Big data is a term for data sets that are extremely large and complex that only a few short years ago were not capable of being processed with traditional data processing applications. Challenges in big data include the capture, search, sharing, storage, transfer, visualization, querying and privacy, among other concerns. Data sets are growing rapidly because there are increasingly more avenues for data including mobile devices, software logs, cameras, microphones, wireless networks, etc.
The massive amounts of data available or "big data" can be overwhelming. The kinds of big data analytics performed by Google, Amazon, Yahoo and many others can be beyond the scope of our understanding or capabilities. However the concept of data analytics scoped to appropriate level can be highly beneficial to an industry, or an organization.
Take healthcare for example. There are huge volumes of data available in healthcare. Pharmaceutical companies have been aggregating years of research and development data into medical databases. Insurance companies and providers have digitized their patient records, known as EMR (Electronic Medical Record). The federal government and other public stakeholders have been collecting stores of healthcare knowledge, including data from clinical trials and information on patients covered under public insurance programs like Medicare and Medicaid.
Healthcare costs have never been higher. The rate of healthcare inflation always exceeds the rest of the economy. Data analytics focused on healthcare with laser-like targeting can, and probably will dramatically change delivery of healthcare in the future. A good example that has been changing gradually is patient care management.
It has been known for years that a low dose of aspirin used by those at risk for coronary heart disease combined with early cholesterol screening and smoking cessation can significantly reduce mortality and the cost of care. These actions have been encouraging for some time but now big data enables faster identification of high-risk patients, more effective interventions and more timely and closer monitoring.
Before patients, physicians and healthcare organizations can see benefit from the use of healthcare data, there will need to be some major changes in the industry. For example, traditional medical management techniques often create direct conflicts between payers and providers, designing benefit plans with respect to what is and is not covered rather than what is or is not most effective. Further, patients, physicians or other medical practitioners must recognize the value of the data and be willing to act on its insights. If patients persist in sedentary lifestyles and overeating, they will not achieve benefit from research on exercise and proper diet, for example. And, physicians must study and understand outcome data on which to base their decisions rather than just on experience or instinct.
HIPPA regulations will continue to be a major concern. With growing databases and increased digital medical record usage, stakeholders across the industry must continually assess the opportunities for unapproved disclosure of private information.
Data analytics in healthcare have the potential for some significant improvements in quality and cost. All those involved including patients, providers and practitioners must be willing to build their capabilities and open their minds to a new view that big data, properly and securely utilized, can improve patient outcomes, both clinical and financial.