Kohn M S, Sun J, Knoop S, Shabo A, Carmeli B, Sow D, Syed-Mahmood T, Rapp W
Martin S. Kohn, MD, MS, FACEP, FACPE, Chief Medical Scientist, Jointly Health, Big Data Analytics for Remote Patient Monitoring, 120 Vantis, #570, Aliso Viejo, CA, 92656, USA, E-mail:
Yearb Med Inform. 2014 Aug 15;9(1):154-62. doi: 10.15265/IY-2014-0002.
This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making.
Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given.
There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data.
Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.
本调查探讨了IBM开发的大数据和健康分析在通过增强循证决策来支持医疗保健转型方面的作用。
描述了医疗保健中的一些问题及变革策略。有人认为变革需要更好的决策,而这反过来又需要更好地利用多种医疗保健信息。介绍了应对各项信息挑战的分析资源。给出了每种资源作用的示例。
有强大的分析工具利用医疗保健中的各类大数据来帮助临床医生做出更具个性化的循证决策。此类资源能够提取相关信息并提供见解,供临床医生用于做出循证决策。有早期迹象表明这些资源具有临床价值。与所有分析工具一样,它们受到数据量和质量的限制。
大数据是医疗保健未来发展中不可避免的一部分。迫切需要管理和利用大数据来做出更好的决策,以支持医疗保健向未来个性化、循证模式转变。认知计算资源对于应对在医疗保健中应用大数据所面临的挑战必不可少。此类工具已经并正在开发之中。分析资源本身并不推动医疗保健转型,而是为其提供支持。