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开发真实世界证据就绪数据集:临床医生参与的时机。

Developing Real-world Evidence-Ready Datasets: Time for Clinician Engagement.

机构信息

Department of Neurosurgery, Hermelin Brain Tumor Center, Henry Ford Hospital, 2799 West Grand Boulevard, Detroit, MI, 48202, USA.

Department of Public Health Sciences, Hermelin Brain Tumor Center, Henry Ford Hospital, 2799 West Grand Boulevard, Detroit, MI, 48202, USA.

出版信息

Curr Oncol Rep. 2020 Apr 16;22(5):45. doi: 10.1007/s11912-020-00904-z.

DOI:10.1007/s11912-020-00904-z
PMID:32297007
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8214868/
Abstract

PURPOSE OF REVIEW

Real-world data (RWD) applications in healthcare that support learning health systems and pragmatic clinical trials are gaining momentum, largely due to legislation supporting real-world evidence (RWE) for drug approvals. Clinical notes are thought to be the cornerstone of RWD applications, particularly for conditions with limited effective treatments, extrapolation of treatments from other conditions, or heterogenous disease biology and clinical phenotypes.

RECENT FINDINGS

Here, we discuss current issues in applying RWD captured at the point-of-care and provide a framework for clinicians to engage in RWD collection. To achieve clinically meaningful results, RWD must be reliably captured using consistent terminology in the description of our patients. RWD complements traditional clinical trials and research by informing the generalizability of results, generating new hypotheses, and creating a large data network for scientific discovery. Effective clinician engagement in the development of RWD applications is necessary for continued progress in the field.

摘要

目的综述

在支持学习型医疗系统和实用临床试验的医疗保健中,真实世界数据(RWD)的应用正在兴起,这主要得益于支持药物审批的真实世界证据(RWE)的立法。临床记录被认为是 RWD 应用的基石,特别是对于治疗效果有限、从其他疾病推断治疗方法,或疾病生物学和临床表型存在异质性的情况。

最近的发现

在这里,我们讨论了在医疗点捕获 RWD 时目前存在的问题,并为临床医生提供了一个参与 RWD 收集的框架。为了获得有临床意义的结果,必须使用一致的术语来可靠地描述我们的患者,从而可靠地捕获 RWD。RWD 通过告知结果的可推广性、产生新的假设以及为科学发现创建大型数据网络,补充了传统的临床试验和研究。为了在该领域取得持续进展,临床医生必须有效参与 RWD 应用的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5422/8214868/d0e51da7fe0b/nihms-1714263-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5422/8214868/d0e51da7fe0b/nihms-1714263-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5422/8214868/d0e51da7fe0b/nihms-1714263-f0001.jpg

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Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.自动评估脑胶质瘤负担:一种用于全自动容积和二维测量的深度学习算法。
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