Maximizing your data to improve patient outcomes
Augmented Curation
By transforming unstructured clinical notes data into structured form through the extraction of key sentiments and relationships from text, our “augmented curation” uses natural Language algorithms to effectively automatically transform unstructured text from the EMR into labeled patient datasets.
Data Harmonization
Due to the long and varied history of health data, even the structured data is complex. For example, the same lab test may be represented with multiple distinct codes and use different units of measurement. We use software-aided processes to transform this semi-structured patient data consisting of distinct data points into common unified data variables, which can then be consumed for downstream applications and analyses.
Triangulating your data
Improving your data connectivity
Triangulation with public and proprietary data
Integrating additional data sources with labeled, curated and harmonized patient data can strengthen the insights derived from downstream data analysis. This data includes patient claim life cycle dataset, biomedical literature (ex. PubMed), clinical trials (ex. clinicaltrials.gov).