Sometimes we like to brag.
Clinical research often requires the identification of cohorts that follow precisely defined patient and disease-related inclusion and exclusion parameters. nDepth™ unlocks knowledge contained in unstructured textual documents (e.g., pathology reports, clinical notes) enabling search, comparison, and evaluation.
In 2015, Regenstrief completed a research project for Perpherial Artery disease (PAD).
Example of Research projects using nDepth™
- Find patients with metastatic melanoma
- Identify pre-diabetic patients for clinical trials
- Identify patients with a family history of lung cancers
- Find reasons for refusal of osteoporosis medications
- Identify “triple negative” breast cancers
The rich and diverse clinical data contained in unstructured textual documents (e.g., physician notes, operative reports, pathology and radiology reports) is invaluable for decision support, patient management, quality improvement and outcomes analysis. Getting this data is time consuming and expensive, requiring highly qualified clinical staff to review and extract discrete information from patient charts manually.
nDepth™ extracts clinical data and other hard-to-find patient characteristics from notes in the medical record, making it available for clinical use.
In 2015, Regenstrief completed a pilot study for extracting LVEF measures for MSSP-ACO-33.
Example of clinical applications using nDepth™
- Extract LVEF values for MSSP-ACO-33
- Capture hypoglycemic events
- Measure adenoma detection rate during colonoscopies
- Map patient trajectory following cancer treatment
- Detect treatment failure in insomnia