3 Data Trends Shaping Clinical Research and RWE in 2026 and beyond

Life Sciences is at an inflection point.

Real-world data (RWD) and AI have progressed from pilot programs and proof-of-concept experimentation to core components of clinical development and evidence generation. As these capabilities mature, competitive advantage is determined less by data access alone and more by the ability to operationalize it. Organizations who do this see greater success in transforming analytics into decision-grade evidence, compressing development timelines without sacrificing rigor, and aligning clinical strategy with downstream payer and market expectations.

At SCOPE Summit 2026, conversations with clinical, data, and operational leaders underscored a clear shift underway in life sciences: the focus is moving from whether RWD and AI belong in clinical development to how to deploy them with discipline, scale, and measurable impact. Three themes consistently surfaced in these discussions, signaling where evidence strategy is headed next.

1) Linked, fit-for-purpose datasets are becoming the standard for decision-grade RWE

As the adoption of RWD has become widespread across clinical development and evidence generation, expectations are evolving around how that data is selected, connected, and validated for a specific use case. Access to isolated datasets is rarely sufficient on its own; the differentiator lies in whether organizations can assemble the right data at the right time to inform critical decisions.

Additionally, regulators, HTAs, and payers are placing greater emphasis on data provenance, longitudinal completeness, and analytic rigor ¹. Knowing that data volume alone can’t meet this demand, sponsors are responding with more structured frameworks to ensure that evidence is complete, traceable, and grounded in data that can withstand scrutiny in both regulatory and access environments.

A fragmented healthcare system means no single dataset captures the full patient journey, making secure connectivity central to assembling evidence that is truly fit for purpose.

How Datavant is working with teams:

• Evaluating not just dataset quality, but linkage readiness and cross-source completeness.
• Connecting EHR, claims, mortality, registry, and SDOH data to reduce outcome gaps.
• Aligning RWD strategy directly to regulatory submissions, label expansions, and market access endpoints.

Where value is showing up:

• Stronger regulator and payer confidence, as connected, well-documented data reduces evidentiary gaps and shortens clarification cycles during review through privacy-preserving linkage across distributed data sources.
• Fewer downstream surprises, with early identification of coverage and linkage gaps preventing costly supplemental analyses and mid-course corrections.
• Higher return on data investments as resources shift from exploratory aggregation to connected, fit-for-purpose evidence that directly influences approval and reimbursement decisions.

Now that fit-for-purpose rigor is the standard, secure data connectivity and privacy-preserving linkage are imperative to transform fragmented RWD into complete, defensible evidence ecosystems.

2) AI is scaling evidence generation — but governance and interpretability are the differentiators

AI and machine learning are expanding how life sciences teams analyze high-dimensional RWD. Techniques ranging from confounding adjustment and causal inference to AI-enabled clinical data abstraction from unstructured notes are increasing both the scale and sophistication of evidence generation. 

As these methods become more widely used in development programs, value depends not only on analytical capability, but on whether outputs can be explained, reproduced, and defended in high-stakes decision settings.

Regulatory and payer audiences expect transparency around model selection, bias mitigation, validation, and documentation ². In response, AI is increasingly embedded within governed, traceable workflows that align with established evidence frameworks.

How Datavant is working with teams:

• Building controlled analytics pipelines with audit trails from raw data to reported outcome.
• Using AI-assisted tools to accelerate programming, literature synthesis, and non-interventional study reporting.
• Creating reusable algorithm libraries and standardized code lists to ensure consistency across studies.

Where value is showing up:

• Shorter analysis and reporting cycles as AI-assisted programming and automated validation reduce turnaround time without compromising quality control.
• Improved reproducibility and review readiness supported by standardized workflows and documentation that streamline internal QA and external regulatory interactions.
• Scalable productivity across global teams with reusable libraries and governed knowledge bases that reduce duplication and variability, allowing evidence teams to do more with the same resources.

The impact of AI in evidence generation is shaped less by model complexity than by the governance and reproducibility built around it. Structured oversight is becoming integral to sustainable, scalable deployment.

3) Trial acceleration is moving upstream, and “queryable data networks” are becoming a core capability

Even with significant technological advancement, clinical development timelines have seen limited improvement over the past twenty years. Fragmented data sources, siloed operating models, and manual feasibility processes, continue to constrain speed and predictability. Increasingly, sponsors recognize that meaningful trial acceleration does not come from fixing enrollment challenges at the end of a trial, but from redesigning how studies are planned and operationalized before first patient in.

Embedding RWD and AI into these upstream decisions is not new in itself. What is changing is the shift from static point-in-time feasibility assessments to continuous, enterprise-scale data discovery. Sponsors are seeking ongoing visibility into what data exists across distributed sources, how it can be queried securely, and whether it is fit for purpose before protocols are finalized. This requires privacy-preserving connectivity that allows teams to evaluate the “shape” and coverage of data across networks without centralizing sensitive information. When feasibility becomes dynamic rather than episodic, trial design is grounded in observable patient and site realities from the outset.

How Datavant is working with teams:

• Stress-testing eligibility criteria against real-world populations before final protocol lock.
• Modeling enrollment scenarios using linked EHR and claims insights.
• Standardizing data models and identity resolution approaches to make feasibility repeatable across the portfolio.

Where value is showing up:

• Fewer protocol amendments and mid-study redesigns, as eligibility criteria are evaluated against distributed real-world populations before launch leveraging privacy-preserving, data connectivity infrastructure. 
• More reliable enrollment projections and faster study start, with reduced variance between modeled and actual recruitment enabled by cross-network data visibility and identity resolution. 
• Portfolio-level efficiency gains, as ongoing connectivity allows feasibility insights to carry across programs rather than being rebuilt trial by trial.

Taken together, these patterns suggest that trial performance is increasingly determined during planning, where decisions about secure, cross-network data connectivity and feasibility set the conditions for execution.

The bottom line: Evidence strategy is now enterprise strategy.

Across these trends, a common thread emerges: Trial performance, decision-grade RWE, and scalable AI are increasingly determined by the strength of the infrastructure that supports them.

The conversations emerging from industry forums such as SCOPE reflect a structural shift that repositions evidence generation from a downstream output to an enterprise capability. As data connectivity, feasibility analytics, fit-for-purpose frameworks, and governed AI workflows all continue to mature, performance depends less on isolated analytic wins and more on how consistently evidence can be generated, defended, and applied across the portfolio.

To explore how connected data, fit-for-purpose RWE, and governed AI can support your enterprise evidence strategy, contact us here: https://www.datavant.com/contact

¹ Themes discussed during Fit-for-Purpose RWD (Thomas Dougherty, Novo Nordisk) and Defensible AI-Enabled RWE: Managing Risk and Leading the Future (Sherrine Eid, SAS Institute), SCOPE Summit 2026.

² Themes emphasized during Defensible AI-Enabled RWE: Managing Risk and Leading the Future (Sherrine Eid, SAS Institute) and Emerging Themes in the Use of AI/ML in Causal Inference Based on RWD (Demissie Alemayehu, Pfizer), SCOPE Summit 2026.