Innovate or stagnate: The challenge of extracting value from unstructured data

In the vast majority of sectors outside of healthcare, managing data is pretty straightforward. You have your continuous, discrete, and categorical data that fits nicely into spreadsheet cells for easy analysis. But then there's healthcare data. It's a different beast altogether, more complex and cumbersome to handle and manage than data from other industries.

For organizations drowning in data, figuring out how to unlock the full potential of healthcare data can seem overwhelming. This challenge is particularly acute for startups; without a viable solution, it could lead to failure.

Healthcare data: Abundant but complex

Whether you're part of a health system striving to beef up reporting, an organization dedicated to precision medicine, or a hospital network building a health information exchange (HIE), understanding the unique complexities of healthcare data is crucial. A strict set of rules governs it and must be handled accordingly.

Healthcare data is typically categorized into two major types:?

• Structured:?Patient diagnoses, medications, immunization dates, allergies, and laboratory and test results
• Unstructured:?Clinical notes, treatment plans, image studies, and genomic information

Structured data is sometimes the more straightforward of the two to utilize and operationalize, but it often still requires?standardization?to fill in gaps. That's because as patient data is extracted from and exchanged among sites and systems, it can become incomplete and inconsistent, making it less useful for analytics. Unstructured data, which also contains a wealth of information that aligns with the original assessment from the care provider, faces the same challenge – and then some.

Gaining the value of unstructured data

With 70 to 80% of healthcare data being unstructured, mining it for valuable insights is no small feat. It demands more than just basic tools like generic natural language processing (NLP) solutions or tools whipped up by researchers. We need sophisticated approaches to turn this unstructured data from a mere collection into curated, actionable, evidence-producing gold.

Unstructured data typically resides in electronic health records (EHRs) or is pulled out through an Extract, Transform, Load (ETL) process for other uses. The real challenge lies in extracting meaningful insights from this pile of data.

Imagine trying to find a book in a library that doesn't use the Dewey Decimal System. It's not impossible, but it's definitely a hassle—similar to the technical headaches of digging through unstructured healthcare data.

Often, it takes manual effort or interpretation to pull out the nuggets of value from healthcare data, which can lead to a deviation from the original clinical intent. And suppose you're not leveraging both structured and unstructured data. In that case, you're likely losing out on capturing the true essence and clinical intent of the data, making it hard to realize its full potential.

Conclusion

Whether you're looking at structured or unstructured data, leveraging clinical data in healthcare is daunting and far from straightforward. Many organizations and leaders are still playing catch-up, needing to fully understand the complexity and the investment required to make their data work for them. And at the end of the day,?if you can’t leverage your data, you can’t monetize it.??

To learn how IMO solutions can transform unstructured data into usable, accurate information, helping you extract real value from your data, click here.

(This article is adapted from a blog by IMO's Senior Director of Clinical Informatics, Amol B. )