By Guy RamsayNov 30
The rapid growth in the use of EMRs and the increased use of digitally driven technology have resulted in the production of large amounts of highly valuable data, bringing the era of big data to healthcare. But what exactly is big data? Essentially, big data is used to help describe large datasets—sizes currently...
The rapid growth in the use of EMRs and the increased use of digitally driven technology have resulted in the production of large amounts of highly valuable data, bringing the era of big data to healthcare.
But what exactly is big data? Essentially, big data is used to help describe large datasets—sizes currently range from a few dozen terabytes to many petabytes of data in a single dataset—that have three features in common: volume, velocity and variety.
- Volume—highlights the exponential growth in the volume of data that is being managed. Data is not just in the form of text data, but also in the form of videos, audio and large image files.
- Velocity—data is streaming in at unprecedented speed and must be treated in a timely manner.
- Variety—data comes in different types of formats: structured, numeric data in traditional databases; unstructured text documents, email, video, audio and information created from line-of-business applications.
Indeed, new technologies make it possible to capture and gather vast amounts of information about individuals over large periods of time, yet these data remain poorly exploited.
Consequences of Big Data for Medical Equipment Suppliers
In light of its volume, velocity and variety, big data creates two major problems for healthcare organizations. First, it is staggeringly complex and increases information technology (IT) costs. The IT departments of most healthcare organizations have expanded rapidly in recent years in response to the increasing complexities of the IT systems used to manage the growing data and communication system needs.
Today’s extremely variable and real-time datasets require new tools and methods, such as powerful processors, software and algorithms, going beyond traditional database systems designed to handle mainly low-variety, small scale and static datasets, often manually.
For medical device developers, big data analytics has provided tools to accumulate, manage, analyze and assimilate large volumes of disparate, structured and unstructured data produced from multiple sources, such as patient medical records, body-worn medical sensors, clinical devices, diagnostics and imaging, to bring new insight to complex medical questions.
Data Protection and Privacy in a Virtual Universe
Big data may be revolutionizing healthcare, but just how secure is our medical data? Indeed, security and privacy issues are amplified by the velocity, volume and variety of big data, such as large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high-volume inter-cloud migration.
As big data is a relatively new concept, and a list of guidelines that is widely recognized by the security community does not yet exist. Nevertheless, there are several general security measures that can be applied to big data to ensure its confidentiality and integrity. Guidelines must ensure that adequate protection mechanisms are in place for cloud storage, it must encourage policies that allow access to authorized users only, and it must adequately protect both the raw data and the outcome by anonymizing or encrypting the data and effectively protecting data in transit.
Big Data Companies on the Rise
Although still in its infancy, the explosion of big data has witnessed the emergence of big data and analytics companies that are providing innovative healthcare services. Moreover, the current market trends predict a huge expansion in data analytics services with a number of companies expanding as big data analytics service providers.
Two of the more prominent big data analytics companies are Oracle and IBM.
Oracle employs Oracle’s Healthcare Data Model to bring structure to the complex task of integrating heterogeneous data coming in from various sources, including clinical, administrative and research. IBM’s Watson Health Cloud is based on the company’s supercomputer, Watson.
Information within the system will be de-identified so partners can analyze it using Watson’s data-mining and predictive analytics capabilities. Apple, Johnson & Johnson and Medtronic are three partners that will use Watson Health Cloud as the foundation for their own connected medical services. The platform will underlie Apple’s HealthKit and ResearchKit applications; Johnson & Johnson will use it to create mobile apps that educate caregivers on pre- and post-operative procedures, and Medtronic will use it to create highly personalized care plans for diabetes patients.