For pharmaceutical companies like Roche, data constitutes important capital, in particular to be able to develop drugs more quickly and more efficiently. How does access to patient data currently work for the Swiss pharmaceutical sector?
Let’s start by saying that patient data is very valuable and we need to protect it well. We treat this data with the utmost respect, protecting patient privacy and preventing unauthorized access. It is important to emphasize that we have no access to identifiable patient data. Unfortunately, so-called real-world data is often not collected in a way that allows for simple and effective evaluation. Their full potential is therefore barely exploited, and I think this could be improved.
How do you concretely imagine the ideal ecosystem for health data?
For us, patients and the added value we can create for them are always at the center of our concerns. I think an ideal ecosystem is one in which different groups such as doctors, institutions, hospitals, but also universities and companies like Roche collaborate closely for patients. Health data can then be used comprehensively for research purposes and innovations for the benefit of patients can be encouraged. Our task is to help organize this process in such a way that we can access the data in compliance with strict data protection conditions in order to acquire new knowledge. And I emphasize once again that we do not have access to identifiable data here and that we have neither the desire nor the need to do so anyway. The goal is to be able to diagnose more quickly and treat in a more targeted way, while allowing researchers to access valuable health data to develop other therapies and diagnostic tests. Efficiency would also be increased, as earlier diagnoses and effective therapies reduce costs – a good thing for the healthcare system in Switzerland.
What role could the electronic patient record (EPR) play in such an ecosystem?
We warmly welcome the fact that the Federal Council wishes to advance digitalization in the field of health and develop the electronic patient file (EPR). Indeed, Switzerland must remain a strong and competitive research center so that the conditions for a sustainable, high-quality health system are met. And an integrated PED system benefits everyone: patients, providers and researchers. It is important to us that the data is usable for research. To do this, they must be complete, accessible in a timely manner, structured and usable in compliance with data protection law. Data quality is particularly important for digitalization in the healthcare sector. To ensure this, certain points must be given particular attention: firstly, it must be possible to make the Swiss DEP compatible with European and other international standards, and secondly, the DEP, as a node central, must be able to judiciously link data from other decentralized data banks or registers.
Switzerland has long lagged behind other countries when it comes to the digitalization of the healthcare system. What does this mean for Switzerland as a pharmaceutical location?
It’s true, and this delay has a direct impact on Switzerland’s competitiveness in pharmaceutical research. According to a study by the University of Basel, lack of access to high-quality, structured (real-world) data is leading to a shift in research and development investments to other countries. Roche, however, is clearly committed to Switzerland as a research center. The new research center which has just been inaugurated in Basel is an example of our commitment. As Europe’s leading life sciences location and the headquarters of pharmaceutical industry and science, Basel is the ideal location for our new research center.
What is needed to advance the digital transformation of the healthcare sector in Switzerland?
Roche is strongly committed to digitalization in Switzerland and works closely with all stakeholders so that the public health system can be organized for the future. Currently, health data is not captured in a structured manner across the entire country. They are therefore not available at all and, secondly, the infrastructural, technical and legal basis for reuse of the data is lacking. We need common standards serving as a basis for the quality of data collection, professionals with enhanced data skills, sustainable financing for an optimal long-term health system, a constructive legal framework to promote initiatives and legal security. In addition, it is essential to establish a networked infrastructure that acts as a sort of highway of the health system. Finally, acceptance and participation of the population are necessary.
For several years now, artificial intelligence has played an important role in the fight against cancer, for example in diagnostic imaging. Where exactly are we today? And how will AI shape the future of oncology?
At Roche, we already use AI in many operational areas, for example in research and development, acceleration of clinical studies or early detection in diagnostics. Significant progress has been made in oncology, particularly in diagnostic imaging, where it helps in the early detection and accurate diagnosis of cancer through advanced pattern recognition and image analysis. AI algorithms are now integrated into clinical workflows and help pathologists and radiologists identify cancers with greater accuracy and efficiency.
Last October, Roche announced a partnership with AWS and Ibex Medical Analytics. The stated goal is to help labs diagnose cancer using AI. What does this actually consist of?
You have to imagine that a large part of the work in pathology today is still largely manual and analog. The potential is enormous to digitize laboratories and introduce innovative AI-based image analysis solutions, which can bring important new insights into the diagnostic process and accelerate it. As an example, Roche’s navify Digital Pathology is a powerful pathology workflow software that makes it easy to both visualize digitized patient samples and use useful AI solutions. This software, built on AWS infrastructure, was designed to support both Roche’s AI solutions and the integration of third-party AI algorithms (like Ibex). This open system gives pathology laboratories the flexibility to enable the use of a wide range of AI solutions in their laboratories, as part of an efficient clinical workflow.
What does generative AI mean for Roche? How do you assess the opportunities and risks of generative AI for medical diagnosis?
Understanding generative AI, how it can be used and how it can support our business is extremely important to us. Generative AI will significantly increase productivity, process efficiency and employee productivity, for example through the automation of repetitive tasks, content creation, semantic search, voice translation, content summarization or code generation. Generative AI also offers great possibilities for medical diagnosis, improving the accuracy and speed of disease detection, enabling personalized treatment plans, and facilitating the discovery of new biomarkers. But all this progress should not overshadow risks – such as potential biases in AI algorithms, data protection concerns, and the need for rigorous validation to ensure clinical reliability. Balancing these opportunities and risks requires a robust regulatory framework, continuous monitoring and collaboration between all stakeholders.
About the interviewee:
Dr. Alan Hippe is Chief Financial & Information Officer of the Roche Group and member of the board of directors of Jacobs Holding in Zurich. During his career, he held management positions in the airport, steel and automobile sectors.
