OSP: Could you start by sharing a bit about your background and how you got involved with Cluepoints?
PH: Certainly. We established the company in 2012, and over the years, especially in recent times, we’ve seen significant growth in the adoption of risk-based quality management (RBQM) and associated statistical AI and machine learning software within the clinical trials industry. My involvement stemmed from doing some work for another founder who had been working on advanced statistical approaches coinciding with the advent of guidance on risk-based monitoring, which later evolved into risk-based quality management. So, the timing was perfect, and I’ve been part of this journey since its inception.
OSP: Fascinating journey indeed. Could you elaborate on Cluepoints’ current focus and what sets your software apart in the market?
PH: Absolutely. We’ve become known for our expertise in risk-based quality management, particularly for the deep statistical algorithms embedded within our software products. Our unique selling proposition lies in these statistical underpinnings, which have garnered recognition within the industry. What makes us even more compelling today is our expansion into artificial intelligence and machine learning applications aimed at enhancing the efficiency of clinical trials. For example, we’ve developed applications like our medical coding tool, which leverages deep learning to achieve a remarkable 97% accuracy in coding tasks, significantly reducing the need for manual intervention and streamlining the process.
OSP: That sounds like an advancement in improving efficiency. How do you see AI and machine learning further shaping the landscape of clinical trials?
PH: AI and machine learning hold immense potential to drive efficiencies and enhance data quality within clinical trials. Our focus is on identifying specific areas within the trial process where these technologies can make the most impact. For instance, we’re exploring the use of large language modeling approaches to assess risks and mitigate them by analyzing trial protocols comprehensively. By harnessing AI in this manner, we aim to provide sponsors and CROs with actionable insights to optimize their trial strategies effectively.
OSP: It’s evident that technology plays a crucial role in streamlining processes and ensuring data integrity. However, what challenges do you foresee in the adoption of these advanced solutions?
PH: While the benefits of AI and machine learning are clear, there are challenges associated with their adoption, including change management and ensuring scalability. Additionally, there may be scepticism among stakeholders accustomed to traditional methods. Hence, our approach focuses on demonstrating tangible benefits and providing tailored solutions that address specific pain points within the clinical trial ecosystem.
OSP: Indeed, overcoming scepticism and effectively managing change are critical aspects of technology adoption. Could you share any notable success stories or real-world applications of Cluepoints’ software?
PH: One compelling example is our collaboration with Pfizer on a COVID-19 study, where our software played a vital role in ensuring data integrity and facilitating efficient data analysis. This experience underscores the importance of advanced technologies in addressing pressing healthcare challenges and accelerating the drug development process.
OSP: That’s impressive. As we look ahead, what advancements or developments can we expect from Cluepoints next?
PH: Moving forward, we’re committed to further enhancing our AI and machine learning capabilities to address evolving needs within the clinical trial landscape. Our focus remains on delivering innovative solutions that drive efficiency, improve data quality, and ultimately contribute to advancing healthcare outcomes.