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Pharma’s Digital-Trust Mandate

Pharmaceutical ExecutivePharmaceutical Executive-08-01-2020
Volume 40
Issue 8

The need for clear transparency around use of real-world data, AI.

A powerful way to think about “digital transformation” in the biopharma sector, and how it might impact our environmental, social, and governance (ESG) strategies, is to focus on two key ideas: real-world data and AI. These new and potentially disruptive technologies are being rapidly adopted throughout biopharma and healthcare, so it’s time for some perspective and big-picture thinking.

Sandor Schoichet

Real-world data is something new: detailed, personalized, real-time, mobile, continuous, and accessible. It’s not just “big,” it’s real.Ubiquitous mobile personal devices and internet connectivity allow real integration of daily life with online services and databases.

AI provides the new tools needed to exploit real-world data in ways that are particularly useful for biopharma and healthcare.While AI is a broad category, the core capability is sophisticated pattern recognition and forecasting, leveraging real-world data for learning. Biology and medicine are inherently data-rich, requiring sophisticated analysis and nuanced communication among patients, providers, and developers. Given the scarcity of human expertise and the scale of global demand, AI tools and the real-world data enabling them are crucial infrastructure for the future of healthcare.

For an example of these ideas coming together in a transformative way, think about the humble pill. Pills are old-school: stand-alone products with tens of millions of dollars of R&D behind them, but little guidance for how to use them (ever read a package insert?), and very limited feedback to developers or physicians about how well they’re working. A hallmark of modern products, in contrast, is that they’re the tip of an integrated system in the background, adding value for many stakeholders. Instead of a pill, think about a phone, and the network of cell towers, GPS satellites, databases, and AI systems that make it useful—that close the loop between users and providers. This is a model for the future of personalized medicine, public health monitoring, and improved healthcare services generally. We can see this future already in clinical trials with instrumented drug dispensers, personal data collection, and continuous medical oversight.

Transformation creates opportunity and risk. Last June, I moderated a Biopharma Sustainability Forum on real-world data and AI, which began exploring opportunities and risks of concern to biopharma companies, and potential impacts on our license to operate. Key forum contributors included Hannah Darnton (BSR), Alex Walden (Google), and Dominik Geller (Sanofi).

We quickly learned that there’s a lack of clarity on how the idea of digital transformation connects with biopharma sustainability concerns, but a few urgent areas for follow-up were apparent. First, firms need to think deeply about their responsibility when working with sensitive personal medical and behavioral data. Building public trust on this front goes far beyond the baseline requirement for cybersecurity. How can we enable meaningful informed consent for real-world data collection at scale, manage privacy and access controls, and comply with burgeoning national and international regulation? What data quality systems are needed to move beyond the limited world of clinical trials, where this is already a challenge?

Ethical and governance challenges are also in urgent need of fresh perspectives. For AI systems, bias and incompleteness in training data are concerns that require active attention to discrimination, inclusivity, and social assumptions. Decision-making transparency is also a problem for most current AI systems, one that must be addressed for patients and providers to understand and trust recommendations. The question of autonomy is also at play—how independent should AI systems be, and how should human medical expertise be incorporated? Finally, most companies do not have the integrated governance structures needed to address both the hard technology and the softer ethical and strategic issues.

Biopharma companies depend on public trust, or a social license to operate. Both real-world data collection and AI-based applications will be increasingly public-facing. Consider, as an example, the COVID-19 contact tracing systems now being deployed. These new applications impact almost every high-priority ESG topic that biopharma companies and investors care about, from ethics and compliance, through trial practices, patient safety, and equitable access, to innovation and risk.

Sanofi has started addressing some of these concerns by defining Guiding Principles for AI, and others are putting their thoughts together too. It’s time for a wider discussion to build on these efforts. Digital transformation is coming, and we need to define a more coherent industry perspective if we are to reap the benefits. 

Sandor Schoichet, Director, Meridian Management Consultants, and Co-Founder of the Biopharma Sustainability Roundtable. He can be reached at sschoichet@meridianmc.com.