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By Mike Branson, Chief Data Officer, UCB

Data and analytics have always played a role in how healthcare companies deliver new medicines and innovative treatments to patients. The advantages to using machine learning, AI, analytics, and data can be seen throughout the drug discovery, development, and innovation process as these technologies optimize workflow and deliver results more efficiently.

Many pharma companies are solely “data-driven,” meaning they take large pools of data and try to see which learnings can be extracted. However, it can be more beneficial to patients when companies take a question-led approach to data. Companies can first address the key questions that are most important to patients such as, “How can we improve symptoms and how quickly?”, “What is the appropriate dose of a medicine?” or “Can we predict future patient response/relapse to treatment?” Once the right questions are identified, companies can deploy their analytic capabilities against those questions to deliver the best results. This approach ensures that the most important topics are considered throughout the drug development continuum and gives healthcare companies the opportunity to better meet patient needs.

“Once the right questions are identified, companies can deploy their analytic capabilities against those questions to deliver the best results.”

In practice, a question-led approach to data can be helpful in three key areas:

  1. Clinical trial design
    • When companies ask the right questions, they can better understand the patient experience. This is especially important in clinical trials. In addition to a better patient experience, these questions can help to optimize clinical trial design including the selection process of patients, analysis of trial data, and overall improvement in logistics and operations. This all has a tremendous impact on how we work and how efficiently we can deliver results.
  1. Disease state research
    • A question-led approach is also crucial when companies are engaging in disease state research. UCB’s work with Stanford University is a great example. This collaboration leverages expertise in clinical, real-world, omics, and other data sources to advance learnings in certain important areas. Our first project with Stanford University focuses on Hidradenitis Suppurative (HS), also known as acne inversa. HS is an immunological skin disease that results in debilitating quality of life for people living with the disease. The treatment journey is often long and complex with delays, misdiagnoses, and ineffective treatment. As part of our collaboration, we plan to further examine phenotyping, computational discovery of pathogenic mechanisms, as well as the disease burden and societal experience for people living with severe diseases like HS.
  1. Efficiency in new technology
    • The last decade has introduced new computational methods which increase the cost-effectiveness and efficiency in the pharmaceutical industry. The digitization of patient records and clinical trials, the advent of cloud computing, the growth of wearable devices, and the medical Internet of Things (mIoT) has transformed clinical development with an explosion of data. UCB has maintained a question-led approach to data during a time of rapid advancement in technology. For example, our Statistical Science & Innovation group recently filed a patent application centered around an improved machine learning method that can search through a large hypothesis space to identify the most meaningful pieces of information and then use AI to predict an outcome or provide decision guidance. This technology can be deployed against patient-focused questions to find the insights that are most useful in a data set, thus adding value to our research. The innovation has already been used to inform patient stratification, clinical trial design, and precision medicine approaches for patients with Parkinson’s disease and epilepsy.

New technologies and advancements in data enable companies to make more informed decisions and accelerate the development of new compounds bringing treatments to patients faster and more safely than ever. Ultimately, a question-led approach to data drives the focus towards what’s most important – using technology to deliver differentiated solutions that serve patients.

To learn more about how data can benefit patients, visit