Posts from November 2013

Simon Beaulah

(Cambridge, England and Boston, USA – November, 18 2013) Linguamatics, the market-leader in natural language processing-based (NLP) text mining and analytics, today announced the launch of Linguamatics Health, a new clinical NLP suite that enables hospitals and research organizations to harness the information contained in unstructured fields of EHRs and patient narratives to drive healthcare analytics, advanced research and improved patient outcomes. 

Linguamatics Health provides the technology needed to extract meaningful information from the mass of data located in complex patient documentation such as pathology and radiology reports, physician notes, and discharge reports. The information is then used in data warehouses, predictive models and dashboards to improve hospital efficiency and support Meaningful Use initiatives.

The information can also be used to populate clinical annotations for biobanks and provide data for Clinical Trial Management Systems to improve disease understanding and clinical trial recruitment. 

“While the rapid adoption of EHRs in recent years has integrated many data silos together, healthcare providers are still faced with a large proportion of their data in unstructured form.

"To achieve the improvements in hospital efficiency and patient outcomes required to cope with rising costs and an aging patient population, hospitals, payers and other healthcare organizations need to make better use of unstructured text,” said Phil Hastings, Senior Vice President, Sales and Marketing, at Linguamatics.

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Linguamatics, the market-leader in natural language processing (NLP)-based text mining and analytics, today announced that Huntsman Cancer Institute (HCI) at the University of Utah has deployed its NLP based I2E software platform to transform the immense stores of unstructured text in electronic health records (EHRs) into actionable information to drive improvements in cancer research, treatments and outcomes.

HCI is using Linguamatics I2E with its in-house clinical informatics infrastructure to extract discrete data from the unstructured text contained in surgical, pathology, radiology, and clinical notes related to hematology disease areas such as Leukemia and Lymphoma.

The resulting data is loaded into an integrated biobanking, clinical research, and genomic annotation platform. This enables HCI’s clinicians and principle investigators to harness the richest possible set of data for research into patient outcomes, comparative effectiveness, and genetic drivers of disease.

Analysis at this scale can find information that would often be missed when reading documents one at a time.

In addition HCI has a better range and quality of data to support clinical trial matching and increase numbers of patients on trials.

“Healthcare organizations face a major challenge to identify, capture and leverage valuable knowledge buried within vast stores of complex, unstructured patient data, and to do it in a reproducible and scalable way”, commented Phil Hastings, Senior Vice President, Sales and Marketing, at Linguamatics.

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