The healthcare industry is generating a high demand for big data analytics platforms, and the global healthcare analytics market is projected to surpass $24.5 billion by 2021 according to new market research.
ReportsnReports anticipates the global health data analytics market to growth by 27 percent CAGR over the next five years. Government initiatives to increase EHR adoption, the need for improved patient outcomes, pressure to cut back on healthcare spending, and the availability of big data in healthcare are several major reasons why the healthcare analytics market is growing rapidly.
Many healthcare organizations are looking to data analytic platforms in order to qualify for federal funding aimed at rewarding efforts to making patient data actionable. Incentives aside, providing valuable information to help advance studies to improve patient populations is also a contributing factor to the growth of healthcare analytics.
“Various initiatives are being taken by governments across the globe to increase adoption of healthcare analytics,” the research firm said in a public statement. “In the U.S. various federal mandates such as the American Recovery and Reinvestment Act of 2009 (ARRA) and the implementation of EHRs and ICD-10 code sets are encouraging healthcare organizations to adopt EHRs, and enhance information exchange between various health systems.”
North America is expected to see the fastest growth by far in the healthcare analytics market because of the general growth of provider EHR adoption.
“High growth in this market is attributed to factors such as growing federal healthcare mandates to curb rising healthcare costs and provide quality care; increasing regulatory requirements; growing EHRs adoption; and rising government initiatives to focus on personalized medicine, population health management, and value-based reimbursements,” the research firm found.
Data analytics adoption in healthcare settings is fraught with many challenges with many organizations still struggling with EHR compatibility issues which prevent them from successfully implementing an analytics solution.
HealthITAnalytics.com recently reported that EHRs aren’t evolving quickly enough to keep up with healthcare big data analytics innovations and complex needs of end-users. Organizations find that data integrity concerns as well as difficult documentation requirements are possibly preventing EHRs from being a meaningful or useful source for big data analytics.
In addition to ensuring a healthcare organization’s analytics solution and is compatible with existing EHR technology, organizations need a scalable solution to store increasing volume of data, along with a flexible and usable infrastructure to support information exchange among healthcare organizations
Many organizations are challenged by their legacy systems and cannot afford to replace major parts of their infrastructure to accommodate new analytic technology. As a result, starting small when implementing data analytics solutions into an existing infrastructure is a safe way to go.
“If you start off with thousands of different data elements, you’re just going to drown in the data before you see any results,” Richard C. Howe, PhD, FHIMSS, Executive Director of the North Texas Regional Extension Center, told HealthITAnalytics.com. “We started with claims data, and we found that there is a lot of really good information there that has been valuable to our hospital members even before we started adding more clinical information.”
Using collected data for one purpose significantly reduces the strain massive amounts of uncategorized data outs on an IT infrastructure. Focusing on one element of research and only analyzing and contributing data to that cause in the beginning allows organizations to determine how their analytics tool will scale in the future. This gives organizations more time to budget for more storage and staff for future projects.
There is little doubt that analytics plays a large role in supporting interoperability and data sharing among healthcare organizations, but ensuring the correct analytics tools are supported by the IT infrastructure is the first step in successfully implementing an analytics solution.
