The growing assortment of software tools has made big data in healthcare copious source of actionable information. Perhaps the most marked momentum to this has come from the shift toward value-based care in the healthcare industry around the world. Technologies to harness the transformative potential of big data healthcare have enabled the healthcare industries meet the yardstick of high quality care to patient populations at low cost. Healthcare data analytics has laid a new paradigm in value-based care.
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Privacy and Budget Constrains Glaring Constraints in Deployment
However, the deployment of such technologies that can make sense of big data in healthcare has been relatively lacklustre compared to other industries. The concern of privacy of medical information is one of the foremost concerns. That aside, the lack of an integrated healthcare structure is also a key roadblock in adoption. Further, the lack of budget in cost-sensitive markets has also delayed the adoption of big data in healthcare over the past few years.
Big Data to help Clinicians Decide on Best Industry Practices
Nonetheless, in recent years several trends have accelerated the adoption of big data in healthcare. Most notable of these trends is the shift of healthcare providers to value-based care, a model of remuneration where they reap gains from improvement of the health of populations. Another important trend is evidence-based information from big data analysis. This has also broadened the understanding of clinicians to decide the best approach for managing any illness.
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Another important trend driving progress in the big data in healthcare market is the pressing need for constant monitoring of health conditions, especially in patients who are at risk of chronic diseases or those who are already suffering with one. The growing numbers of wellness programs that harness insight from big data in healthcare has also imparted a big momentum to the market. However, a few concerns will continue to attract the attention of stakeholders, most notably data aggregation, policy overhaul, and changes in management structure.