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Data Analytics Solutions: What to Look For

Any mining operation requires modern tools and systems. You can’t recover much gold using only a pick and a shovel. In the same way, extracting the gold from EHRs requires modern tools and systems. In fact, healthcare organizations may require more power than their current analytics system can provide. Big Data is defined as a body of information “beyond the ability of typical database software tools to capture, store, manage, and analyze.” It’s not surprising that mining more value from the Big Data in EHRs often requires more sophisticated technologies. “Without the appropriate investments in contemporary healthcare IT that enables value-based care, existing systems will be pushed beyond the breaking point,” concludes a recent study from McKesson. McKinsey consultants echo the same sentiment, warning that “legacy systems and incompatible standards and formats” can defeat any attempts to gain from “more sophisticated analytics that create value from Big Data.” Organizations that have invested heavily in HIT must find a way to implement more analytics, without having to rip-and-replace their existing systems. To help extract more insights from EHRs, what features should healthcare executives look for? What Healthcare Organizations Need Healthcare organizations need a system that can extract critical insights from the unstructured patient data in their EHRs and other systems. Using a combination of natural language processing (NLP) and a comprehensive healthcare-specific taxonomy, this system should be able to identify important clinical information that is explicitly present in the data, as well as important information which is not explicit but for which there is evidence. This system should integrate seamlessly with existing IT systems, support interactive dashboards, and generate cohorts of patients sorted by risk factors. And it should be able to merge this data back into EHRs and other HIT systems. What to Look for in an Ideal Solution Here are five key capabilities that an advanced data analytics system must provide to truly mine more of the gold from EHRs and other HIT systems:
  1. Access to unstructured data in EHRs
  2. Advanced natural language processing
  3. Comprehensive healthcare-specific taxonomy
  4. Fine-grained risk stratification
  5. Smooth integration with existing HIT systems
In my next blog post, I’ll discuss Healthline’s HealthData Engine and Coding InSight application – and how they power clinical insights at the point of care. If you missed Parts 1 and 2 of this series, be sure to check them out: Unlocking the Value of Unstructured Data Mining the Gold in EHRs: Today’s Challenges Interested in learning more? Download our data analytics white paper.