IQVIA recently hosted a customer conference, Fusion 2022in Clearwater Beach, Florida. Fusion guests had the opportunity to see how ongoing trends in the life sciences industry are driving innovative new strategies across Safety, Regulatory, and Quality (SRQ) operations, as well as interact with and explore the game-changing technologies IQVIA uses to meet the unique needs of today’s safety, regulatory, and quality professionals.
Fusion 2022 also provided a forum for IQVIA to engage with customers and attendees through collaborative discussions on today’s challenges and key trends that will impact the life sciences industry of tomorrow.
Life Sciences Companies Explore Artificial Intelligence and Machine Learning
One of the things demonstrated at Fusion 2022 is that almost every company present is exploring the application of artificial intelligence (AI) and machine learning (ML) to power automation. At the same time, regulators are looking at how this technology can enhance healthcare.
The US Food and Drug Administration recently published a dossier Discussion paper on machine learningand how they can provide guidance and supervision. The FDA paper helps ensure that organizations approach the deployment and use of AI in the right way.
Demystifying AI and Machine Learning
When it comes to artificial intelligence and machine learning, what companies should do is treat it as a tool. While it is a game-changer, it is not earth-shaking. It is a tool that can be used to accelerate capabilities and build consistency and should be treated as such.
The important thing about ML is to demystify it. For example, let’s say you travel to work and try different ways of commuting. Over time, you can build a level of consistency in getting from point A to point B, but it varies. If you apply ML to that trip, it can use historical data from your commute to determine the optimal route and predict how long it will take each day. That’s what ML is. Analyzes historical data to predict the future.
The uses of artificial intelligence and machine learning in life science operations
There are almost limitless ways in which AI can be used to improve life sciences processes. Just for a few examples, we’ll go over some of the specific uses of Natural Language Processing (NLP), machine-learned data labels, and intelligent searchable digital media discussed at Fusion 2022.
natural language processing
Medical device adverse events are reported to the Food and Drug Administration and regulatory agencies worldwide. Since companies collect these reports together, there is a requirement to set a trouble code for the report. Two separate AE descriptions can use different language, syntax, or context, but still refer to the same problem, and therefore require the same problem code.
Human analysis of AE descriptions is time consuming and requires a significant human resource expense. However, NLP processing can automate the analysis of AE descriptions and eliminate the risk of human error when searching through hundreds or thousands of AE reports. The ability to harvest and analyze the different ways in which a problem can be described – by making use of NLP – greatly speeds up the amount of time it takes to parse AE reports and assign codes with a high level of confidence.
Auto-learned data labels
As ML algorithms analyze information surrounding treatments and AEs, they learn to make connections between related pieces of information. For example, if two sources of information list a similar side effect or reaction to a drug, machine learning can classify those sources under the same description. This makes it easy to compile and review AE indicators and is critical to making digital media widely searchable and usable by human operators.
Searchable smart digital media
Regulatory agencies are expanding the range of digital media that must be analyzed in efforts to ensure product safety. Life sciences 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 operate efficiently and effectively.
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 classify, categorize, and flag media containing relevant AE information for reporting. Labeled media is also saved within the corporate system to increase searchability later – eliminating the need to sort through countless files to find the specific information you’re looking for.
Embracing AI and automation is preparing life sciences companies for the next wave of innovation
Businesses are made up of people, and people need to be aware of and comfortable with technology. What organizations and regulatory agencies are doing now is building confidence and comfort in new uses of ML and AI technology to automate processes. As companies use AI and machine learning, it demystifies technology and paves the way for exploration of more innovative technologies and uses of AI.
At IQVIA, we investigate the potential uses of AI and automation across the operations of life sciences organizations. AI has seemingly unlimited potential to provide benefits ranging from improving business strategy and supporting customer interactions to reducing time and human resource expenditure. IQVIA is pleased to lead efforts to explore and enable the use of AI for life sciences organisations.
For more information about Fusion, visit fusion page. For more questions, or to inquire about future Fusion events, please contact fusionevent@iqvia.com.