Posts from March 2017

What are the challenges facing life sciences and healthcare organisations, where text analytics can play a part?  This is one of the key questions that I ask myself and others regularly. There is so much buzz at the minute around big data, real world data, healthcare informatics, wearables; but what is really working, and what is just hype?

One of the ways we get input on this question is, of course, meeting our customers and hearing about their successes. Linguamatics hosts two user group meetings every year, and our European Spring Text Mining Conference is coming up rapidly. Held over 3 days in April, the conference provides scientists and clinicians interested in text mining to come for hands-on training workshops, round table discussions, and a day of talks from both Linguamatics staff and our customers.

This year, our customer speakers encompass a wide range of use cases, spanning the pipeline of discovery, development, and delivery of therapeutics:

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Pharmaceutical companies can now extract competitive intelligence from Dow Jones Factiva content using Linguamatics advanced NLP-based text analytics

Cambridge, UK & Boston, USA – March 28, 2017 – Market leading NLP text analytics provider Linguamatics today announced a partnership with premium news content provider Dow Jones. The agreement allows pharmaceutical companies to extract key insights from Dow Jones Factiva utilizing Linguamatics I2E text mining technology.

The Linguamatics I2E platform is currently used by 18 of the top 20 global pharmaceutical companies. The Linguamatics-Dow Jones partnership helps users to derive key insights from Factiva content by leveraging advanced NLP to identify and extract critical concepts in a structured format for review and quick analysis. I2E eliminates the need for users to manually read through large quantities of documents to search for critical information. Instead, I2E rapidly connects relevant facts and relationships in a way that synthesizes knowledge and creates actionable insights.

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HIMSS 17

Information Technology AND Healthcare? Why on Earth would you combine such incompatible career fields?

I can’t tell you how many times I was questioned about this in my past. Early on in my career, no one ever told me that my early pursuits of combining my Computer Operations training in the Air Force with my decision to pursue medicine was actually a good idea. In fact, it was quite the opposite. And yet - this year I can give about 45,000 more reasons (the number of attendees at HIMSS 2017 [1]) on why the path led to a promising merging career field after all.

The “missing link” career - people divided by a common career field.

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Population risk stratification has, so far, been biased toward structured data due to accessibility issues. As interest in long-term member wellness increases in importance it is the insights trapped in unstructured data that will become the differentiator in a changing and competitive market. The payers who are able to characterize member groups at a fundamentally more detailed level will have the advantage of population insight over those who struggle to do so.

Data sources that are increasing in scale and availability include electronic healthcare records (EHRs) data in Continuity of Care Document (CCD) format from providers, OCR notes about members, and nurses’ notes.

How can payers make effective use of unstructured data to stratify populations more effectively when much of their infrastructure is tied to structured data? Sources of unstructured data contain significantly more detail about members but are much more varied.

Here at Linguamatics Health, our Clinical NLP specialists understand the urgency and complexity of bringing together data sources, both structured and unstructured, in a workflow that gets you to insights you need quickly.

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Ever find an acute problem such as a fracture, which shows in a Problem List, but healed months ago? Or perhaps the problem list states a case of bronchitis that may have been transient or may actually be Chronic Obstructive Pulmonary Disease (COPD)? After all, a diagnosis of COPD is a collaboration of symptoms and test results. How many clinicians find the spare time to go retrospectively back in the EHR and calculate a patient’s, “coughing with excessive sputum nearly everyday for at least 3 months of the year, for 2 years in a row” [1]?

But fixing the problem list can be time-consuming and complicated. Isn’t there an alternative (better) way?

Many organizations believe that in order to derive an accurate picture of their population’s health, medication lists can be just as good as their problem list. What if you find a patient taking an atypical antipsychotic medication and they don’t have a diagnosis that coincides on their Problem List? Can we just assume a mental health diagnosis? After all, this conclusion seems logical. Or is it? Is it an oversight on their Problem List or are they prescribed it for an off-label reason? According to the Agency for Healthcare Research and Quality (AHRQ), a 2011 report stated off-label atypical antipsychotic medications uses. This included areas such as; anxiety, ADHD, behavioral disturbances of dementia and severe geriatric agitation, MDD, eating disorders, insomnia, OCD, PTSD, personality disorders, substance abuse, and Tourette's syndrome. [2].

Therefore, can we really make assumptions?

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