Beyond EDC: Designing a Connected Data Strategy for Predictive Clinical Development - IMS HEALTH HOLDINGS INC

Financial Performance:

• The transcript is a strategic webcast sponsored by IQVIA and does not contain specific financial metrics (revenue, earnings, profit margins, cash flow, debt) or results for a specific quarter.
• IQVIA is noted as having approximately 82,000 employees and operations in over 100 countries.

Guidance and Future Outlook:

• Timeline Reduction: Management predicts clinical development timelines, currently averaging up to 10 years, will shorten if the industry shifts from process augmentation to fundamental operational transformation.
• EDC Adoption: Christina Lentz-Larsen forecasts that within three years, 10% to 20% of clinical trials will be conducted without traditional Electronic Data Capture (EDC) systems.
• Strategic Shift: The goal is to move from reactive data cleaning to proactive, predictive decision-making, aiming for "decision-ready" data within 3–5 years.

Business Segments and Product Lines:

• Digital-First Data Collection: IQVIA is promoting a "digital-first" strategy where data is collected directly from patients via connected devices (DHTs) and ePROs, treating EDC only as a residual collection method for data not captured elsewhere.
• Data Ecosystems: The company highlights its 2020 release of an automated "data ecosystem" designed to integrate 5–10 different digital data sources (e.g., wearables, remote monitoring) into a single, federated environment, moving beyond siloed EDC systems.
• Connected Devices: Jeff Knoll's segment focuses on medical-grade devices (e.g., activity trackers, PEF meters) that enable continuous, passive data collection, eliminating manual transcription for endpoints like vital signs and six-minute walk tests.

Market and Competitive Landscape:

• Industry Trend: Clinical development timelines have increased from 8 to 10 years despite technological advancements, attributed to a failure to fundamentally restructure trial operations.
• Data Volume vs. Quality: The market is shifting away from collecting "data for data's sake" toward prioritizing critical-to-quality (CTQ) factors to avoid redundancy, discrepancies, and increased operational burden.
• Regulatory Landscape: E6(R3) and E8(R1) guidelines are driving the need for study-specific criticality assessment and quality integration during protocol design.

Risks and Challenges:

• Implementation Complexity: Stacking multiple digital technologies can initially slow trials due to the need for system integration, reconciliation, and process re-evaluation.
• Data Discrepancy: Collecting duplicative concepts via multiple sources (e.g., multiple PROs measuring the same endpoint) can create data contradictions that are difficult to resolve.
• Patient Accessibility: Digital adoption faces barriers regarding patient diversity, specifically for geriatric and pediatric populations who may struggle with mobile device interfaces or BYOD (Bring Your Own Device) requirements.
• Cost: High costs of digital health technologies can be rate-limiting for tertiary or exploratory endpoints, necessitating careful cost-benefit analysis.

Management Commentary and Tone:

• Tone: The panel expressed a sense of urgency and conviction, emphasizing that the industry has "augmented" processes but failed to "transform" them.
• Mindset Shift: Speakers consistently stressed that technology alone is insufficient; the industry must shift from defaulting to EDC to making EDC a deliberate choice and focusing on protocol design upstream.
• Confidence: Management demonstrated high confidence in the feasibility of running studies without EDC within three years, provided cross-functional teams collaborate early in the design phase.

Analyst Questions and Answers:

• Q: How is data managed when EDC is removed?
  • A: Management anticipates reliance on upgraded data ecosystem platforms (data lakes) that unify subjective patient data and objective device data for real-time visualization and decision-making.
• Q: Why aren't FDA drug approvals increasing despite automation?
  • A: Panelists noted that while automation and AI are accelerating, the regulatory process often involves a lag, and the industry has been too focused on short-term horizons. They expect approval rates to rise as trial activity increases and process efficiency improves, though the net effect of tech on timelines requires broader operational changes.

Other Key Points:

• Protocol Design: Data collection strategies must be defined during protocol development, not post-hoc, to ensure data is critical for patient safety and study results.
• Cross-Functional Collaboration: Success requires biostatisticians, medical teams, data management, and digital health experts to agree on CTQs before trial execution begins.
• Real-Time Monitoring: Digital-first approaches allow for real-time adherence monitoring (e.g., flagging patients who fail to submit digital diaries), reducing the need for post-study cleaning.
• Future Proofing: IQVIA is positioning itself as a guide for sponsors to navigate these complex ecosystem transitions, offering expert consultation to define data needs and collection methods.