Demos
The synchronized emergence and merge of electronic medical records (EMRs) and data science led to the evolution of other sciences that started to re-shape the healthcare research. Precision medicine, predictive analytics, evidence based medicine (EBM) were all results of this synergetic merge. Accordingly, the emergence of those new sciences led to re-shaping the policies of the healthcare insurance and medical industry.
Big data could be of great benifit to medical insurance companies and insurers thorugh tracking and monitoring the employees. Even the public sectors can make use of the big data concepts.
Companies started to hire special firms who can predict (through big data analytics) sickness possibilities of the employees or the possibility of their pregnancy. Those firms provide employers with such reports after analysing employees medical claims, prescription drugs they use, search queries, and birth-control forms.
Such monitoring and tracking can help in the employees health needs prediction, sickness suscpetibility, possible absentism from work.
Tracking and monitoring process can lead to not only sickness prediction but also to choosing the best treatment plans or prophylactic measures.
In addition, it can support claims denials/approvals.
Untill now there is no rules governing such data manipulation (employees tracking process). There is no control over firms who gather such type of data about the employees’ health or what they can do with it. Such analyzed data can not be covered by HIPAA, especially if they are not including employees’ names.
New technological methodolgies facilitated the “anonymization” or “de-identification” of EMRs and so could comply with HIPAA deidentification standards.
There is a great debate about the use of the risk factors (age, sex, social status, family history, … etc.) in estimating the prices of healthcare services.
We can simply say that big data allowed medical insurance companies to have a more precise Risk Assessment for evey proposed case. Accordingly, membership fees could be categorized according to the risk-level that case will fit in.
Even marketing departments use big data but in a different way. They trace customer care calls to the call center and started to “Flag” customer on the top of the list with respect to the number of calls. From marketing point of view, this might be an indication that the customer is thinking or might think to opt-out the insuance plan in the future. Accordingly, marketing guys can contact this customer and introudcue a specialy offer for him/her.
Governments and public sectors statrted to realize the importance of applying big data concepts to their data management plans. The number of electron microscope images generated per day in the past, could now be generated in one second. The worldwide data increases by 2.5 quintillion bytes every day. Most of these data stored daily are unstructured data (90% in some estimations).
From 2008 till 2013, Premier health alliance reduced healthcare spending by USD 9.1B. In addition, it helped to save 92,000 lives through 333 participating hospitals. Detecting unnecessary readmission and ER visits were beyond the huge costs cut down (1).
Every government now believes that there must be a quick and magnificent change to their data management policies and used technologies to cope with this new situation.
Cleaning and organizing this mass amount of unstructrued data cannot be done by the traditional SQL tools.
New technologies like Hadoop, NoSQL and Tableau are becoming an alternative to the the traditional data management process (extraction, transformation and loading processes (ETL).
Capgemini’s Seth Rachlin has been working in the insurance field for more than 25 years. He reported that he has never seen such rapid changes in the insurance industry business models like now (2).
Exploring the impact of Generative AI in Healthcare can provide insights into enhancing predictive analytics and improving patient care strategies. Additionally, the integration of Healthcare Chatbot technology can streamline communication between insurers and clients, leading to better service delivery and satisfaction.
References:
- Srinivasan S. Big Data in Healthcare Transformation. 2013; Available from: https://www-01.ibm.com/events/wwe/grp/grp004.nsf/vLookupPDFs/Sri Srinivasan’s Presentation/$file/Sri Srinivasan’s Presentation.pdf
- Woodie A. How Big Data Analytics Is Shaking Up the Insurance Business [Internet]. Available from: https://www.datanami.com/2016/01/05/how-big-data-analytics-is-shaking-up-the-insurance-business/
