Healthcare industry has huge data. This data is considered “Big data” when it is large in terms of volume (terabytes or even petabytes), variety (different types of data from both structured and unstructured sources) and velocity (periodic flow of data). Big data has opened a lot of opportunities for healthcare providers, payers, patients and other constituents.
- Involving patient: Healthcare model has turned upside down. The old theory of incentive for providers by keeping the patient in treatment for maximum days has gone now. Although it meant more revenue for the organization but now the new model of Accountable care organizations (ACO) is to incent and compensate providers to keep patients healthy. Interestingly now patients are also demanding information about their healthcare options and want to be part of the whole process so as to make the right decisions about their care. Here the role of Big data is to give the accurate and up-to-date information to the providers and patient so that they can make better decisions and better adhere to treatment programs.
- Improving quality by utilizing external data: More and more external data is becoming available due to initiatives like electronic medical records (EMR). But EMR Integration is a challenge that needs to be addressed. This can be solved by Big data and it can effectively use this data for better information and decisions, and more meaningful efforts.
- Addition of regional as well as global data: Out of the various sources of data, various countries and regions form the most important part. This data will provide meaningful information to the healthcare researchers for clinical studies, trending and disease monitoring for epidemics, as well as early detection and the potential for improved results. Big data will prove to be an innovator across this broader healthcare data ecosystem.
- Increased data mobility: The regular flow of data (velocity) is one of the important features of Big data. As current data is gathered, the necessary information can be immediately passed to the people in need especially providers for clinical decision support. In addition to providers, users will also demand access to this data to make the best possible healthcare decisions.
- Creating useful information from social media: Although social media increases communication between patients and providers but the data produced by social networking websites is huge in volume and lacks structure and velocity. This will not only work to globalize and democratize healthcare, but it is also a potentially important source of big data.
Technological solutions for dealing with Big data
There are abundant technology solutions for dealing with big data. Like a healthcare organization may have an on-site, cloud or open source solution. On-site options have low time to value and maintenance but relatively high total cost of ownership. On the other side, Cloud-hosted software can reduce the barriers of participating in the big data arena. Open-source is a high-performance, scalable and relatively low-cost option for dealing with big data.
How to successfully identify and implement big data solutions?
Every healthcare organization must devote time and resources to envision and plan the identification and implementation of big data solutions. A healthcare organizations looking to leverage big data should follow the following steps:
- First of all a business intelligence center of excellence should be developed with a focus on big data
- A big data strategy should be decided based upon the current and future objectives of the healthcare organization.
- Various big data initiatives should be assessed to meet overall corporate objectives, especially focusing on early win.
- Selection and discussion with a Big data technology partner about different trends, security, integration of internal and external system, hosting and development platforms, and application and solution development.
This preparation will provide the right foundation needed for strong execution. Moreover this Big data solution will definitely solve the challenges of large and fast-growing data volumes in a cost-effective way. The end result will be insights that can improve patient care.
