Gen Li, president and founder of Phesi on data management

Here’s a summary of our question and answer session. 

How has the role of data management in clinical development changed?

The biopharmaceutical industry has adopted new technologies at an unprecedented pace in recent years. Data from patient records, laboratory research and experiments, clinical studies and scientific publications have all been submitted to digital repositories, giving sponsors the opportunity to use insights from these sources when designing and executing clinical trials. More data means more precision when trial planning, but managing these larger datasets can be a challenge.

Even today, clinical development organizations often depend on qualitative learnings of how previous trials had been successful. Trials often go to the same recruiting sites, creating a shortage of patients as sponsors all ‘recruit from the same patient pool.’ When trials do not take a data-led approach, they often fail to meet objectives and are subject to delays. In short, despite the availability of data, there is a tendency in the industry to rely on ‘gut feel,’ and make perception-led rather than data-led decisions. To overcome this, the industry is now turning to data managers to modernize and accelerate the clinical development process, moving from data management to data science.

What does the future look like for data management?

Combining data from historical trial records, ongoing clinical trials, electronic patient records, published data and epidemiological studies with newer sources such as wearable and telemedicine devices and health applications, will streamline trial design. Building detailed and contextualized knowledge of the patient population can inform various elements in clinical trial protocol design including inclusion/exclusion criteria, comparator(s), treatment duration and endpoints, avoiding protocol amendments and reducing non-active and non-enrolling investigator sites. Sponsors looking to unlock the potential of data in this way will need data science expertise to collate, harmonize and analyze data effectively.

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