Introduction

The pharmaceutical industry is undergoing a significant transformation, driven by the increasing importance of real-world evidence (RWE) in driving innovation and improving patient outcomes. A recent panel discussion featuring industry leaders highlighted the key challenges and opportunities in leveraging RWE to accelerate the development and commercialization of new treatments.

Breaking Down Silos and Empowering Teams

One of the primary challenges in leveraging RWE is the existence of silos within organizations, which can hinder the generation and use of insights.

According to Lori Lebson, Senior Vice President for EMD Serono, "Empowering teams to generate insights and break down silos requires a cross-functional collaborative process, where everyone feels their voice is heard and valued." This can be achieved by establishing governance and charters early on in the process and ensuring that all stakeholders are aligned and working towards a common goal. As Lebson noted, "It's about bringing that collective mindset together, so that when we're thinking through what is it that we need for phase one, for phase two, for phase three, we've already built that roadmap and everyone feels empowered to then go and execute on a cohesive plan."

Collaboration and Partnerships

Collaboration and partnerships are essential in leveraging RWE, particularly when it comes to working with external providers. Sunil Dravida, Global Head of the Real World Data Center of Excellence at Takeda Pharmaceuticals, emphasized the importance of centralizing real-world data assets and having a centralized infrastructure to support data integration and analysis. He also highlighted the need for partners who can provide patient-level data and have a good tokenization strategy in place. As Dravida noted, "I want to make sure that the data is accurate and it represents the population, and I want to make sure there is encryption at rest as well as in transit, and they are complying with HIPAA and GDPR."

The Role of Emerging Technologies

Emerging technologies, such as natural language processing (NLP) and machine learning, are playing an increasingly important role in leveraging RWE. Lori Lebson noted that NLP has been particularly useful in identifying patients with rare diseases, where traditional diagnostic criteria may not be effective. She also highlighted the potential of these technologies to accelerate the development of new treatments and improve patient outcomes. As Lebson noted, "NLP has come to the rescue, to help us identify patients, create cohorts, identify likely patients that when you think about bringing more patients down the funnel, identifying these in data sets and querying them as potential diagnoses that we might want to look into."

Pull-Through and Field Engagement

Pull-through and field engagement are critical components of leveraging RWE, particularly when it comes to ensuring that insights are translated into action. According to Lori Lebson, "Planning for pull-through starts even before you actually start building your evidence plan." This requires a deep understanding of the needs of field teams and the development of tailored solutions that meet those needs. As Lebson noted, "The sales rep is probably going to have five-minute stops to show probably a couple of sentences and have a conversation, but if there is enough of a hook built into that, it opens the door for the MSL to then walk in and have a deeper scientific conversation."

Rare Disease and Real-World Evidence

Real-world evidence plays a critical role in accelerating the diagnosis and treatment of rare diseases. Liana Hennum, US HEOR Lead at BridgeBio, noted that "lack of awareness is a major challenge in rare disease, and real-world data can help identify pockets of underperformance and guide better treatment and identification efforts." The use of digital tools, such as wearables, can also help to identify patients with rare diseases and accelerate diagnosis and treatment. As Hennum noted, "We've done some mapping of variation, geographic variation in prevalence rates to centers of excellence, and you see the trend that the farther these patients are away from a center of excellence, the lower the prevalence is."

Addressing Gaps in Claims Data

One of the challenges in leveraging RWE is addressing gaps in claims data, particularly when it comes to outcomes that cannot be pulled from claims data. Sunil Dravida noted that "claims data in itself might not answer all the questions, so that's why we want to supplement that with good EMR data, both structured and unstructured." He also highlighted the importance of partnering with vendors who can provide patient-level data and have a good tokenization strategy in place. As Dravida noted, "If you have a vendor that tokenizes the data, you can integrate multiple data sets and have a better view of the patient journey."

The Future of Real-World Evidence

The future of RWE is exciting and rapidly evolving, with emerging technologies and innovative partnerships driving change. As Chaitanya Badwe, AVP of Insights and Analytics at Definitive Healthcare, noted, "There are ways to do data integration without going full-fledged tokenization, by superimposing data sets and basically bypassing the tokenization in a way with the right sort of regulation, with the right HIPAA controls in place." This approach has the potential to accelerate the development of new treatments and improve patient outcomes, particularly in rare diseases.

Key Takeaways

1) Breaking down silos is critical: Empowering teams to generate insights and break down silos requires a cross-functional collaborative process.

2) Collaboration and partnerships are essential: Centralizing real-world data assets and having a centralized infrastructure to support data integration and analysis is critical.

3) Emerging technologies can accelerate innovation: NLP and machine learning can help identify patients with rare diseases and accelerate the development of new treatments.

4) Pull-through and field engagement are critical: Planning for pull-through starts even before you actually start building your evidence plan.

5) Real-world evidence can drive rare disease diagnosis and treatment: Real-world data can help identify pockets of underperformance and guide better treatment and identification efforts.

In conclusion, the panel discussion highlighted the critical role that real-world evidence plays in driving innovation and improving patient outcomes in the pharmaceutical industry. By breaking down silos, collaborating with partners, leveraging emerging technologies, and ensuring effective pull-through and field engagement, organizations can unlock the full potential of RWE and drive meaningful change in the industry. As Lori Lebson noted, "Evidence is only as good as its utility in the field to influence decision making and move hearts and minds." By prioritizing the use of RWE and driving innovation, the pharmaceutical industry can create a brighter future for patients and transform the way healthcare is delivered.

"Patients are really one of the best sources of data and insights about their own health. By going directly to individuals, you can reveal the full patient experience in quite a different way."

Traditional real-world data often comes from episodic healthcare system interactions, creating significant gaps when patients aren't regularly engaging with providers. Evidation's approach bridges these gaps by creating what they call "behavior grams" – layered visualizations combining passive data collection from wearables with active patient-reported information.

The Power of Layered Data

Wilberforce demonstrated this approach through several examples, including a 65-year-old woman with type 2 diabetes. Her behavior gram combined wearable data tracking BMI, steps, aerobic activity, sleep duration, heart rate variability, and glucose levels from a continuous glucose monitor. This passive data collection was supplemented with validated questionnaires measuring depression and anxiety.

This layered approach revealed valuable insights about the patient's health journey, including her switch to a GLP-1 medication and subsequent weight loss, followed by participation in a digital health coaching program. The combined data showed corresponding decreases in glucose levels that might not be captured in traditional healthcare records.

"Some of this data, especially today with how people are accessing GLP-1s, doesn't exactly always exist in your EHR data with the prevalence of people getting GLP-1s prescribed from Noom and hims and hers or from telehealth providers," noted Wilberforce. "This is a really different method to try and pull the full picture together."

Bridging Data Sources

The approach doesn't replace traditional healthcare data but rather complements it. Wilberforce emphasized that policy and technology changes over the past decade have empowered individuals to mediate and control their health data sharing.

"Ten years ago, most patient data from the healthcare system was largely locked in the healthcare system," she said. "The really important shift over the last number of years, through policy, through technology and a number of other things, is that there's many more ways for individuals to mediate or choose who their data gets shared with."

This patient-mediated approach allows researchers to address data gaps by going back to individuals with targeted questions. It also enables personalized health interventions at precisely the right moment, potentially improving outcomes through better timing and contextualization.

Case Studies Demonstrating Value

Wilberforce shared three case studies illustrating the practical applications of this approach:

1) Measuring medication impact on alertness: A study of 500 individuals taking OTC allergy medication combined surveys, vigilance tests, and wearable data to demonstrate improved attention and reduced sleepiness on days when medication was used, with no impact on activity levels or heart rate.

2) Understanding GLP-1 medication usage: Within a weight management cohort of 140,000 people, Evidation discovered that 40% of GLP-1 users obtained prescriptions from providers other than their primary care physician – information likely missing from traditional EHR data. The study also revealed that depression scores (measured by PHQ-8) decreased while patients were on GLP-1 therapy and slightly increased after discontinuation.

3) Early detection of respiratory illness: Using machine learning algorithms analyzing wearable data patterns from 200,000 individuals, Evidation identified over 28,000 instances of early flu-like symptoms. This early detection enabled timely interventions and facilitated enrollment in a clinical trial testing therapy to reduce household flu spread.

"The challenge our partner was having there was like, this is really hard to enroll because by the time the person shows up at the provider, they've probably been sick for a few days and they're outside of the early onset window where we actually need to get them the drug," Wilberforce explained.

Building Trust and Engagement

Critical to Evidation's success is their approach to privacy and user control. The company established early principles that users control and own their data, with transparent consent processes for how data is used. This foundation of trust enables them to collect increasingly sensitive health information.

User engagement is maintained through both extrinsic motivators (rewards and gamification) and intrinsic value (providing insights and research participation opportunities). This has allowed Evidation to build a community of 5 million people across 97% of U.S. zip codes, with over a billion data points collected daily.

Five Key Takeaways

1) Direct patient connections reveal health insights not captured in traditional healthcare data – "By going directly to individuals, you can really reveal this full patient experience in quite a different way," as Wilberforce emphasized.

2) Layering multiple data sources creates a more complete picture of an individual's health journey, with passive wearable data and active surveys contextualizing each other.

3) Patient-mediated data sharing bridges critical gaps in healthcare records, particularly for treatments obtained outside traditional healthcare settings like GLP-1 medications prescribed through telehealth.

4) Early detection algorithms can identify health changes before symptoms become severe, enabling timely interventions that improve outcomes and research opportunities.

5) Building trust through transparency and user control is essential for successful data collection: "If people didn't trust us to be good stewards of their Fitbit data, they would never trust us to be good stewards of electronic health record data or any more sensitive types of data."

"A partnership of 45 members might seem too large, but I've noticed emerging partnerships across sectors that quickly address issues that previously would take longer."

The Venture Capital Perspective

Haider Alleg, General Partner at Allegory Capital, brings a venture capital perspective to the discussion. He emphasizes that the biotech investment model cannot simply be applied to digital health: "We tend to copy in digital health the biotech model, like 'let's build something that Novartis wants and Novartis will buy it.' And that doesn't happen."

Alleg sees tremendous potential in digital health, which he describes as "the last frontier of regulated industry where anything that we put at it is amazing." However, he notes that startups often struggle to scale due to the high cost of patient acquisition, suggesting that pharma companies could play a more significant role in helping these companies reach millions of patients.

Building Partnerships That Last

The panelists agree that successful partnerships require clear alignment on objectives from the outset. As Richard Cassidy advises:

"Look at all those partnerships you have, and if you can't link them back to your business goal and your partner's business goal, stop doing them."

For Nipun Jain, the key challenge is "aligning what is in it for everyone" and ensuring that startups understand that pharma companies are not the ultimate customers for their digital health solutions.

Adrian Gheorghiu emphasizes the importance of governance and shared goals in sustaining large consortia, while Haider Alleg calls for better connection between corporate venture capital in pharma and commercial objectives.

Key Takeaways:

1) "The science is embedded in the industry. The more there will be VCs coming up pouring investors' money and de-risking your initiatives, the more we are going to impact patients." - Haider Alleg

2) "If you can't link partnerships back to your business goal and your partner's business goal, stop doing them." - Richard Cassidy

3) "Digital health is the great equalizer where technologies can provide that edge to healthcare systems to cover the gap." - Nipun Jain

4) "Stay truthful to health impact. The collaborations can grow around that, and whatever side effects like making money or building reputation will come as well." - Adrian Gheorghiu

5) "Partnership is driven by looking at market forces and dynamics and being more proactive, absorbing risk in a different way compared to how big pharma operated in the past." - Adrian Gheorghiu

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