Extract hidden text information and build precise patient registries — quickly and easily!
The Regenstrief Institute has developed data analytics tools for population health that enable deep exploration of both unstructured textual data and structured discrete data to create precise patient cohorts.
nDepth™ incorporates natural language processing (NLP) capabilities and advanced text mining to query vast collections of free text data in order to qualify patients based on concepts derived from notes in the medical record. Paired with the Regenstrief Patient-List Generator (RPG), which searches limitless structured data points to identify populations that match specific criteria, the ability to query across the entire electronic health record significantly improves the accuracy and effectiveness of patient identification for research, quality assessment, and improvement initiatives.
Why the rising interest in NLP?
“Up to 80% of clinical data is locked away in unstructured documents”
A wealth of useful data is buried in free text within electronic health records and other clinical documents. Unlocking the value of this information is key to quality improvement, research and outcomes analysis. Typically, highly qualified clinical staff must extract discrete information from patient charts manually which is time-consuming and expensive. Getting this data efficiently and effectively requires faster and more scalable alternatives.
Built on the foundations of natural language processing and text mining, nDepth™ unifies vast collections of free-text data sources creating a sophisticated clinical index. Built and fine-tuned by clinicians using the oldest and largest health information exchange in the country, nDepth renders complex search patterns as simple recipes in order to find more patients with higher accuracy and faster than other methods.