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强化心血管疾病预防的学习型卫生系统:利用大数据和数字解决方案的时机已到。

Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions.

机构信息

Department of Medicine, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Harvey Building, Suite 808, Baltimore, MD, 21287, USA.

Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

出版信息

Curr Atheroscler Rep. 2021 Mar 10;23(5):19. doi: 10.1007/s11883-021-00916-5.

Abstract

PURPOSE OF REVIEW

The past few decades have seen significant technologic innovation for the treatment and diagnosis of cardiovascular diseases. The subsequent growing complexity of modern medicine, however, is causing fundamental challenges in our healthcare system primarily in the spheres of patient involvement, data generation, and timely clinical implementation. The Institute of Medicine advocated for a learning health system (LHS) in which knowledge generation and patient care are inherently symbiotic. The purpose of this paper is to review how the advances in technology and big data have been used to further patient care and data generation and what future steps will need to occur to develop a LHS in cardiovascular disease.

RECENT FINDINGS

Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback. Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.

摘要

目的综述

过去几十年,心血管疾病的治疗和诊断技术取得了重大创新。然而,现代医学的复杂性不断增加,主要在患者参与、数据生成和及时临床实施等领域给我们的医疗体系带来了根本性挑战。美国国家医学研究院倡导建立学习型医疗体系(LHS),使知识生成和患者护理具有内在的共生关系。本文旨在回顾技术和大数据的进步如何用于进一步改善患者护理和数据生成,以及为在心血管疾病中建立 LHS 需要采取哪些未来步骤。

最近的发现

以患者为中心的护理已经从技术进步中取得进展,例如决策辅助工具。LHS 还可以通过增加和标准化患者报告信息的收集来纳入患者的偏好。此外,通过从多个来源开发大型互操作数据集,可以使用大数据分析来优化数据生成,从而实现实时数据反馈。建立 LHS 需要具有以患者为中心视角的创新技术解决方案,以促进数据生成和临床实践的共生关系。

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