What Happens After the Trial? Tokenization Has the Answer

In clinical research, gaining a complete understanding of patient outcomes remains a significant challenge. Clinical trial data and real-world data (RWD) often exist in separate silos, making it difficult to track what happens to patients before, during, and after a trial. Privacy concerns have traditionally limited the ability to connect these data sources, delaying access to critical information on treatment effectiveness, safety, and healthcare utilization.

To overcome these barriers, innovative methods are emerging that enable secure and privacy-preserving linkage of clinical trial data with real-world sources. One such method is tokenization: a technology that allows researchers to connect disparate datasets at the patient level without compromising confidentiality. This approach helps fill important evidence gaps and provides a more comprehensive view of patient journeys, supporting better decision-making throughout drug development and patient care.

What Is Tokenization and Why Does It Matter?

Tokenization replaces personally identifiable information (PII) with unique, encrypted tokens. This enables secure linkage of clinical trial data with electronic health records (EHRs), insurance claims, registries, and other real-world sources without exposing sensitive patient details. Unlike traditional anonymization, tokenization preserves the ability to track patients longitudinally, extending the clinical trial snapshot into a continuous, real-world timeline.

This expanded view is critical because clinical trials typically capture data during a limited period, missing important events before, between, and after study visits. Tokenization bridges these gaps, providing insights into long-term safety, treatment adherence, healthcare utilization, and overall effectiveness; the data increasingly demanded by regulators, payers, and clinicians.

The Growing Use of Tokenization and Why It’s Still Complicated

Tokenization, once reserved for late-stage trials, is now being adopted earlier in the development process, particularly in rare disease and personalized medicine studies. This early integration supports better trial design, regulatory planning, and richer evidence generation for natural history studies.

As adoption increases, more protocols are being revised to incorporate tokenized data sources. This shift is helping researchers extend their view beyond traditional trial endpoints and into long-term patient outcomes.

What’s driving progress:

1. Claims data are the primary source for tokenization due to:

• Their extensive coverage (hundreds of millions of patients)
• High overlap with trial populations (up to 50%)

2. Proactive consent from patients enables match rates close to 50%

3. Mortality registries help capture death events not always recorded in medical records

What’s still holding tokenization back:

1. Low consent rates for post-trial follow-up, often under 40%, limit longitudinal data capture
2. EHR integration challenges, including:

• Inconsistent record formats
• Variable data quality
• Limited overlap with trial populations

3. Current workflows often rely on:

• Protocol amendments to secure consent
• Secure platforms for linking data
• Heavy use of claims data, which offer breadth but lack clinical detail
• EHRs, which offer richer context but smaller population coverage

To fully realize tokenization’s potential for real-world evidence and long-term outcome tracking, gaps in consent processes, data integration, and coverage must be addressed.

How Tokenization Enhances Real-World Evidence

Tokenization opens the door to several key benefits:

• Longitudinal Patient Tracking: Monitor patients beyond the trial period to capture disease progression, adverse events, and medication adherence in real-world settings.
• External Control Arms: Create synthetic control groups by linking external patient data, reducing placebo use and speeding enrollment.
• Improved Data Quality: Combine multiple data sources to fill gaps and increase dataset completeness.
• Support for Decentralized Trials: Facilitate remote data collection and patient monitoring, supporting hybrid and patient-centric trial designs.

What It Takes to Implement Tokenization Successfully

Tokenization’s power comes with responsibilities. Successful implementation requires:

• Robust Data Governance: Clear policies and technical safeguards to ensure data integrity and privacy.
• Informed Patient Consent: Patients must understand how their data will be linked and used.
• Interoperability: Tokenization solutions must work across diverse healthcare IT systems and data standards.
• Regulatory Compliance: Strict adherence to privacy laws such as HIPAA and GDPR is essential, alongside alignment with ICH E6(R3) and EMA Clinical Trial Regulation (EU) No 536/2014 guidelines (ICH, 2025; EMA, 2025; FDA, 2024).

Real-World Use Cases: Closing the Evidence Gap

Recent studies highlight how tokenization is applied to bridge CTD and RWD. For example, linking randomized controlled trial data with administrative claims has shown match rates near 50% when patient consent is obtained early, enabling long-term follow-up without additional burden on patients or sites. Another case involved linking consumer credit reporting data to trial participants to validate diversity goals post-enrollment, demonstrating tokenization’s flexibility to meet evolving evidence needs.

These examples show tokenization’s potential to unlock insights previously inaccessible due to fragmented data and privacy concerns, enabling sponsors to generate richer, more actionable evidence.

Our Approach to Tokenization

At Maxis Clinical Sciences, we have developed a privacy-first tokenization framework designed to meet these challenges head-on. Our platform uses advanced probabilistic matching algorithms that maintain accuracy even when patient data is incomplete or inconsistent, achieving match rates above 90%. This enables reliable linkage of clinical trial data with real-world sources while preserving patient confidentiality.

We integrate seamlessly with leading electronic data capture (EDC) systems and healthcare databases, automating data collection and quality assurance to reduce the burden on clinical sites and patients. This accelerates access to critical long-term safety and effectiveness data and supports regulatory readiness through robust audit trails and compliance with global privacy standards.

Looking Ahead: Tokenization as a Strategic Asset

Tokenization is becoming a foundational tool that’s changing how clinical research collects and uses data. As more organizations adopt tokenization early in trial planning, they unlock deeper insights, accelerate research timelines, and improve trial design. This evolution supports richer evidence generation for regulatory submissions, payer negotiations, and ultimately better patient care. By using tokenization, we’re creating a future where clinical trials follow the entire patient journey, making sure all data and patients are included.

Key Takeaways

• Tokenization enhances real-world evidence integration: Securely links clinical trial data with real-world sources such as EHRs, claims data, and registries; providing a more comprehensive view of patient journeys without compromising privacy.
• Implementation requires careful planning and compliance: Depends on robust governance, informed patient consent, interoperability across data platforms, and strict adherence to global privacy regulations such as HIPAA and GDPR.
• Potential to transform clinical research efficiency and quality: Simplifies long-term follow-up, enables external control arms, improves real-world outcomes analysis, and supports decentralized trial models; accelerating timelines and enhancing study relevance.

References

1. Cunningham, B. (2025, June 24). Tokenization: The Unsexy Plumbing of Clinical Research. Applied Clinical Trials. https://www.appliedclinicaltrialsonline.com/view/tokenization-unsexy-plumbing-clinical-research
2. Clinical Trial Tokenization for RWE Integration | Use Case. (2024, December 18). Maxis Clinical Sciences. https://maxisclinical.com/insights/use-case/privacy-compliant-clinical-trial-tokenization-for-rwe-integration/

Author: Nishaa Panwaar