The pharmaceutical industry is entering a new era of evidence generation. For decades, randomized controlled trials (RCTs) have served as the gold standard for evaluating the safety and efficacy of medicines. While that framework has enabled countless medical breakthroughs, it was designed for a pre-digital world, one in which data was collected episodically, analyzed in silos, and often limited to narrowly defined patient populations.
A new whitepaper from Sonata, Integrated and Holistic Evidence Generation 2.0, argues that advances in AI, interoperability standards, blockchain technology, and connected health devices now make it possible to fundamentally rethink how evidence is generated and used throughout a drug’s lifecycle.
According to the report, healthcare is approaching a pivotal inflection point. Said the paper’s author Raghav Dave, “the convergence of real-world data networks, artificial intelligence, blockchain infrastructure, and connected patient devices is creating conditions that make it possible — for the first time — to build a drug evidence ecosystem that is continuous, comprehensive, transparent, and genuinely patient-centered.”
At the heart of the paper is the concept of “Evidence 2.0,” which is a framework designed to move pharmaceutical research beyond static snapshots and toward a living, continuously updated evidence ecosystem. The report states that “technology now makes it possible to move from a drug evidence system that is episodic, siloed, and static — to one that is continuous, integrated, and dynamically updated.”
It also adds that this shift represents “a generational upgrade in our ability to understand what medicines do in the real world.”
Dave argue that several technological developments have converged to make this transformation possible. Electronic health records are now widely deployed across healthcare systems, while interoperability standards such as FHIR APIs have created the infrastructure necessary for large-scale data exchange.
At the same time, federated health data networks have demonstrated that privacy-preserving analysis can be conducted across millions of patient records without requiring data centralization. These developments provide the foundation for a new generation of real-world evidence programs.
AI plays a central role in the framework. According to Dave, machine learning systems are increasingly capable of identifying safety signals and treatment patterns across large, complex datasets.
Rather than relying solely on voluntary adverse event reporting, regulators could use AI-driven systems to continuously monitor health outcomes, detect emerging risks, and evaluate treatment effectiveness across diverse patient populations.
Another major theme is the growing importance of patient-generated evidence. Wearable devices, digital biomarkers, and connected health technologies are producing streams of health data that were previously inaccessible to researchers.
Beyond evidence generation itself, the report calls for greater transparency across the entire pharmaceutical ecosystem. One proposal is the creation of a Unified Evidence Traceability Platform (UETP), which would provide a publicly accessible record linking regulatory decisions and supporting analyses.
Dave envision a system that creates “an end-to-end chain of custody from evidence to decision” and enables stakeholders to understand not only what evidence exists, but also how that evidence influenced regulatory outcomes.
Importantly, the report does not position this transformation as a challenge to regulators. Instead, it argues that agencies such as the FDA are uniquely positioned to lead the next phase of modernization.
While many of these ideas remain aspirational, the paper’s central argument is that the underlying technologies already exist. What is needed now is coordination among pharmaceutical companies, regulators, healthcare providers, technology vendors, and advocacy groups.
