~Big data and healthcare are big businesses, and well suited for one another. The healthcare community generates massive amounts of information. From electronic health records (EMR) to imaging and doctor’s notes, the data is there for curation that can improve care quality, marketing and compliance.
~The goal of big data and healthcare is a little different than what you see in other industries. Researchers can use this information to study genetics, investigate disease processes and develop theories that will improve healthcare down the road. It is the clinically relevant information that makes big data and healthcare analytics such a goldmine, explains Information Week.
What is Big Data?
~Historically, big data covers the three V’s:
• Volume – Large data sets have more value
• Velocity – How quickly the data is being generated and received
• Variety – Diverse information has more analytic potential
~You could add complexity and variability to the definition, as well. Big data is an umbrella term that applies to large data sets that requires special handling and curation. And there is still some redefining and refinement that continues even today.
Big Data vs. the Traditional Database
~Relational databases are built around a table and column structure. Big data harvesting is broader and less defined. It has its roots in open source processing, so it is less expensive and easier to maintain. The hard part is analyzing this raw data to put it to work. There are no simple schemas that guide the process like you see with relational databases.
Incorporating Big Data and Healthcare
~Healthcare faces a number of challenges such as facilitating EMR systems and regulatory reporting. Patient security limits the value of big data, for example. Currently, the use is limited to research and that type of data harvesting requires expertise not available at most hospitals. These data scientists are in big demand across many industries.
~HIPAA compliance is another obstacle. Patient privacy is absolute and works against the healthcare big data process. Medical organizations have to be selective to protect patient rights, because big data processing isn’t always secure.
~How can the healthcare industry get the most from big data analytics and still follow all the rules? Finding the right platform is the key.
The Future of Big Data and Healthcare Analytics
~The future of big data and healthcare analytics lies in what experts refer to as the Internet of things (IoT). This is a growing network of information taken from a variety of sources. Smart technology is allowing the Internet to evolve and soon everything from your major appliances to the city streets will revolve around the web creating the Internet of Things.
~The IoT will include healthcare devices that generate data and send it into the cloud. Wearable monitors are a good example of this process in action. Today, people wear fitness monitors to track how many steps they take in a day while measuring their heart rate and respirations. There are monitors for blood pressure and wearable blood sugar management systems. This information is processed wireless using cloud technology.
~Add to this growing trend of home monitoring systems that allow patients to spend more time out of the hospital and healthcare will potentially contribute a considerable amount of data to the IoT network.
~Through healthcare analytics, the industry can make use of predictive analytics for public health. The socioeconomic data can pinpoint areas at risk for disease. It can be used to manage individual healthcare, as well. For example, following up after a patient is dismissed from the hospital or scheduling necessary appointments.
Getting the Healthcare Industry into the Big Data Game
~Big data and healthcare technologies are still evolving, so what can healthcare organizations do now to be part of the game? It starts with finding the right data warehousing solution to help make the transition from a traditional database models to raw, unstructured big data.
~There are already examples of big data and healthcare in action. Health Catalyst is working with Microsoft to create a parallel data warehouse to hold massive amounts of raw data using a Microsoft APS appliance and the Hortonworks Hadoop Cluster. This will allow healthcare organizations to run relational databases in parallel to a big data cluster. By experimenting with this process, they can find ways to use big data and healthcare analytics to improve the industry.