Natural Language Processing and Social Listening to Increase Patient-centricity in Life Sciences
Natural Language Processing and Social Listening are transforming various aspects of modern business operations. Artificial intelligence (AI) has seeped into nearly every professional field, improving workflows and providing access to unprecedented amounts of data – but for life science, we’re still testing the waters with AI applications. Historically, the life science industry is cautious about adapting new technology, and as a result, tools like AI can seem intimidating.
Life science experts are beginning to understand how AI applications suit essential industry functions, such as within the insights gathering process. As insights gathering expands to include more channels, our industry is experiencing a significant evolution of the patient voice. Patients are more aware of products on the market, are more curious about how new products can help them, and are passionate advocates for their own health.
In theory, that’s great news.
But as the patient voice becomes more amplified, life science teams wonder how to keep pace with information overload and ensure they’re tapping into the right information. As online channels proliferate, patients turn to a variety of outlets to share their experiences with new treatments. This may be a net benefit for patients, but it increases the spaces where pharma professionals can mine insights and information. Life science teams must build social listening strategies to use this deluge of data, but to do so, they’ll need technology.
Applying AI to this step in the drug development process ensures that teams don’t miss essential insights and allows for a more directional review of large volumes of information. For pharma teams, the result is actionable insights that can help drug and device companies better understand their audience and develop more beneficial products.
Here’s a look at how natural language processing and social media listening are making the life science listening process smarter and more productive.
Natural Language Processing
Natural language processing (NLP) is an AI application that can monitor conversations to identify trending concepts and inform strategic decisions. Pharma teams can facilitate a more in-depth, analytical overview of the intent and sentiment within multiple information streams.
NLP can eliminate some of the manual processing tasks associated with collating and interpreting information, reducing the risk of missing essential information, such as key side effects patients experience. This capability allows pharma teams to truly understand the conversation around various stages of product development, ultimately providing more effective treatments for patients, a significant value-add for pharma teams.
Social Media Listening
Social media can also serve as a powerful insight-gathering tool for pharma teams. Patients often take to social media platforms to share unfiltered experiences with treatments and conditions in terms they may not get the opportunity to use in traditional patient advocacy groups. These candid posts give providers a deeper understanding of how treatments impact patients than what they may receive in traditional healthcare settings.
At times, life science companies don’t quite live up to their patient-centricity goals, because biases like general mistrust of healthcare professionals or language barriers can make equitable participation difficult. Nonetheless, providers still need to obtain these valuable insights – social listening allows teams to hear directly from patients themselves without being filtered through physicians or a reluctance to directly disclose sensitive personal experiences.
Social media listening or monitoring allows pharma teams to understand patients’ needs in an unfiltered way. When teams use social listening to analyze online discussions, teams can understand the patient voice in ways they may not be able to in traditional settings. This tool allows life science teams to bring patient voices into the drug development process sooner, making interventions quicker and drugs more tailored to patients’ needs.
What’s Next for AI and the patient voice?
Despite this significant growth potential of AI, authentically capturing patients’ voices will remain a significant challenge for life science teams. In an era of information overload with patients finding new channels for engagement faster than we can keep up with, AI will be vital for teams to develop drugs for the needs of modern patients. These AI applications can ensure that important insights don’t fall through the cracks, saving time and giving added security to the decisions they make.
It’s important that we embrace new AI tools so that the life science industry can continue delivering high-quality treatments and effective patient care. Further, as life science and pharma teams seek out new ways to keep up with patient insights, automating processes with new AI tools presents a compelling solution. As artificial intelligence continues to find new applications in business, you can expect to see more life science teams apply AI to better understand the patient voice.
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