How would you characterize the current role of data in the healthcare industry?

We are at a point where massive amounts of data are being generated, and for the first time, we have the technological ability to bring it all together and make sense of it at the moment it’s needed. It feels like healthcare is poised to lead other industries in demonstrating the real-world impact of AI, moving from a question of if we should use it to how we can best use it to solve our most pressing problems.

What are your key strategic priorities to ensure Tempus has the “right” data to power its mission?

My team focuses on several key areas. First is data access; we invest in technology to make the process of sharing and receiving data from health systems simple, secure, and controllable. Second, we have an underlying data platform that brings together all of this information and makes it usable and analysis-ready. This involves processes like de-duplicating patient records, linking to external data sources, and using AI to help organize and interpret vast amounts of unstructured data. And finally, delivering that data in the right way to users of the data, along with research-ready data and tools that shorten the time to insight.

How does Tempus leverage AI to transform its multimodal RWD into actionable insights for researchers?

For researchers, this is transformative. Instead of starting from a blank sheet of paper, our tools can generate a first draft of an analysis almost instantaneously. This allows highly skilled experts—statisticians, epidemiologists, and data analysts—to use their training to refine, edit, and build upon that foundation. It dramatically shortens a process that might have taken weeks or months, freeing up researchers to focus on generating more insights, particularly at the individual patient level. The ability to do that for each patient as they walk in the door is the holy grail of precision medicine.

What does a “learning healthcare system” mean to you, and how is Tempus helping to make this vision a reality?

To make this a reality, you need data to be accessible and consumable at the moment of care. You also need a system that continually aggregates new data, so the insights provided a month from now are informed by all the patients seen in the interim. At Tempus, we are building the foundational data platform to break down these silos. By integrating this platform with tools that surface the right information to a physician within their workflow, we can help them make more informed decisions and ensure care is constantly improving.

Tempus acts as both a generator of molecular data and an aggregator of clinical, imaging, and claims data. Can you explain the strategic importance of this dual approach?

This approach also builds trust. When we partner with health systems, they often have a longstanding relationship with us through our sequencing business. This provides a foundation of trust to build upon for broader collaborations around AI applications, clinical trial matching, or care gap identification. It allows us to expand the relationship from sequencing a specific patient population to providing insights across all of their patients, linking our deep molecular data to the rich clinical context—like treatments, outcomes, and physician notes—found in the aggregated records.

A powerful real-world dataset must be both complete and diverse. How does Tempus ensure its data is robust enough to inform critical decisions and representative enough to address health equity?

Tucker: We spend a lot of time thinking about data quality. This includes validating key data elements like clinical outcomes, characterizing what we have and what’s missing, and measuring the performance of our curation, whether it’s done manually or with AI. We believe in transparency, which is why we publish on the quality of our data and enable users to trace data points back to the source text. We also focus on ensuring the data is “fit for purpose” for each specific research question, which may involve linking to external data sources, such as claims data, to create a more complete picture.

On representativeness, it’s critical to look at the makeup of the patient population. We work with more than half of all treating oncologists in the country, and our network includes a broad mix of community and academic centers that closely mirrors the distribution of where patients are receiving care in the real world. By its nature, this type of RWD is inherently more diverse than data from clinical trials or specific registries. Building a dataset of hundreds of thousands of sequenced patients with their corresponding clinical data is a massive step toward understanding the natural history of disease in a truly representative population.

Given the sensitive nature of patient data, what is Tempus’ philosophy on data privacy and ethics?

Philosophically, we believe that the providers who generate data own their data. We act on their behalf, and if we create derivative data products through curation or processing, we give that value back to them. We don’t ask for data exclusivity because we see data as a renewable resource. When handled appropriately, it can be used to learn, improve patient care, and help the entire ecosystem.

Can you share an example of how Tempus’ AI and data solutions have enabled a learning loop that improved patient outcomes or accelerated research?

This process also accelerates the accrual of patients to clinical trials, which directly benefits our life sciences partners and the broader research community. Our entire portfolio of AI applications is an example of this learning loop in action, where we leverage real-time, multimodal data to provide actionable recommendations to physicians that can have a direct and positive impact.

What support does Tempus offer researchers in terms of leveraging your data and AI models?

This capability is unique and can spur new research questions and use cases. It creates a continually reinforcing feedback loop where researchers can explore the data, identify new variables of interest, and then request that they be curated at scale using AI, unlocking even more value from the data.

How does Tempus ensure researchers can seamlessly integrate their own data into the Lens platform?

Looking ahead, what emerging data technologies or methodologies do you see as having the most significant impact on the life sciences industry?

Tucker: Beyond the broad impact of large language and multimodal models, I believe we will see a significant impact from AI-powered diagnostics. This means moving beyond the results of a single test and instead using the totality of a patient’s data—combining multiple data points from different moments in their care—to identify and predict valuable insights. We are already starting to do this in cardiology with our ECG AI products.

This is the true promise of precision medicine. It’s about understanding that every patient is different and using foundational models to recognize patterns across a much broader scale than was ever possible for a human to see. This will help us predict significant health events well in advance and invest more in preventative care.

For life sciences leaders considering data collaborations, what are the most important questions they should be asking about a partner’s data and technology platform?

Second, consider the dimensionality of the data. In the future, unstructured text, raw imaging, and pathology will become increasingly important as we develop better tools to analyze them. A partner should have access to this type of high-dimensional data.

Finally, and perhaps most importantly, ask about the partner’s investment in making the data usable and actionable. Having data that just sits in a platform is not enough. The real value comes from a partner who has spent time thinking about how to integrate that data into decision-making and deliver it to the right person at the right time.