I recently interviewed Peter O’Blenis, the CEO and Founder of DistillerSR, a Canadian AI pioneering company incorporated in 2008. DistillerSR automates the complex literature-based evidence-generation process, supporting over three hundred customers, including top research organizations and seventy percent of the top 10 Pharma and medical device companies.
I always enjoy finding self-funding entrepreneurs, and Peter and his leadership team have built a successful AI software global company that solves a real business problem and has demonstrated improving operational inefficiencies in the health technology sector. Here are some of our discussion highlights on DistillerSR’s business idea roots, customer experiences, entrepreneurial CEO lessons learned, and cultural values that guide DistillerSR’s journey as a global software leader in systematic, intelligent literature reviews.
Where did your business idea come from?
“The idea first surfaced while, at a previous company, we were providing clinical trial data management software for organizations conducting type 3 clinical trials. One of our offices was located in a research hospital, and one of the research teams there was conducting systematic literature reviews. They introduced us to the process and to the challenges of conducting this type of research manually. This was a process that was ripe for automation.
We were fortunate to find an acute problem, and although we did not have the domain expertise, we were surrounded by people who did. Their knowledge and willingness to teach us the nuances of the process enabled us to innovate and bring a viable and profoundly impactful solution to the market.” says Peter.
Can you describe DistillerSR’s product offerings?
“Systematic literature reviews are the gold standard for evidence-based healthcare. In the pharmaceutical and medical device markets, they comprise part of the dossier required to get therapeutic products approached and to keep them on the market. The issue is that literature reviews are an extremely time consuming and error prone process when done using traditional methods.
DistillerSR is an AI-enabled, regulatory compliant, literature review and evidence management platform that allows organizations to dramatically accelerate time-to-completion while also reducing errors and providing unprecedented transparency in the process. It is a key component in allowing them to deliver high quality, sustainable regulatory coverage for their products.”
Peter, can you share any memorable customer story experiences?
“I can share a number of experiences. The first one was a medical device manufacturer who discovered that their product was harming patients. Unfortunately, they did not pick up on this risk as early as they would have liked. DistillerSR was brought in as part of the remediation process as they needed to develop a better way to monitor the literature. They have been customers for five or six years now.
A second experience was during the Covid-19 pandemic. The Canadian governmental agency in charge of Covid-19 health policy had to continuously monitor the fire hose of new evidence that was being published for content that would inform the country’s federal health guidelines. With a small team, there was simply no easy way to keep up without introducing automation.
The team leveraged DistillerSR’s workflow automation capabilities as well as a number of the platform’s integrated AI systems to help with the screening process and identify the key elements that they were searching for. This allowed them to rapidly filter through all the new inbound literature to inform public policy on Covid-19 in a timely manner. We were pretty proud to be able to help in that way.”
What can your customers look forward to in terms of new release capabilities?
“This year, we will see significant advancements in how our constituents capture and reuse data. Often organizations do their literature review work in more than one place. Unfortunately, this work is frequently performed in silos that don’t communicate with one another, resulting in repeated work and other data management challenges. Our platform allows these different groups to share content and collaborate transparently in real time, reducing the overall workload and accelerating time to completion.
We are doubling down with more advanced data extraction features that will allow the software to read full-text documents and find all of the key data elements a researcher is looking for with a human-in-the-loop approach. The human-in-the-loop piece is critical as you can’t afford to have errors in your data sets.” says Peter.
What is your perspective on ChatGPT and Generative AI?
“I think that we are in the middle of an AI hype cycle. In our market , generative AI has accelerated the adoption of AI because it has served to raise people’s expectations of it. While organizations were once hesitant to adopt AI, they are now mandating finding ways in which to leverage it.
That said, adoption needs to be measured and safe. You cannot sue an algorithm, but you can certainly sue someone who used that algorithm irresponsibly. If you are using ChatGPT to generate clinical data, then you are probably going to end up unemployed or in court.
On the other hand, ChatGPT and similar generative algorithms can be used for many interesting peripheral use cases. For example, simple template generation to reduce report writing has a place. Rather than humans filling in the minutia, generative AI can perform the mundane tasks, and humans can do the intellectual sense-making, “ says Peter.
What are some of the key lessons that you have learned as an entrepreneur?
Peter says, “It’s not just about technology. It’s about solving complex problems with innovative technology. We have been successful for more than twelve years, and it’s because we are bringing solutions that our customers can use right out of the box; that have immediate value. I’ve met many tech entrepreneurs who are technology people trying to find ways to apply their technology to solve a problem, but unfortunately, they don’t have a deep understanding of the problem. It’s failure to address the subtleties that get you in the end, so if you don’t have the domain expertise, hire people who do so you are not missing the market.”
What are the leadership behaviours that your culture inspires?
“There is a scene in the television show Ted Lasso where one of the characters asks her friends for their opinion on her date. One says he’s fine. The other friend explodes with “He’s fine. Lots of people are fine. Don’t you dare settle for fine”. I want my team to be thinking this way when they recruit new team members. Lots of people are fine, but fine isn’t going to get you where you need to go, so you just can’t tolerate mediocrity on your leadership team. The same goes for product innovation or any other corporate initiative. Fine won’t cut it.
I look for team players who are highly collaborative. Teams need to build trust to allow the hard discussions to happen without fear of being shot to pieces. It’s critical to have spirited discussions, but they always need to be respectful and people need to feel safe. I don’t like being surrounded by people who tell me I’m doing a good job because I know that’s not true a lot of the time. I would much rather have people call me on things and be comfortable being honest.
Lastly, I would say that, when you have the right people in place, give them space to do what they need to do. They should know more about their domains than you do, and you should empower them,” says Peter.
Conclusion
The global COVID-19 pandemic has underscored the imperative to reconsider established working methods and approaches while exposing the vulnerabilities of the health sciences industry. It has become evident that the demand for more human-centric yet scalable solutions has become even more pressing.
As we navigate the shift from the familiar “old normal” to the emerging “new normal,” we are presented with a unique opportunity to transform and redefine the industry’s societal role. As part of this transformation, revamping the systematic literature review process and leveraging the power of artificial intelligence (AI) to disrupt conventional practices is crucial. DistillerSR, a company based in Ottawa, is at the forefront of this disruption, challenging the status quo with its AI-powered platform. With a growing clientele that spans the globe and the world’s top Pharma and medical-devices customers, DistillerSR is driving innovation and paving the way for a more efficient and effective literature-based evidence process.
More About Peter O’Blenis
Peter is the CEO and co-founder of DistillerSR and has been a thought leader in the automation of literature reviews and evidence management since 2005. Since its inception, DistillerSR, has garnered the trust of 70% of the top 10 pharma and medical device organizations worldwide. His innovative work has revolutionized evidence generation in health sciences.
With an undergrad in Computer Science and an MBA from Queen’s University, Peter’s background includes management roles at TrialStat, Mitel, Oracle, Flick Software and WebGain. He frequently speaks on the subject of literature review automation and has published peer reviewed articles on literature review automation and AI.
Research Sources:
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