It's been more than a few months since the last blog post about I2E - enough time for two versions to be released!

In this post, I will highlight one feature from each release, as well as one part of the product that improves with every release.

I2E 5.4.1 - Blank Smart Query Slots

Previously, Smart Queries required a search term in each field: you had to include a Phase (or multiple Phases). In I2E 5.4.1, it is now possible to design the smart query with a field that is allowed to be blank. For example, you may want to make it possible to leave the Phase field blank (that is, allow clinical trials with any Phase) but still search for an Indication (or multiple Indications).

This applies to Dates, Authors, Locations, etc. any Smart query item can have its field blank. A great example of this is the collection of Table queries in the Resources query tree: they now allow table headers to contain Any item.

Fig. 1 Out-of-the-box Table Extraction queries support the new ability to allow a Smart query field to match Anything


Hot Topics at HIMSS19

The 2019 Annual HIMSS Conference & Exhibition in Orlando proved to be another spectacular event. HIMSS continues to grow, with an estimated 45,000+ individuals from over 90 countries attending (a 5K population increase in projections from last year).  This was Linguamatics 5th consecutive year exhibiting at HIMSS, and each time it seems the more information we ingest from the event, the more eager we are to attend the subsequent year. This 5-day event offers endless opportunities to educate oneself on ‘what’s new’ and ‘hot topics’ within the industry, and to engage in robust networking sessions.

Trying to find the “signal in the noise” can be difficult at HIMSS. It is both exciting and overwhelming. Two topics of particular interest come to the forefront of my mind: Artificial Intelligence and Interoperability is the one and physician burnout is the other. Burnout ‘studies’ seem to be wildly inconsistent but when you look at the frustrated, sometimes defeated physician faces (and hear the tone of the conversations) my expertise says there is only one obvious conclusion!


Real world evidence provides significant insight into how a drug or drug class performs or is used in real world medical settings. Real world evidence (RWE) and real world data (RWD) can inform all phases of pharmaceutical drug development, commercialization, and drug use in healthcare settings.

The ability to quickly transform real world data sources (e.g. EHRs, or patient-reported outcome data from forums, social media) into evidence can improve health outcomes for patients by helping pharmaceutical companies be more efficient in drug development and smarter in commercialization.

Voice of the customer call feeds: a valuable source of real world data 

One source of patient reported outcomes available to pharma companies are the feeds that come into the 1-800 call centers – calls from patients, carers, healthcare professionals or pharmacists, asking questions covering many different issues, such as:


Learning more about drugs understanding in the market

How can pharma product managers efficiently learn how their drugs are faring with patients in the market?

Product managers and teams in pharmaceutical companies need to know what patients and healthcare professionals are reporting and asking about their drugs as they are used in the market, in order to discern trends and patterns and respond appropriately. Real world data (RWD) on drug usage and patient behaviours is available in multiple formats from myriad sources, but mining these disparate structured and unstructured sources with traditional manual search and curation is time-consuming and inefficient.

Novo Nordisk wanted to accelerate, automate and scale this process to provide enhanced access to the extracted information for superior and actionable insights.

Natural Language Processing-based Text Mining at Novo Nordisk

Novo Nordisk was already using the Linguamatics NLP platform in-house on multiple individual text mining projects with good success (e.g. reducing a publication gap analysis from three-to-four people for six weeks to a few hours). They wanted to capitalise on this success for real world data about their diabetes therapeutic products, from medical affairs team, healthcare professionals, and patients.


Linguamatics NLP platform supports medical research and patient care delivery

Natural Language Processing (NLP) is used to transform text and unstructured data into valuable real-life, outcomes. Generally in Healthcare this application is still in a relatively early stage of adoption. However, some organizations are moving forward towards full success in using NLP to deliver enhanced healthcare research and clinical processes.

Walter Niemczura, the director of application development at Drexel University College of Medicine in Philadelphia, is one of the individuals driving the ongoing initiative to improve healthcare research. Niemczura began working with Linguamatics seven years ago, in order to identify patients with certain characteristics that were well represented in unstructured clinical notes from Electronic Health Records (EHRs). Niemczura realized that the discrete data they had been working with wasn’t going to be enough to really advance and support research and patient care efforts.

"Linguamatics NLP was a huge time-saver. When you’re looking at hundreds of thousands or millions of patient records, the value might be not the ones you have to look at, but the ones you don’t have to look at." Walter Niemczura, director of application development, Drexel University College of Medicine