Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
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Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more
Developing IQVIA’s positions on key trends in the pharma and life sciences industries, with a focus on EMEA.
Learn more
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SEARCH JOBSIQVIA recently hosted a customer conference, Fusion 2022, in Clearwater Beach, Florida. Fusion guests had the opportunity to learn how ongoing trends in the life science industry are driving new and innovative strategies across Safety, Regulatory and Quality (SRQ) operations, as well as to interact with and explore game-changing technologies that IQVIA is using to meet the unique needs of safety, regulatory and quality professionals today.
Fusion 2022 also provided a forum for IQVIA to connect with customers and attendees through collaborative discussions on the challenges of today and key trends that will impact the life science industry of tomorrow.
One of the things made clear at Fusion 2022 is that nearly every attending company is exploring the application of artificial intelligence (AI) and machine learning (ML) to power automation. At the same time, regulatory bodies are looking at how this technology can advance healthcare.
The FDA recently published a discussion paper on Machine Learning, and how they can provide the guidance and oversight. The FDA’s paper helps ensure that organizations are approaching the deployment and use of AI in the right way.
When it comes to AI and ML, what companies should do is treat it as a tool. While game-changing, it’s not earth-shattering. It’s a tool that can be used to accelerate capabilities and build consistency and should be treated as such.
The big thing with ML is to demystify it. For example, let’s suppose you’re traveling to work and trying different commute routes. Over time, you build a level of consistency in going from point A to point B, but it varies. If you apply ML to that journey, it can use historic data from your commutes to identify the optimal route and predict how long it will take you each day. That’s what ML is. It analyzes historical data to predict the future.
There are nearly unlimited ways that AI can be used to optimize life science operations. For just a few examples, we’ll address some specific uses of natural language processing (NLP), machine-learned data classifications and intelligent searchable digital media that were discussed at Fusion 2022.
Natural Language Processing
Adverse events for medical devices are reportable to the FDA and regulatory bodies around the world. As companies pull together those reports, there’s a requirement to assign a problem code to the report. Two separate AE descriptions can use different language, wording or context, but still point to the same issue and therefore require the same problem code.
Human analysis of AE descriptions is time consuming and requires the expense of significant human resources. However, NLP can automate the analysis of AE descriptions and eliminate the risk of human error when poring over hundreds or thousands of AE reports. The ability to harvest and analyze the different ways a problem can be described – by leveraging NLP – greatly speeds up the amount of time it takes to analyze AE reports and assign codes with a high level of confidence.
Machine-Learned Data Classifications
As ML algorithms analyze information surrounding treatments and AEs, it learns to make connections between bits of information that are related. For example, if two sources of information list a similar side effect or reaction to a drug, ML can classify those sources under the same description. This makes it easier to compile and review AE indicators and is critical to making digital media searchable and usable by human workers at scale.
Intelligent Searchable Digital Media
Regulatory agencies are expanding the scope of digital mediums that must be analyzed in efforts to ensure product safety. Life science companies are expected to be able to collect, analyze and report on potential AEs regardless of their digital medium – video, social media and online forums. This is simply too much information for human workers to efficiently and effectively work through.
NLP and ML technology can be used to accurately transcribe and analyze potential AE information from web-based sources. Through analysis, NLP and ML can also categorize, classify and flag media that contains relevant AE information for reporting. Categorized media is also filed within a company’s system for increased searchability later on – removing the need to sort through countless files to find the specific information you’re looking for.
Companies consist of people, and people need to be familiar and comfortable with technology. What organizations and regulatory agencies are doing now is building confidence in, comfort with, new uses of ML and AI technology to automate processes. As AI and ML are utilized by companies, it demystifies the technology and sets the stage for exploration of more innovative technologies and uses of AI.
At IQVIA we’re looking at potential uses of AI and automation across life science organizations’ operations. AI holds seemingly unlimited potential to provide benefits ranging from business strategy optimization and supporting customer interactions, to decreasing time and human resource expenditure. IQVIA is excited to be leading the charge in exploring and enabling the use of AI for life science organizations.
For more information on Fusion, visit the Fusion Event page. For additional questions, or to inquire about future Fusion events, please contact fusionevent@iqvia.com.
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