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The challenges of implementing big data analytics in healthcare

When implementing big data analytics in healthcare, three main challenges face healthcare providers.

This tip is part of a series exploring big data analytics in healthcare. Each story in the series will break down an aspect of analytics and where it fits into healthcare needs. This piece focuses on the three biggest hurdles to applying valid big data analytics in healthcare.

Much hype surrounds data analytics systems in healthcare, mostly among provider executives who are focused on key initiatives such as ICD-10 and meaningful use mandates. Yet clinical and IT departments both have narrow focuses; meaning they solve one problem at a time and don't think holistically. The latter approach will not lead to a good big data implementation.

Clinical and IT departments must take a longer view to overcome the biggest challenges facing big data adoption: episodic culture, data puddles and IT leadership.

The episodic culture: The healthcare industry is extremely conservative and lacks the IT entrepreneurial mindset. Healthcare and government are the only two sectors where you will find a large population of the workforce with more than 20 years of work in the same organization. Executing the same protocols for years without a desire to question their validity has led to stagnant minds. This mindset has created a rigid culture and can prevent bringing new blood with innovative ideas to the organization. Only a handful of healthcare providers have overcome this barrier, but most still can't think beyond existing protocols. However, it is possible to be creative within the regulated industry. You must have an inquiring mind when it comes to big data.

The data puddles: Silos are all over in the healthcare processes -- labs, diagnosis, radiology, emergency, case management, etc. All silos have their own way to collect data. Most still rely on paper charts in paper folders, and fax is the method of sharing. Then EHR/EMR vendors simply transfer these paper charts to electronic charts without leveraging IT systems' best design principles. Even when you use EHRs, data remains imprisoned within proprietary EHR databases. Removing data from such repositories is a huge challenge. Making analytical use of this requires content harmonization to build a data warehouse. Most healthcare organizations are not familiar with basic concepts of data warehouses. It will be long way before healthcare providers understand the value of big data.

The IT leadership: Just recently, I had the opportunity to meet with one of the largest healthcare providers to discuss their data challenges. Much to my surprise, the IT executives who were chartered to select analytics tools and technologies had no idea about what a data warehouse is or who, where, when or how often users will use specific analytics. If we find such incompetency among highly progressive healthcare providers, imagine what we can expect across the rest of the country. IT executives mostly rely on vendor sales pitches, but this isn't always favorable for big data implementations because often data is stored within a vendor's tools and they can control your level of access.

To really exploit the value of big data, healthcare providers need to change their culture, starting with the leadership team. They should bring innovative leaders to their organization from other industries such as consumer packaged goods, entertainment and social media.

Providers should also team new IT blood with medical experts from their organization to craft analytics-driven clinical workflows and processes to continuously improve patient care. The scope must go beyond the four walls of your organization, with integration at the heart of big data. Healthcare providers don't need to build on their large IT staff, but they must be able to share knowledge subscribed from clinical clouds -- just like IBM's Watson. Not all organizations need a big data engine like Watson, but most should use a specialty cloud service that sits on top of a device like Watson that provides different diagnosis and can be woven within your clinical workflows to exploit the value of big data.

Naeem Hashmi is chief research officer at Information Frameworks, as well as an expert in healthcare data analytics, information management and data exchange. He is an active member of HIMSS, CHIME and AMIA. Let us know what you think about the story; email[email protected] or contact @SearchHealthITon Twitter.

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