Healthcare database analyses (claims, electronic health records) have been identified by various regulatory initiatives, including the 21st Century Cures Act and Prescription Drug User Fee Act (“PDUFA”), as useful supplements to randomized clinical trials to generate evidence on the effectiveness, harm, and value of medical products in routine care. Specific applications include accelerated drug approval pathways and secondary indications for approved medical products. Such real-world data (“RWD”) analyses reflect how medical products impact health outside a highly controlled research environment. A constant stream of data from the routine operation of modern healthcare systems that can be analyzed in rapid cycles enables incremental evidence development for regulatory decision-making.Key evidentiary needs by regulators include 1) monitoring of medication performance in routine care, including the effectiveness, safety and value; 2) identifying new patient strata in which a drug may have added value or unacceptable harms; and 3) monitoring targeted utilization. Four broad requirements have been proposed to enable successful regulatory decision-making based on healthcare database analyses (collectively, “MVET”): Meaningful evidence that provides relevant and context-informed evidence sufficient for interpretation, drawing conclusions, and making decisions; valid evidence that meets scientific and technical quality standards to allow causal interpretations; expedited evidence that provides incremental evidence that is synchronized with the decision-making process; and transparent evidence that is audible, reproducible, robust, and ultimately trusted by decision-makers.Evidence generation systems that satisfy MVET requirements to a high degree will contribute to effective regulatory decision-making. Rapid-cycle analytics of healthcare databases is maturing at a time when regulatory overhaul increasingly demands such evidence. Governance, regulations, and data quality are catching up as the utility of this resource is demonstrated in multiple contexts.

中文翻译：

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适合监管决策的真实数据分析

医疗保健数据库分析（索赔、电子健康记录）已被各种监管举措确定，包括 21英石Century Cures Act 和 Prescription Drug User Fee Act (“PDUFA”)，作为随机临床试验的有用补充，以生成有关医疗产品在常规护理中的有效性、危害和价值的证据。具体应用包括加速药物批准途径和已批准医疗产品的次要适应症。这种真实世界数据（“RWD”）分析反映了医疗产品如何在高度控制的研究环境之外影响健康。来自现代医疗保健系统日常运营的源源不断的数据流可以在快速周期中进行分析，从而为监管决策制定增量证据提供支持。监管机构的主要证据需求包括 1) 监测常规护理中的药物性能，包括有效性，安全和价值；2) 识别药物可能具有附加值或不可接受的危害的新患者阶层；3) 监控目标利用率。提出了四项广泛的要求，以实现基于医疗保健数据库分析（统称为“MVET”）的成功监管决策：有意义的提供足以解释、得出结论和做出决定的相关和背景信息证据的证据；有效的符合科学和技术质量标准的证据，可以进行因果解释；加急提供与决策过程同步的增量证据的证据；和透明可听、可重复、可靠并最终受到决策者信任的证据。高度满足 MVET 要求的证据生成系统将有助于有效的监管决策。在监管改革越来越需要此类证据的时候，医疗保健数据库的快速周期分析正在成熟。随着该资源的效用在多种情况下得到证明，治理、法规和数据质量正在迎头赶上。