Built on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individually, each may differ in important ways from an RCT cohort, and the physician will wonder each time if following EBM will provide best guidance for this unique patient. In an effort to overcome this weakness, and promote higher quality care through a more personalized approach, a new framework has been proposed: Medicine-Based Evidence (MBE). In this approach, big data and deep learning techniques are embraced to interrogate treatment responses among patients in real-world clinical practice. Such statistical models are then integrated with mechanistic disease models to construct a “digital twin,” which serves as the real-time digital counterpart of a patient. MBE is thereby capable of dynamically modeling the effects of various treatment decisions in the context of an individual's specific characteristics. In this article, we discuss how MBE could benefit patients with congenital heart disease, a field where RCTs are difficult to conduct and often fail to provide definitive solutions because of a small number of subjects, their clinical complexity, and heterogeneity. We will also highlight the challenges that must be addressed before MBE can be embraced in clinical practice and its full potential can be realized.

建立在随机对照试验 (RCT) 的基础上，循证医学 (EBM) 在优化同类患者队列（如参与 RCT 的患者）的结果时处于最佳状态。它的弱点是无法解决临床困境：患者单独出现，每个人都可能在重要方面与 RCT 队列不同，而且医生每次都会想知道遵循 EBM 是否会为这个独特的患者提供最佳指导。为了克服这一弱点，并通过更加个性化的方法促进更高质量的护理，提出了一个新框架：基于医学的证据 (MBE)。在这种方法中，大数据和深度学习技术被用于在现实世界的临床实践中询问患者的治疗反应。然后将此类统计模型与机械疾病模型集成以构建“数字双胞胎”，作为患者的实时数字对应物。因此，MBE 能够在个人特定特征的背景下动态模拟各种治疗决策的影响。在本文中，我们将讨论 MBE 如何使先天性心脏病患者受益，这是一个 RCT 难以进行的领域，并且由于受试者数量少、临床复杂性和异质性，往往无法提供明确的解决方案。我们还将强调在临床实践中采用 MBE 并充分发挥其潜力之前必须解决的挑战。因此，MBE 能够在个人特定特征的背景下动态模拟各种治疗决策的影响。在本文中，我们将讨论 MBE 如何使先天性心脏病患者受益，这是一个 RCT 难以进行的领域，并且由于受试者数量少、临床复杂性和异质性，往往无法提供明确的解决方案。我们还将强调在临床实践中采用 MBE 并充分发挥其潜力之前必须解决的挑战。因此，MBE 能够在个人特定特征的背景下动态模拟各种治疗决策的影响。在本文中，我们将讨论 MBE 如何使先天性心脏病患者受益，这是一个 RCT 难以进行的领域，并且由于受试者数量少、临床复杂性和异质性，往往无法提供明确的解决方案。我们还将强调在临床实践中采用 MBE 并充分发挥其潜力之前必须解决的挑战。由于受试者数量少、临床复杂性和异质性，RCT 难以实施且经常无法提供明确解决方案的领域。我们还将强调在临床实践中采用 MBE 并充分发挥其潜力之前必须解决的挑战。由于受试者数量少、临床复杂性和异质性，RCT 难以进行且经常无法提供明确解决方案的领域。我们还将强调在临床实践中采用 MBE 并充分发挥其潜力之前必须解决的挑战。