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What’s next for clinical trials? Exploring the rise of personalized research
Beyond AI, what factors are driving pharmaceutical innovation today?
Mukhtar Ahmed: Historically, healthcare has lagged many other industries in technology adoption. Today, that is changing as patient needs evolve, value-based care matures, and healthcare systems remain focused on improving outcomes through safer therapies and better treatment pathways.
At the same time, biopharmaceutical companies are investing heavily in data, technology, and AI-driven discovery. Advances in cell and gene therapies, cancer vaccines, and GLP-1 treatments are creating new opportunities to address unmet medical needs. We’re also seeing growing interest in real-world data (RWD), real-world evidence (RWE), synthetic data, and emerging technologies such as digital twins.
As these technologies and evidence sources converge, they have the potential to reshape research across the development lifecycle. Ultimately, success depends on having the right data and applying it to the right research question.
Looking at today’s environment, what is the biggest hurdle the industry needs to solve? What is the challenge that cannot simply be avoided or skipped?
One of the biggest challenges is balancing innovation with the realities of healthcare, where patient safety must always come first. New approaches need to be adopted responsibly, and that takes time.
The second challenge is access to the right data. It doesn’t matter what technology or AI model you’re using if the underlying data isn’t fit for purpose.
Too often, organizations start by collecting large amounts of data and only later decide what they want to achieve. The process should work in reverse: start with the outcome, then determine what data is needed to support it.
You mentioned the importance of getting the right data. Many companies claim they have good data, large datasets, and the right data. How does BC Platforms actually get good data? How does your approach differ?
Getting the right data is about more than volume. It starts with understanding the research objective and aligning the right technology, data, and analytical approach to that goal.
One of healthcare’s biggest challenges is fragmentation. Relevant data may be spread across hospitals, biobanks, laboratories, and diagnostic centers, often with restrictions on how it can be accessed or shared.
That’s where our approach differs. We’re not a data aggregator. Through BC Mosaic and federated analytics, data can remain where it resides while researchers generate evidence across institutions and geographies. The result is richer, research-ready datasets that support evidence generation.
How does BC Platforms support evidence generation across multiple data sources while maintaining local data control?
Our approach is a little different. Through BC Mosaic, we connect data custodians directly to specific research objectives. Whether the sponsor is a pharmaceutical company or a health system, the platform creates the connections needed to support that research program.
Researchers can then access and analyze relevant data through federated capabilities while data remains under local control. Rather than aggregating data into a central repository, the goal is to generate the evidence needed to answer a specific research question.
In practice, that means connecting hospitals, biobanks, and other healthcare data sources while respecting local ownership and governance requirements.
How does BC Platforms help organizations work with multimodal data, such as genomics, pathology, imaging, and clinical data?
We use BC Unify to ingest, curate, and harmonize data from multiple sources, including electronic medical records, laboratory systems, imaging platforms, and genomic data. We also support capabilities such as medical image de-identification.
Our role is to connect those sources, establish the necessary pipelines, and prepare the data for research. Once the data is available within the BC Mosaic environment, researchers can use our analytical tools or bring their own tools and models to perform the analyses required for their specific objectives.
How does this approach support collaboration across organizations and geographies?
Organizations can use BC Mosaic to conduct their own research using their own data, analytical tools, and models. The environment is designed to be flexible, allowing researchers to work in the way that best supports their objectives.
The real value comes when those organizations choose to connect. Research centers can collaborate with institutions in other countries, participate in multicenter studies, and contribute to broader evidence-generation initiatives while maintaining control of their own environments.
The same approach can support sponsored research. Pharmaceutical companies can connect with leading research organizations across different regions and patient populations, making it easier to identify collaborators and generate evidence at scale.
This creates a connected research ecosystem where organizations can pursue independent research, participate in collaborative programs, and contribute to global evidence-generation efforts through the same platform.
BC Platforms is helping build the Japan Precision Medicine Platform (JPP). What makes this initiative significant?
In collaboration with NTT Precision Medicine, we’re helping build a national precision medicine platform that connects leading hospitals and biobanks across Japan. The goal is to create a highly representative dataset spanning multiple prefectures and support a broad range of precision medicine research initiatives.
What makes the initiative particularly powerful is the combination of scale and diversity. By bringing together multimodal and multi-omics data from institutions across the country, we’re creating a resource that can support many different research scenarios while keeping healthcare organizations closely involved in the research process.
There is significant interest from the pharmaceutical industry because of the scale and quality of the data being assembled. Because the initiative is nationally sponsored, it also has broad visibility and the potential to help drive future research and medical innovation across Japan.
For us, it also demonstrates how connected data ecosystems can help advance precision medicine by bringing together healthcare organizations, research institutions, and industry around shared research objectives.
We’ve talked about access to data. What does it take to make that data truly AI-ready?
It starts with the research question. If the underlying hypothesis is flawed, no amount of data or computing power will produce meaningful results.
Organizations need to work backwards from the outcome they’re trying to achieve: what question are they trying to answer, what data do they need, and what technology is most appropriate?
The same applies to AI. Model selection, validation, safeguards, and human oversight all matter. AI readiness depends not only on the quality of the data, but on how well data, technology, and research objectives are aligned.
How does BC Platforms help pharmaceutical companies generate better research outcomes?
When a sponsor comes to us with a research program, we start by looking at the research protocol and the outcomes they are trying to achieve. From there, we help refine the approach, identify appropriate data sources, and determine which data partners may be the best fit for the research objectives.
In many cases, sponsors are approaching a problem through a more traditional lens. We work with them to optimize the process by helping them access the right data and the right research partners to support their objectives.
The goal is to accelerate access to research insights while improving the quality of the evidence generated. That can help reduce timelines, lower costs, and create more robust datasets for analysis.
We do this through a combination of technology, data science, and domain expertise, tailoring the approach to the needs of each research program.
How can organizations balance evidence generation with data privacy, consent, and data sovereignty requirements?
It ultimately comes down to choice. Patients are increasingly aware of how their data is used, and healthcare organizations have a responsibility to respect those choices while complying with local regulations.
Different organizations and countries have different requirements, which is why flexibility is so important. Federated approaches, de-identification, tokenization, synthetic data, and digital twin-derived datasets can all support research while protecting sensitive information.
Our goal is to help organizations conduct research in a way that respects privacy, governance, consent, and data sovereignty requirements.
Looking ahead five to ten years, how do you see precision medicine, real-world evidence, and healthcare innovation evolving?
I think we’re going to see much greater convergence across healthcare and research, including value-based healthcare, pharmaceutical and biotech research, medical devices, and wellness. As those areas become more connected, opportunities for evidence generation will continue to expand.
Patient centricity will remain central to that evolution, supported by stronger partnerships and deeper collaboration among researchers, providers, and patients.
We’ll also need better models, better computing capabilities, and more responsible AI. These advances will make evidence generation more powerful, but they must remain trustworthy and scientifically rigorous.
Ultimately, I believe we’re moving toward a future of “personalized research,” where research becomes increasingly tailored to specific patient populations and their needs.
We talk about personalized medicine. There will also be personalized research, where the research is specific to a patient profile or a particular patient cohort.
Key takeaways
Pharmaceutical research is evolving rapidly, but one principle remains unchanged: better outcomes depend on asking the right questions and having the right data to answer them. As more data and new technologies become available, the challenge will be turning those capabilities into meaningful evidence that supports better research and better decisions, keeping in mind that:
- Pharmaceutical innovation is being driven by the convergence of AI, real-world data, and advances in medical science.
- Access to the right data remains one of the industry’s biggest challenges.
- Federated approaches can enable evidence generation while maintaining local data control.
- AI readiness starts with the research question, not the model.
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