Clinical data found in electronic health records (EHRs) and patient registries represent a significant source of actionable insight into patient-centered care, public health, and traditional or integrated models of clinical research. Data from these sources are available in both structured and unstructured formats.
Data captured in structured fields tend to be categoric, numeric, or coded and may be recorded, organized, and analyzed with relative ease. The data housed in unstructured or semi-structured fields, however, are much more difficult to analyze but may represent a significant source of understanding, particularly when considering the social determinants of health.
In this white paper we share:
- Industry insights into how data scientists are enriching EHR data through natural language processing (NLP) and machine learning (ML)
- How this data enrichment can help you tackle clinical challenges and explore therapeutic opportunities
- Case studies demonstrating how enrichment may reveal potential care gaps and underreporting of diagnoses and may augment clinical outcomes, cohort definitions, and cohort richness
Read Data Enrichment Whitepaper