What if your doctor prescribed a one-size-fits-all treatment for you? That is, the recommended treatment didn’t cover all of your symptoms but some of them. How much comfort would you take in knowing that the doctor’s recommendations might work, or has worked some of the time on some people in some parts of the world at some point in time? How much comfort would a doctor or hospital administrator take in knowing that this course of treatment may not work and that you are likely to return?

And would an insurance provider take these chronic hospital visits as a signal that you are a high-risk patient whose premiums need to increase, or maybe it would reconsider offering coverage to this particular health network for such a high readmission rate?

For generations, doctors, nurses and medical practitioners of all types used first- and second-hand knowledge and other limited resources to treat patients. What seems accurate is generally derived from “the aggregate,” or a general context-specific treatment. We’ve come a long way since the early days of diagnostics, and modern medicine has done wonders on advocating lifestyle, diet and exercise changes as preventive care options.

But even now, the rise of big data can deliver insights and care solutions that medical professionals did not have access to just several years ago. Aggregating patient records, widespread epidemiological studies, chronic insurance claims, and even flu-related Internet search queries for data mining can help improve the quality of care and reduce costs However, integrating all this information to achieve these valuable, data-driven insights is no small task.

With the world’s population growing, and average life expectancies increasing, treatment deliveries are improving but are still constantly evolving. Big data initiatives are trying to address this need for evolution. But employing big data to achieve useful insights requires interpreting and processing everything from a short doctor’s scribble on a patient chart to multiple lab test results to massive imaging scans from several non-networked facilities. It’s complicated, and this is before we start talking about the rapidly expanding healthcare technology built on the Internet of Things.

Part of the complication stems from an explosion in the wide-ranging set of formats, from open to structured, semi-structured and unstructured data. The challenge with managing such a variety of data means companies must have the capacity to support any type of file and connect any type of endpoint. And with many interconnected components in healthcare – hospitals, labs, pharmacies, insurance companies, and more – the number of endpoints can be overwhelming.

Integrating different data sources poses its own set of challenges: Reliably connecting all of these to quickly integrate the information while maintaining stringent security and compliance mandates.

Electronic health records often live across various systems and in various formats. Coupled with the fact that the vast majority of medical information – including physician, nurse, and even discharge notes – is unstructured data, easy analysis becomes challenging.

Data transformation and integration deliver the ability to work from the same data set in standard formats and then map that data into administration or clinical systems. Some critical data and technology requirements for every healthcare organization include:

  • Compliance with HIPAA and other healthcare-related standards
  • Widespread integration with EMR and other care coordination applications, whether on-premise or in the cloud
  • Large files and transaction volumes, including large MRI/X-ray files and FDA clinical trial reports
  • Communication and data security, including mobile and machine-to-machine data exchanges comprised of information from sensors, monitors, and other medical devices
  • Near-real time delivery response
  • Visibility into transaction communication and processing status

But cost pressures, not technology, have made achieving the aggregation and access of critical data seemingly unattainable.

Modern technology, including advanced enterprise integration and data transformation solutions, can help operationalize data to make monumental gains for healthcare providers, payers, and life sciences organizations. Data integration technology, combined with big data infrastructure software, has caught up, and data usability and fragmentation challenges can now be managed with a relatively modest investment. Thus, individual organizations—and the healthcare industry as a whole—now can realize the goals of providing better patient care while minimizing costs as more streamlined, functional units.

This not only unlocks the capabilities of big data, but also gives a much wider group of people within and external to an organization (medical, academic, administrative, payer, and research) access to the information.

Connecting information circulating out of hospitals, lab networks, clinics and patients is critical to guiding up-to-date prevention and treatment plans, and health networks must consider the benefits of a next-generation big data gateway, equipped with data transformation functionality, to facilitate the secure and on-demand movement of this valuable data.

With information properly formatted and routed to the appropriate end user, companies achieve faster cycle times, efficient delivery, and real-time transaction processing to keep healthcare networks humming. Organizations moving at this pace improve care, eliminate inefficiencies, gain new revenue, and distinguish themselves from the competition.

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