Join our seminar in Rosemont: Connected Natural language processing

NorthShore, in partnership with Linguamatics, is developing a natural language processing (NLP) system to extract important insights hidden in our vast amounts of unstructured data.The initial use case is to identify and extract social determinants of health (SDOH) data. Currently we are in the nascent stages of screening for SDOH needs in a standardized format so our SDOH data is frequently buried in the clinical note text. This data collection format makes it challenging to bring the relevant information to the forefront for care teams to act on. By leveraging NLP to store SDOH data in a structured format, we can feed this information back into our EMR for care team member review and intervention. The pilot program will focus on providing our social workers a list of patients who are in the Emergency Department (ED) with an NLP-identified SDOH need. The social workers will verify the SDOH need with the patient and provide community resources or enroll the patient in care management where applicable. Where previously the social workers may not have interacted with the patient, they will now be able to intervene on any unmet social needs.

For true precision medicine, the importance of deep phenotyping is absolute. With increasing volumes of unstructured data vitally important to understand both the genetic basis of disease, but also the severity and progression of disease, repeatable means to create highly accurate curated features from these unstructured data is essential. In this session we will talk about how IQVIA NLP provides a repeatable and transparent means of accurate computational phenotyping that drives precision medicine. 

Natural Language Processing is NLP is a key component of augmented intelligence in medical chart review, driving predictive analytics and risk stratification in healthcare. From government programs – such as Medicare Advantage Risk Adjustment – where chart review is necessary to ensure accurate capture and reporting of comorbid conditions, to predicting disease progression, there are many applications that are benefitted by bootstrapping chart review with AI. In this session – we will discuss how updates to the IQVIA NLP portfolio are helping healthcare organizations drive efficiencies in these areas.

Our application scientific will be running some demonstration on the different IQVIA NLP products. 

Around 80% of a data scientists time is spent cleaning and preparing their data before it is ready to be processed. For unstructured information, this figure could be even higher when you factor in the lack of schemas, term ambiguity, inconsistent formatting and terminologies, etc. In the session, we will show how IQVIA NLP products can be used to obtain data from free text that has been cleaned, structured and normalized, allow users to spend their time on what matters: solving high-value business problems using the available tools and data. 

From bench to bedside, researchers and clinicians need to base decisions on the best possible view of data. As 80% of data resides in unstructured sources, rapid effective access to the knowledge buried in text documents is essential.  Natural Language processing (NLP) allows organizations to transform unstructured text into actionable structured data that can be rapidly visualized and analyzed, for decision support. We will present how NLP provides a powerful solution to these challenges, with customer use cases ranging from disease understanding to medical affairs.

Powerful text-mining technologies can prove invaluable for everyone from data scientists and technology teams through to scientists and professionals across pharma functions. In this session we will take a closer look at three scalable ways we can put the power of NLP into the hands of these teams in safety, regulatory and medical affairs. The case studies and examples in this session bring to life how text mining scientific literature, drug labels & regulatory documents, social media and news sources can unlock new insights or help teams work more efficiently.  By empowering users to search, compare, extract and analyse relevant rich data that is otherwise buried within text and document sources, teams can focus on applying their expertise and less on sifting through vast data sources for the information they need to get started.