In his role as Global Head of Data, Analytics & Research at the healthcare company Roche, Dr. Bryn Roberts has global responsibility for developing digital healthcare solutions together with his teams, using data and artificial intelligence (AI). The PhD pharmacologist has held various leadership positions within the Roche Group for around 20 years. In the interview with Porsche Consulting, he shares his insights on how data and AI are transforming global healthcare systems, improving patient well-being, and helping to predict and prevent diseases at an earlier stage.
Dr. Roberts, what do you see as the biggest problems facing global healthcare systems?
One of the biggest challenges today is the aging population. This leads to a steady increase in diseases in areas such as neurology, oncology, cardiovascular, and renal health. As a result, healthcare systems face enormous pressure, regardless of the country. One major factor is the rising costs associated with these conditions. Additionally, staff shortages further strain the overall healthcare systems.
The increasing availability of data is opening up new opportunities to transform global healthcare systems. In which areas do you see the greatest potential?
The targeted use of data and predictive analytics can help in risk stratification, early disease detection and therapeutic intervention, or even the prevention of diseases. It can contribute to reducing the heavy burden of late-stage treatments, including surgery, and chronic care management for serious illnesses. Another major opportunity lies in remote patient management – meaning a shift from traditional settings like hospitals toward enabling care at home through digital tools, combined with new medicines and diagnostic methods.
AI and large amounts of data – how do they work together?
It all starts with data: without complete, high-quality, relevant data, there is limited opportunity to leverage AI. With meaningful, high-quality data at scale, it becomes possible to use machine learning, build AI models, and use AI to analyze huge quantities of data and generate the required insights and outputs. For example, AI can help summarize complex patient histories or uncover hidden patterns in data that aren’t visible to humans, even to expert physicians. Experienced healthcare professionals will use AI to enhance their workflows and productivity, while less experienced staff or healthcare facilities in countries with weaker healthcare infrastructures, may benefit the most in terms of augmenting their capabilities.
Which concrete innovations and advances have been achieved at Roche through the successful use of data and AI?
At Roche, our goal is to use data and AI to make healthcare more efficient and ultimately improve patient outcomes. One concrete example is our “patient summarization tool”, which supports multidisciplinary cancer boards. It condenses a patient’s complex medical history into a concise, structured summary in less than half the time it used to take. So that physicians and nurses can spend more of their valuable time focusing on the patient instead of compiling records.
Another example is an AI-based tool that predicts the colorectal cancer risk from routinely collected healthcare data. By identifying at-risk patients in a population, we can enhance the effectiveness of screening programs and enable earlier, less invasive treatment interventions, which leads to improved outcomes and reduced overall costs. That’s exactly what makes this work so rewarding: leading teams that develop solutions to reduce the burden of disease for individuals and healthcare systems – resulting in people living healthier, happier lives.
How did Roche manage the transformation to a digital business model – what were the success factors? And what can other companies learn from it?
Our digital transformation at Roche is driven by corporate data and AI strategies. Every employee is encouraged to use AI in their daily work – what we call “everyday AI.” But the real game-changing transformations are taking place in our digital products and in key data-driven processes, such as R&D, which are augmented and automated using AI. Many of our digital health products are heavily dependent on machine learning. Without AI, we simply couldn’t bring these kinds of innovations to market. Success in digital healthcare starts with a data-first mindset and culture. It forms the foundation for real innovation. Many companies approach it from a “software-first” angle, focusing on engineering. But in healthcare, the quality of your outcomes – and your AI – depends on the quality of your data. Once you have that, AI becomes the engine that turns data into insights, better decisions, higher efficiency, and ultimately better care for patients.