The Ultimate Potential of Big Data in Healthcare: Careful Adoption and Targeted Application
Perhaps not a day goes by where a new article touting the benefits of Big Data does not appear. The situation is further exacerbated with an actual dig data application: The internet – bloggers and opinions continue to proliferate and progress the hype.
Taken at the basic level, “Big Data” would appear to be the panacea for the future of all research productivity and results. But, Big Data itself will not cure all the problems in 21st century healthcare, and it should be carefully considered.
As experts discussed in the recent SAP Big Data Advisory Committee in Berlin, Germany, it is the careful adoption and targeted application of Big Data that will drive results. Unfortunately, many healthcare organizations are at the most basic levels of readiness.
Problems in utilizing Big Data run on a number of levels: First, the speed at which some applications can generate new data may overwhelm the ability of some systems to store that data. Second, many current infrastructures will not support the actual transmission of Big Data levels between departments or organizations. A number of additional, but clearly not minor, issues also challenge the adoption and success of healthcare Big Data, including security and privacy, data ownership, and the extreme regulation and oversight within the industry.
Real advances in leveraging Big Data will depend on the development of more effective ways to leverage currently disconnected data fields such as health records or clinical trial data. And most of the data used in healthcare is generated by people. While computers understand structured program language, human language may be emotional or nuanced.
Further, the industry is still developing better tools and applications to generate, capture, and analyze the massive quantities of data from genomics, sensor readings, and population tracking that are flooding healthcare.
But benefits in Big Data are real, and do exist. Healthcare organizations must move quickly but carefully in adoption models for Big Data analytics. Experts predict that it will take more than several years and a new generation of electronic medical records, patient reported outcomes systems, and activity based accounting systems before the gap can be closed in the data ecosystem to make predictive models and natural language processing widely valuable in the industry.
Healthcare organizations should not just jump at the hype – plot, plan and learn.