer premium discount applications, hospital prevention program submissions.
多中心联邦RWE证据Multi-Center Federated RWE Evidence
跨多家机构、基于联邦学习的大样本PSM因果报告，符合NMPA RWE最高等级要求。适用于药企注册申报、医保战略合作谈判、行业标准制定参考。Multi-institution federated learning large-sample PSM reports meeting NMPA RWE highest standards. Suitable for pharma regulatory submissions and public insurance strategic negotiations.