Breaking Down R&D Knowledge Silos in the Life Sciences
In This Article
The life sciences have lagged behind other healthcare ecosystem members embracing data as a critical component of business success. Payers and health systems embarked on their data journey decades earlier due to the rapid growth in patient health information and the challenges surrounding data silos. But pharmaceutical and device companies are now rethinking their relationship with data as they work to remain competitive in this rapidly changing market.
With only 14 percent of drugs in clinical trials making it to market according to a study from the MIT Sloan School of Management, pharmaceutical companies continue to double down on improving drug discovery cost and success rate. These organizations are reimagining the role of enterprise data, and the R&D arm of the business is front and center in this transformation.
The research organizations within pharmaceutical companies are comprised of multiple teams that are often working towards the same effort -- with very little cross-departmental communication. Fragmented and heavily manual workflows result in information silos that prevent critical knowledge from being shared between groups. This, in turn, can lead to costly decisions being made based on inadequate or outdated information.
A very successful scientist recently shared a story with me about his time at a large, global pharmaceutical company. He had been working for months on a particular drug discovery project without any awareness of an almost identical research study conducted in a lab on his same floor. Access to the data generated from that research could have saved him hundreds of hours of time wasted on duplicating previously failed efforts.
Before a meaningful conversation about harnessing and driving predictive insights from data can be had, the discussion about capturing and storing the data, in the first place, must take place. We must walk before we crawl. The pandemic in particular has brought the significant challenges of antiquated, paper-based workflows in R&D organizations to light.
Research scientists represent some of the most brilliant, creative thinkers in the world. Much like physicians using paper patient charts before the introduction of electronic health records, these independent thinkers have traditionally had significant autonomy in choosing their tools and applications for conducting their research. Despite the increasingly digital nature of other parts of the enterprise, paper lab notebooks, legacy research instruments and technologies that are not connected to the rest of the enterprise are still commonplace inside the lab.
Thus, before we can tackle the challenges of harmonizing and unlocking heterogeneous research data captured from a myriad of disparate sources, we need to ensure that the data is captured in the first place. And to capture data, the adoption of new technologies, tools and workflows within the lab must take occur.
I recently asked a group of senior leaders representing five global life sciences companies, what they saw as the most significant barrier to a successful data transformation in their industry. The unanimous answer was "culture." In the R&D organization, this doesn't come as a surprise. A successful scientist is often independent, self-sufficient and tirelessly determined, so asking them to adopt standardization of tools and processes to drive collaboration would understandably be challenging. But in an industry where the average cost of successful drug development is $2 billion, the status quo is no longer an option for R&D.
"There's no free lunch," my college physics professor often said.
We all have those things in our personal and professional lives that we don't want to change, yet we know we ought to change them in order to get the results we desire. Researchers are no exception. For example, a survey of 1,576 researchers shows that over 90 percent of scientists feel that the inability to reproduce experiments is a significant crisis in the research community, and 40 percent report that their labs are taking steps to overcome this by implanting processes like improving data documentation methods.
However, change is hard. When comparing paper versus electronic laboratory documentation, another study shows that only 7 percent of scientists are using electronic lab notebooks (ELNs), 21 percent state that they prefer paper notebooks and 62 percent report that they will think about it ELNs the future, but not now.
As life science organizations begin their journey to becoming data-driven, a few key considerations to drive cultural adoption are essential in achieving success in the R&D organizations.
- Empowerment: In highly regulated, highly specialized industries like healthcare and life sciences, end-users must be empowered starting at the beginning of any technology-driven change. Giving scientists a seat at the table during the data transformation journey ensures that workflow congruent tools and strategies are put in place and that risks and unintended consequences are avoided.
- Transparency and communication: Actively listening to researchers' reasons for resistance and implementing steps to address them is crucial in building trust and ensuring success.
- Art of the possible: Creating a shared vision of the aspirational future with scientists allows them to see the value that access to previously unavailable data can afford them.