Clinithink: Delivering Clinical Data Insights

Clinithink: Delivering Clinical Data Insights

CIO VendorDr. Chris Tackaberry, CEO
Globally, healthcare is undergoing profound changes primarily driven by the increasingly challenging economics of conventional healthcare delivery. Healthcare providers are progressively adopting new technologies to analyze vast amounts of data. It has become critical to have the ability to use the available data within healthcare and life sciences.

“Transforming unstructured clinical narrative into structured patient information helps to make better clinical decisions improving patient outcomes, more accurate population health management, and faster subject recruitment for clinical trials,” says Dr. Chris Tackaberry, CEO, Clinithink. Clinical information systems typically capture data in structured formats, derived from template-driven user input. “But unstructured narrative within progress notes, discharge summaries, consults, referrals and reports are also increasingly available electronically. Using conventional approaches, this data is not easy to exploit,” adds Tackaberry. Headquartered in Georgia, Clinithink provides Natural Language Processing (NLP) applications that can interpret unstructured clinicians notes using specialized linguistic algorithms, extracting the clinical information for better health management and faster subject recruitment for clinical trials.

Clinithink has developed CLiX, its Clinical Natural Language Processing (CNLP) engine. Using sophisticated clinical language models and algorithms, CLiX CNLP transforms the unstructured data’s clinical meaning into standardized terminology to use across a broad range of healthcare technology applications. Furthermore, Clinithink has developed CLiX ENRICH, a data query platform that turns the unstructured data into a valuable, consumable data stream for analytics. “Analytics plays a vital role in reaching the next clinical frontier, in which reactive treatment of disease is replaced with proactive prevention, monitoring, and management of chronic conditions,” explains Tackaberry.

CLiX ENRICH allows users to import unstructured clinical narrative data, process it using CLiX CNLP platform, and then interrogate the output using the abstraction and query platform, CLiX Query.

Analytics plays a vital role in reaching the next clinical frontier, in which reactive treatment of disease is replaced with proactive prevention

The resulting data is then stored for use by the consuming system or can be integrated into a business intelligence platform or enterprise data warehouse.

One of the UK’s leading healthcare providing organizations, Barts Health NHS Trust, deployed Clinithink’s CLiX ENRICH platform as a part of their solution to support the analysis of e-health record information linked to other routinely connected clinical data in order to learn more about vulnerable patient groups. Using CLiX ENRICH, the customer was able to access and “light up” the relevant unstructured clinical data extracted from their EMR. CLiX ENRICH helped them to interrogate the unstructured data and combine it with structured data to enable them to identify high risk groups that can then be proactively managed to improve outcomes and reduce cost.

According to MarketsandMarkets, the global NLP market for health care and life sciences industry is expected to grow from 1.10 billion dollars in 2015 to 2.67 billion dollars by 2020, at a CAGR of 19.2 percent. In the current scenario, North America is expected to be the largest market on the basis of spending and adoption of NLP solutions for the healthcare and life sciences industry.

The company believes their solutions are creating a significant impact in the life sciences space on the patient identification stage of the recruitment process. Using CLiX ENRICH, clients can largely automate the manual chart review—an expensive and time-consuming process needed to assess eligibility against the more complex inclusion and exclusion criteria for a protocol—delivering potentially significant savings in the cost and time taken to identify high quality patients for screening. “Early proof point work we have done in the field supports our belief that the overall impact this approach can have on patient enrolment would be substantial, especially for those trials with complex clinical inclusion and exclusion criteria,” shares Tackaberry.
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