Ensuring patient safety is the highest priority for drug companies and prescribers – and obviously for patients themselves – so any steps that can give scientists and clinicians more accurate, well rounded descriptions of safety data should be welcomed by all parties. AstraZeneca (AZ) wanted test the hypothesis that adverse reaction (AR) information from patients could effectively supplement information from clinical trials, and a key challenge was assembling comparable data sets. AZ studied the commonly reported adverse reaction “nausea”: it is associated with many drugs, and there is a wealth of documented information – albeit in a variety of formats. It is also often debilitating, so anything to reduce its occurrence would be of value to patients.

Patient-reported Real-World Evidence

AZ worked with the patient-generated health data in the PatientsLikeMe system and looked for records reporting nausea as an adverse reaction. Because the PatientsLikeMe system is very well structured, it was relatively simple to extract a clean nausea AR data set that was amenable to comparison. 

Clinical Trial Events

Adverse reactions observed in clinical trials are included on drug labels and the data is then listed in the online DailyMed repository maintained by the National Library of Medicine. FDA only offers guidance on how to submit the data, so the content and formats are highly variable, and this complicated creating a well-structured data set to compare with the PatientsLikeMe real-world data.

A valuable part of a clinician’s training includes the effective identification and careful documentation of all the elements impacting a patient's well-being. Thorough documentation is essential to ensure accurate and timely clinical care. Although electronic health records (EHRs) hold many great opportunities to capture essential details in electronic form, patients could be at risk if all elements of their medical records are not compiled and analyzed. With 990.8 million reported visits to physician offices in 2015 [1], odds are that precious information could slip off the radar of even the most dutiful clinical staff. 

The importance of Nature AND Nurture in healthcare

For this reason, providers must adopt a successful population management strategy that considers all elements of a patient’s record, including the estimated 70% of the record that exists as unstructured notes. Structured data is excellent for documenting patient information that ensures a hospital runs effectively but not as efficient for capturing imperative clinical concerns during a patient’s 20-minute encounter with a physician. 

Although the concept of nature vs. nurture has been well-documented for centuries, providers are just now realizing the critical importance of social determinants:

  • Does a patient live alone?
  • Do they utilize a walking cane?
  • Are they on a fixed-income? 

Identifying before protecting: Using I2E to help vulnerable populations

Undoubtedly, EHRs contain a wealth of information to identify patients requiring special attention, such as those with:

At this year’s Spring Text Mining Conference, you will get the opportunity to take part in our new I2E Certificate Program, which we launched at our 2017 Text Mining Summit.

This exciting opportunity will allow you to certify your I2E natural language processing (NLP) text-mining knowledge and skills. The Level 1 Query User Certificate will be open to those who attend the “Introduction to I2E” hands-on workshops that will take place at the STMC this April, as well as more established users, who have already attended the “Introduction to I2E” training. See the Spring Text Mining Conference Workshop Selection Guide for more details. It’s free to join in as part of your registration.

Completing the different levels of the Certificate Program will allow you to validate, extend and improve your I2E skills. The Query User Certificate will focus on using and editing basic queries and Resource queries to:

  • Create simple queries with different constraints, morphological variants, preferred terms and alternative lists
  • Use classes to improve recall and precision of queries with linguistic classes, ontologies, and pattern ontologies
  • Work with results by using limits, output formats and displays
  • Use Resource queries to answer common questions

Those taking the Level 1 Query User Certificate at the Spring Text Mining Conference will have access to:

Tracking and reporting adverse events

In recent years, regulatory authorities such as the FDA and EMA have placed an increased emphasis on drug safety of marketed products, particularly the tracking and reporting of adverse events. Pharmaceutical companies are expected to regularly screen the worldwide scientific literature for potential adverse drug reactions, at least every two weeks. The use of text mining and other tools to streamline the literature review process for pharmacovigilance is more crucial than ever in order to ensure patient safety, without overloading drug safety teams.

Manual review of adverse events is time-consuming

Eric Lewis (Safety Development Leader at GlaxoSmithKline) talked at the Linguamatics Text Mining Summit about the challenges of reviewing medical literature for safety signals. For example, he looked for literature for a sample of just 20 marketed products across a 300-day period. Eric found that there were on average 60 new references per day (with a total of over 11,000 documents). He found that manual review time was 1.2 to 1.6 minutes per abstract. He extrapolated this to a typical pharma company product portfolio of 200 marketed products, and showed that this volume of literature would take over 2,200 hours to review – hugely time-consuming.

The 2018 Annual HIMSS Conference & Exhibition takes place next month in Las Vegas, where the Sands Expo Convention Center will be filled with 40,000+ healthcare industry professionals, who have come together from around the world to discuss hot topics in health IT, industry issues and cutting-edge solutions.

For a fourth consecutive year, Linguamatics is proud to be exhibiting at HIMSS, an event that aims to help health IT professionals find innovative solutions to the challenges facing their organizations.

The five-day conference gives attendees the opportunity to educate themselves on ‘what’s new’ and ‘hot topics’ within the industry, and to participate in powerful networking sessions at breakfast networking events and the careers fair.

Linguamatics will showcase their world-leading NLP text mining software, I2E, which has social determinants of health, pathology and other healthcare modules set up to automatically “read” clinical documentation, and rapidly extract relevant elements into discrete values for analysis and effective decision support.