Suppr超能文献

替代研究资金以改善临床结果:预测和预防心脏性猝死模型。

Alternative research funding to improve clinical outcomes: model of prediction and prevention of sudden cardiac death.

机构信息

From the Division of Cardiology, Department of Medicine, University of Miami Miller School of Medicine, FL (R.J.M.); and Center for Health Sector Management and Policy, University of Miami, Coral Gables, FL (S.G.U.).

出版信息

Circ Arrhythm Electrophysiol. 2015 Apr;8(2):492-8. doi: 10.1161/CIRCEP.114.002580. Epub 2015 Feb 10.

Abstract

Although identification and management of cardiovascular risk markers have provided important population risk insights and public health benefits, individual risk prediction remains challenging. Using sudden cardiac death risk as a base case, the complex epidemiology of sudden cardiac death risk and the substantial new funding required to study individual risk are explored. Complex epidemiology derives from the multiple subgroups having different denominators and risk profiles, while funding limitations emerge from saturation of conventional sources of research funding without foreseeable opportunities for increases. A resolution to this problem would have to emerge from new sources of funding targeted to individual risk prediction. In this analysis, we explore the possibility of a research funding strategy that would offer business incentives to the insurance industries, while providing support for unresolved research goals. The model is developed for the case of sudden cardiac death risk, but the concept is applicable to other areas of the medical enterprise.

摘要

虽然心血管风险标志物的识别和管理为人群风险洞察和公共卫生带来了重要贡献,但个体风险预测仍然具有挑战性。本文以心源性猝死风险为例,探讨了心源性猝死风险的复杂流行病学以及研究个体风险所需的大量新资金。复杂的流行病学源于具有不同分母和风险特征的多个亚组,而资金限制则源于传统研究资金来源的饱和,而没有可预见的增加机会。要解决这个问题,必须从针对个体风险预测的新资金来源中寻找解决方案。在这项分析中,我们探讨了一种研究资金策略的可能性,该策略将为保险业提供商业激励,同时为未解决的研究目标提供支持。该模型是为心源性猝死风险开发的,但该概念适用于医疗领域的其他领域。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验