Thriving in uncertainty: real-world data as the foundation for life sciences resilience
The life sciences industry is undergoing profound transformation, marked by regulatory evolution, pricing reform, technological disruption, and changing patient expectations. In this Guest Column, Broderick Jones (Senior Vice President, Customer Delivery, Insights Business Unit, Inovalon) discusses how real-world data (RWD) can serve as a stabilizing foundation amid this uncertainty. By leveraging robust, longitudinal evidence, organizations can anticipate emerging risks, enhance clinical development and market access strategies, and strengthen their capacity to respond with agility and confidence in an increasingly complex global environment.
Navigating a time of unprecedented change
The life sciences industry has never been short on complexity, but today’s environment brings a new level of instability. From evolving regulatory dynamics and global pricing reform to AI disruption and shifting consumer expectations, uncertainty has become the norm rather than the exception.
This volatility has tangible consequences. An estimated 40–60% of recent pharmaceutical launches have underperformed financially (Deloitte, 2023), underscoring the urgent need for data-driven strategies that allow organizations to act quickly and confidently.
RWD offers a foundation for resilience. When harnessed effectively, it enables life sciences companies to anticipate change, understand real-world patient needs, defend product value, and drive innovation in ways that traditional approaches cannot.
The four forces of uncertainty
Uncertainty in healthcare arises from four interconnected domains:
- Policy and regulatory shifts
- Market fragmentation and access pressures
- Technology acceleration and data complexity
- Consumer behavior and societal change
Each domain influences the others, creating a cycle of disruption that requires proactive, data-powered responses.
Policy and regulatory shifts are creating unpredictability at every level. Delayed FDA guidance on AI and devices, potential reinstatement of the “Most Favored Nation” drug pricing policy, and possible cuts to opioid recovery programs exemplify the volatility at play. Such policy changes could disproportionately affect Medicaid and Medicare populations, where opioid use disorder diagnoses are significantly higher 2.6–3.1% for Medicaid and 2.0–2.2% for Medicare Advantage, compared with 0.4–0.6% for commercial plans.
Meanwhile, market fragmentation, driven by biosimilar competition, patent expiries, and tariff uncertainty, has heightened pressure on access and pricing. Proposed import tariffs of up to 200% could increase manufacturing costs and limit availability (J.P. Morgan, 2025), while persistent supply-chain fragility adds risk. Companies now require granular, community-level visibility into patient access and payer dynamics to manage these pressures effectively.
Technology acceleration brings both promise and risk. The FDA’s cderGPT pilot demonstrates progress in AI-based drug review but also exposes ongoing uncertainty around validation and accountability. Meanwhile, cybersecurity breaches have compromised the data of up to 190 million individuals, revealing critical weaknesses in healthcare infrastructure and emphasizing the importance of strong governance.
Finally, consumer and societal shifts are redefining expectations. Telehealth and direct-to-consumer platforms are transforming care delivery and accelerating trends such as off-label GLP-1 use among younger, commercially insured, and predominantly female patients. Generational differences further complicate engagement: younger cohorts expect seamless digital care, while older patients prefer traditional provider-led models.
A converging challenge – and a data-driven solution
These forces do not act independently. A cybersecurity breach can stall digital trials, a regulatory shift can destabilize pricing models, and evolving patient behavior can disrupt engagement strategies. In this environment, resilience demands agility,and that agility depends on data.
RWD brings together claims, electronic health records (EHR), specialty pharmacy, and social drivers of health (SDOH) to provide a connected, longitudinal view of healthcare realities. This breadth enables organizations to detect patterns, anticipate risks, and respond dynamically across the value chain, from drug discovery and clinical development to HEOR, market access, and commercialization.
Clinical development: data-powered precision
Clinical trials remain one of the most resource-intensive and unpredictable phases of the drug lifecycle. More than 11% of trial sites fail to enroll any patients, and 55% miss their enrollment targets (Lamberti et al., 2024; Hung et al., 2024), contributing to costly delays and lost opportunities.
RWD helps overcome these barriers. By using claims and EHR data, sponsors can build synthetic control arms, inform hybrid trial models, and identify eligible populations earlier.
Tools such as Inovalon’s Clinical Research Patient Finder™ advance this approach, automating EHR-based pre-screening to reduce site burden and improve recruitment diversity. Looking forward, AI-driven platforms could further enhance trial design, site selection, and patient engagement through predictive models that minimize burden and personalize retention strategies.
HEOR and market access: strengthening evidence and equity
HEOR and market access teams face uncertainty from pricing reform, fragmented HTA requirements, and reimbursement variability. Traditional methods often fall short of the speed and granularity required to navigate these challenges.
Longitudinal claims and EHR data reveal treatment effectiveness, total cost of care, and utilization trends. When combined with SDOH, these insights contextualize value, expose disparities, and help teams align evidence with payer priorities.
Several examples illustrate the impact:
- Advanced Parkinson’s disease: RWD showed that Medicare beneficiaries often faced low utilization of device-aided therapies despite geographic availability. Barriers like distance, race, and socioeconomic status highlighted equity gaps.
- Hyperkalemia in long-term care: Medicare data revealed high recurrence and cost burden among patients in long-term care, identifying opportunities for contracting and cost containment.
- CAR T vs non-CAR T therapies: Real-world analyses in relapsed/refractory mantle cell lymphoma found that non-CAR T regimens were linked to earlier progression and higher subsequent therapy costs, supporting earlier adoption of innovative therapies.
Beyond specific therapies, real-world evidence is reshaping policy perspectives. A recent Inovalon-Harvard analysis comparing Medicare Advantage (MA) and Fee-for-Service (FFS) at age 65 revealed meaningful differences in cost, utilization, and outcomes even after adjusting for sociodemographic and clinical factors—challenging prior MedPAC assumptions and informing future program design.
Commercial and patient engagement: from insight to impact
Nearly half of new drug launches underperform, placing greater pressure on commercial and patient-facing teams. RWD transforms fragmented datasets into actionable insights that support micro-targeting, predictive adherence modeling, and personalized engagement.
By combining claims and SDOH data, organizations can segment prescribers, tailor messaging, and identify at-risk patients through patterns in medication fills, clinical triggers, or social risk factors. Dynamic forecasting grounded in real-world response rates improves launch planning and territory optimization, while omnichannel strategies ensure relevance across generations.
The next evolution links commercial strategy directly with patient care—creating ecosystems where engagement drives both business growth and patient well-being.
The new frontier: AI, harmonization, and agility
Innovation in life sciences now extends beyond molecular discovery to every stage of product development and delivery. The next decade will be shaped by three imperatives:
- AI + RWD synergy: Out of 519 studies reviewed, only 5% used real patient-care data to evaluate large language models (JAMA, 2023). Integrating RWD ensures AI tools are clinically relevant, diverse, and representative.
- Data harmonization: Integrating EHR, claims, genomics, and patient-reported outcomes into a unified intelligence layer is essential to unlock their full value.
- Organizational agility: The most resilient companies are those embedding data literacy and cross-functional collaboration into their culture—learning faster, acting smarter, and scaling what works.
Turning complexity into clarity
The life sciences industry will continue to evolve, not stabilize. The organizations that thrive will be those that use RWD and AI to inform every decision, from trial design to patient engagement.
By anticipating market shifts, personalizing strategies, and aligning evidence with payer and patient needs, data-driven companies can turn uncertainty into opportunity.
This is the blueprint for a resilient, adaptive, and equitable life sciences ecosystem. The future belongs to those ready to embrace it.
Author
Broderick Jones
Senior Vice President of Customer Delivery, Inovalon
Broderick Jones is Senior Vice President of Customer Delivery within Inovalon’s Insights Business Unit. He leads a team that designs and implements solutions generating deeper insights across commercial, clinical, and health economics domains. His expertise includes AI-driven innovation, strategy development, and operational transformation across the healthcare value chain.
Acknowledgments
Special thanks to contributing authors Kelly Birch, MPH; Laura Greene; Andrew Li; Iman Mohammadi; Nils Nordstrand; and Virginia Noxon-Wood for their collaboration and insights that informed this article.
Sponsorship for this Guest Column was provided by Inovalon, Inc.