NLP & Text Analytics


Health Catalyst Editors

Healthcare NLP: The Secret to Unstructured Data’s Full Potential

While healthcare data is an ever-growing resource, thanks to broader EHR adoption and new sources (e.g., patient-generated data), many health systems aren’t currently leveraging this information cache to its full potential. Analysts can’t extract and analyze a significant portion of healthcare data (e.g., follow-up appointments, vitals, charges, orders, encounters, and symptoms) because it’s in an unstructured, or text, form, which is bigger and more complex than structured data.
Natural language processing (NLP) taps into the potential of unstructured data by using artificial intelligence (AI) to extract and analyze meaningful insights from the estimated 80 percent of health data that exists in text form. Though still an evolving capability, NLP is showing promise in helping organizations get more from their data.

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Mike Dow

Healthcare NLP: Four Essentials to Make the Most of Unstructured Data

Many health systems are eager to embrace the capability of natural language processing (NLP) to access the vast patient insights recorded as unstructured text in clinical notes and records. Many healthcare data and analytics teams, however, aren’t experienced in or prepared for the unique challenges of working with text and, specifically, don’t have the knowledge to transform unstructured text into a usable format for NLP.
Data engineers can follow four need-to-know principles to meet and overcome the challenges of making unstructured text available for advanced NLP analysis:

Text is bigger and more complex.
Text comes from different data sources.
Text is stored in multiple areas.
Text user documentation patterns matter.

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Mike Dow
Levi Thatcher

How Healthcare Text Analytics and Machine Learning Work Together to Improve Patient Outcomes

Healthcare organizations that leverage both text analytics and machine learning are better positioned to improve patient outcomes.
Used in tandem, text analytics and machine learning can significantly improve the accuracy of risk scores, used widely in healthcare to help clinicians identify patients at high risk for certain conditions and, therefore, intervene.
Health systems can run machine learning models with input from text analytics to provide tailored risk predictions on both unstructured and structured data. The result? More accurate risk scores and the ability to identify every patient’s level of risk in time to inform decisions about their care.

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Mike Dow

Text Analytics in Healthcare—Two Promising Frameworks that Meet Its Unique Demands

Text analytics in healthcare is unique because it demands a higher degree of precision than what’s acceptable for a Google search or Yelp recommendation. So, it’s no surprise fewer than five percent of health systems are effectively leveraging clinical information in a truly significant way.
Fortunately, there are two promising frameworks for clinical awareness designed to meet the industry’s unique text analytics demands:

ConText—solves for negation, experiencer, and temporality.
cTAKES—includes the functionality of ConText, and adds the ability to identify the type of clinical term.

By integrating these frameworks into their solutions, health systems can overcome the unique challenges of text analytics in healthcare. They can evolve from simply finding matching text to understanding context and achieving the precision required in patient care.

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Eric Just

How to Use Text Analytics in Healthcare to Improve Outcomes—Why You Need More than NLP

Given the fact that up to 80 percent of clinical data is stored in unstructured text, healthcare organizations need to harness the power of text analytics. But, surprisingly, less than five percent of health systems use it due to resource limitations and the complexity of text analytics.
But given the industry’s necessity to use text analytics to create precise patient registries, enhance their understanding of high-risk patient populations, and improve outcomes, this executive report explains why systems must start using it—and explains how to get started.
Health systems can start using text analytics to improve outcomes by focusing on four key components:

Optimize text search (display, medical terminologies, and context).
Enhance context and extract values with an NLP pipeline.
Always validate the algorithm.
Focus on interoperability and integration using a Late-Binding approach.

This broad approach with position health systems for clinical and financial success.

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