The digital age is the age of big data where every piece of technology captures data available for later use. The McKinsey Global Institute (MGI) describes data generated in this way as digital “exhaust data,” data that are created as a by-product of other activities.
The rapid expansion in the use of EMRs and digitally-driven technology—MRI scanners, body sensors, automated lab tests—brings the era of big data to healthcare. MGI estimates that big data presents a $300 billion potential annual value to the U.S. healthcare system. The five broad areas to deliver that value are: 1) clinical operations, 2) payment/pricing, 3) R&D, 4) new business models, and 5) public health. Sub-areas include comparative effectiveness research (CER), clinical decision support, remote patient monitoring, health economics, and personalized medicine.
The four large data sources for healthcare include clinical, pharmaceutical, administrative, and consumer.
New analytic tools such as Semantic Web 3.0—linked data—offer ways for machines to analyze these data sets leveraging approaches impossible using standard relational databases and statistical methodologies. These new tools permit researchers to work around the barriers presented by data sets’ non-conformance to standards for data collection or storage.
Similar to the use of metadata, Semantic Web techniques allow the assignment of descriptors to each data point, providing a context and meaning to the data. This allows machines, applying powerful statistical techniques, to analyze the disparate data sets in ways not available to humans alone due to the data sets’ size and complexity.
Organizations that properly collect, analyze, and utilize big data will achieve a significant competitive advantage over those organizations that fail to recognize the opportunity big data presents.
Excerpts from: Big Data Drives Big Change. PSQH, January/February 2012