Life Sciences’ Next Move into AI with Data

“Patient-centricity” is emerging as one of the biggest buzzwords in life sciences today, but shouldn’t the criticality of putting patients first be self-evident? After all, people are the primary reason for the development, delivery, and consumption of medicine and treatments. The industry has traditionally struggled with making patient-centricity a reality. In fact, North Highland's research shows that only 21 percent of healthcare and life sciences leaders said they feel very prepared for customer-centricity, highlighting lack of knowledge and skills as a key obstacle.

Artificial intelligence (AI) is rewriting the story around patient-centricity for life sciences and pharmaceutical organizations.

Emerging AI capabilities, fueled by an abundance of structured and unstructured data points, are driving deeper insights into the voice of the patient. It is through this insight that organizations have an opportunity to improve understanding of patient populations and identify the patterns that can improve human health on a global scale.

Life sciences is undoubtedly familiar with the changes brought by the digital era. Technologically-enabled advancements including precision medicine and the generation of real-time insights to reduce failure rates in clinical trials have introduced new opportunities for the industry.

As the industry explores new applications of technology, AI requires organizations to fundamentally rethink the way their data is configured. Data in the industry today is often siloed, and connecting data across these silos takes time—ultimately compromising speed to insight. In addition, opportunities for finding patterns and correlations that can advance human health and quality of life may be missed—ultimately undermining the higher-level purpose of patient-centricity.

Data systems, when connected in the right way, enable the automation and execution of data cleansing and profiling across disparate data sources, facilitating more seamless decision-making across the healthcare ecosystem. Examples of how data-enabled AI and cognitive services can power patient-centricity include:

  • Improved data visualization and storytelling. Enhancements here can help healthcare practitioners provide better, more immersive explanations of medication benefits to patients and more quickly highlight obstacles to supply and gaps in insurance coverage.
  • Real-time insights. Deeper understanding of customer experiences can help healthcare practitioners adjust dosages or suggest an alternative course for treatment.
  • Enhanced journey mapping. By enabling healthcare practitioners to more directly visualize a day-in-the-life of a patient, they can course correct for the care and experiences they provide.

For life sciences organizations, AI-ready data should embody the following characteristics.

  • Described: embed context and relevancy into data sources so machines can help humans make meaningful use of data
  • Accessible: AI must have programmatic access to data sources, databases, APIs, and sensors
  • Connected: We disconnect data from its context when we collect it. Humans must reconnect data stored in silos to enable AI to solve complex problems

Technology is never just technology. Of equal importance is a focus on cognitive ethics to ensure that organizations apply AI in a way that fosters trust, acceptance, and meaningful adoption—all core fundamentals as organizations seek to use AI to enable an authentic connection with the voice of the patient.

Life sciences and pharmaceutical companies: the future of patient engagement is yours to build. The patients’ voice is growing louder on social media platforms and in other online communities by the second. By building the AI mechanisms to capture patient insight—and the data infrastructure to support it—life sciences organizations will be uniquely positioned to not only hear this voice – but make sense of it and action upon it.