5 Top Challenges of Big Data Analytics in Healthcare

Big data has become the most disruptive technology that every industry segment uses today to improve their services and especially it has more significance in the healthcare sector. Big data is collected from various sources that include both internal and external sources where data is systematically analyzed to discover patterns and successfully use those findings to improve and avail quality health care services. As Big data is expanding its significance, the electronic health records (EHR) turn out to be a viable mode to pull out actionable insights from them. For healthcare organizations that successfully integrate data-driven insights into their clinical and operational processes, the rewards can be huge.

But typically the road to healthcare analytics has a lot of variations involved within them and is filled with some challenges that have been listed below:

1. Discrepancies with respect to Data Capture: It has been found that Data comes from various sources and usually this data is not clean, complete, accurate or properly formulated. This sort of data shows convoluted work flows and does not provide a complete understanding of the health complications and does not provide actionable insights. Thus, valuable data types should be used for specific projects and proper clinical documentation should be followed.
2. Lack of proper integration of data between clinical and administration systems: There is often an integration gap that exists between the patient care and the administration that looks up to it. The electronic medical records maintained by physicians on the hospital floor should be properly reflected in the insurance claims and proper billing. Care should be taken such that proper treatment codes are actually reflected for both administrative purpose and also for enabling proper big data analytics such that it delivers actionable insights.

3. Non implementation of proper operational analytics:

Healthcare data is mostly dependent on the electronic health records and this forms an integral part of the operational analytics. Proper analysis of this data helps to evolve new strategies to achieve better profitability and thus enable better and quality healthcare to patients.

4. Meeting HIPAA Compliance requirements: It has become important in meeting the HIPAA compliance as it requires a specific set of security measures for EHRs that have to be shared among practitioners and they should also be made accessible to the patients. But, this has become a major challenging factor for most of the healthcare providers as this needs proper implementation of network systems and lack of knowledge of cloud technologies that is also equivalently important in order to maintain large data.

5. Discrepancies with respect to Data Storage: Most

healthcare providers are unaware of their data storage as it involves cost, security, and specific performance issues with their IT departments. It has also been observed that as the volume of healthcare data grows exponentially, some healthcare providers are no longer able to manage the costs and impacts of on premise data centers. Thus, cloud storage needs to be implemented as it is increasingly becoming popular due to low costs and enables more reliability.
What Should Healthcare Providers do to avail best Big Data Analytics Services?

There are certain good and reliable Big Data Advanced Data Analytics service providers with Edge IoT solutions that help businesses to become more agile and smart.

Interestingly, the otherwise painful and tedious jobs such as data availability, speed to analysis and inability to predict the future are well-taken care of by Edge IoT. It further provides a perfect solution to make streaming data available as fast as possible to make complete usage of machine learning and analytical models.

Summary: Big data is the most disruptive technology and is used by all industries to improve their services and especially it is true with respect to healthcare industry. Big data collected from various sources is used to analyze the data to discover patterns that can be used to improve patient care and enable quality services.