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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.

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.

Five Lessons for Building Adaptive Healthcare Data Models that Support Innovation

Healthcare data models are the backbone of innovation in healthcare, without which many new technologies may never come to fruition, so it’s important to build models that focus on relevant content and specific use cases.
Health Catalyst has been continuously refining its approach to building concise yet adaptive healthcare data models for years. Because of our experience, we’ve learned five key lessons when it comes to building healthcare data models:

Focus on relevant content.
Externally validate the model.
Commit to providing vital documentation.
Prioritize long-term planning.
Automate data profiling.

These lessons are essential to apply when building adaptive healthcare data models (and their corresponding methodologies, tools, and best practices) given the prominent role they play in fueling the technologies designed to solve healthcare’s toughest problems.

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.