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"From Fragments To Clarity: Empowering Research with Complete Patient Data"

To advance drug development, researchers need the complete picture of the patient journey. Natural language processing can help by accessing and making sense of the unstructured clinical notes in EHRs.

The need for pharma researchers to leverage comprehensive information has grown as medical care has advanced. The problem is that crucial information is typically not included at the ICD-10 code level in claims data. 

When working with complicated diseases, such as myocarditis and many others, researchers need the details that exist only in unstructured clinical notes. Indeed, researchers who are seeking to improve drug development need to work with a complete picture of the patient journey that includes real world evidence (RWE), when trying to advance clinical treatments. 

The problem: EHRs typically contain structured information such as demographics, diagnostic codes, vital signs, lab results and prescription data as well as unstructured information such as physician notes, pathology reports, discharge summaries, patient narratives and other information.

Researchers, however, can get the complete picture needed to advance their work by:

• Using natural language processing to extract and make sense of the unstructured information such as disease markers, patient-reported symptoms and treatment rationales from EHRs
• Combining NLP-enhanced EHR data with claims data to provide a more complete view of patient journeys
• Integrating an NLP tool into an EHR dataset
• Working with a technology vendor, such as Veradigm, that owns the data or has explicit permission to access the data to effectively apply an NLP tool that will create valuable insights

By accessing such intelligence from unstructured EHR data and combining this knowledge with insights culled from structured data, researchers can benefit from regulatory-grade, RWE that’s purpose-built for drug development.

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