McShea Michael, Holl Randy, Badawi Omar, Riker Richard R, Silfen Eric
Philips VISICU, Suite 1900, Baltimore, MD 21202, USA.
IEEE Eng Med Biol Mag. 2010 Mar-Apr;29(2):18-25. doi: 10.1109/MEMB.2009.935720.
As the volume of data that is electronically available promliferates, the health-care industry is identifying better ways to use this data for patient care. Ideally, these data are collected in real time, can support point-of-care clinical decisions, and, by providing instantaneous quality metrics, can create the opportunities to improve clinical practice as the patient is being cared for. The business-world technology supporting these activities is referred to as business intelligence, which offers competitive advantage, increased quality, and operational efficiencies. The health-care industry is plagued by many challenges that have made it a latecomer to business intelligence and data-mining technology, including delayed adoption of electronic medical records, poor integration between information systems, a lack of uniform technical standards, poor interoperability between complex devices, and the mandate to rigorously protect patient privacy. Efforts at developing a health care equivalent of business intelligence (which we will refer to as clinical intelligence) remains in its infancy. Until basic technology infrastructure and mature clinical applications are developed and implemented throughout the health-care system, data aggregation and interpretation cannot effectively progress. The need for this approach in health care is undisputed. As regional and national health information networks emerge, we need to develop cost-effective systems that reduce time and effort spent documenting health-care data while increasing the application of knowledge derived from that data.
随着电子可用数据量的激增,医疗保健行业正在寻找更好的方法来利用这些数据进行患者护理。理想情况下,这些数据是实时收集的,可以支持即时医疗临床决策,并且通过提供即时质量指标,能够在患者接受护理时创造改善临床实践的机会。支持这些活动的商业世界技术被称为商业智能,它提供竞争优势、提高质量和运营效率。医疗保健行业面临着诸多挑战,这使其在商业智能和数据挖掘技术方面成为后来者,这些挑战包括电子病历采用延迟、信息系统之间集成不佳、缺乏统一技术标准、复杂设备之间互操作性差以及严格保护患者隐私的要求。开发等同于商业智能的医疗保健技术(我们将其称为临床智能)的努力仍处于起步阶段。在整个医疗保健系统开发并实施基本技术基础设施和成熟的临床应用之前,数据聚合和解读无法有效推进。医疗保健领域对这种方法的需求是无可争议的。随着区域和国家卫生信息网络的出现,我们需要开发具有成本效益的系统,减少记录医疗保健数据所花费的时间和精力,同时增加从这些数据中获得的知识的应用。