This was to discuss the Site Profile & Oversight Tool (SPOT), which takes risk-based quality management (RBQM) to the next level by facilitating an adaptive site monitoring approach that compliantly flexes to the needs of the study.
Can you share any new products or services your company is unveiling at this year’s DIA? How do your latest innovations address current challenges in the pharmaceutical and healthcare industries?
We will be unveiling not one, but two new products at this year’s DIA.
The Site Profile & Oversight Tool (SPOT) takes risk-based quality management (RBQM) to the next level by facilitating an adaptive site monitoring approach that compliantly flexes to the needs of the study. By enabling teams to swiftly pinpoint anomalies and actionable insights, it improves the ability of sponsors and CROs to evaluate the performance of clinical trial sites, and adjust visitation plans accordingly.
The CluePoints’ Intelligent Medical Coding solution further streamlines clinical trial processes, harnessing advanced deep learning technology to autonomously generate accurate code suggestions. These products improve the efficiency of data analysis and management by alleviating the burden of time-consuming, labour-intensive aspects of site monitoring activities. They help organisations to consider risk and allocate resources more accurately, making trials more time- and cost-efficient.
How is your company using emerging technologies like AI and machine learning in drug development?
As an industry leader in AI-driven risk-based quality management (RBQM) tools and software, CluePoints embeds cutting-edge machine and deep learning techniques into everything we do. Yet we also understand that technology is part of the equation. Products such as the Site Profile and Oversight Tool, which enables adaptive site monitoring, for example, uncovers the insights needed to adjust site visitation plans according to emerging risks and evolving operational needs. But it is people – people with in-depth knowledge of clinical trial data and processes – that use these insights to make the decisions that enable faster, safer, more cost-effective clinical trials. In short, we turn artificial intelligence into human intelligence.
What major trends do you see shaping the future of the pharmaceutical industry?
Everyone is talking about AI. Fast-evolving technologies such as machine learning and deep learning are fuelled by large data sets, making them perfectly suited to clinical trials. Already, they are making studies faster, safer, more efficient, and less expensive. And the constant and fast evolution of language models (in the context of GenAI) opens tons of opportunities in still unexplored territories. What is less talked about, however, is how to turn artificial intelligence into human intelligence. AI is not a stand-alone solution, but a way to provide people with the high-quality, reliable data they need to make decisions, and drive meaningful change. In the coming months, we expect the conversation to shift from the technology to how the industry builds the talent and processes needed to fully embrace its potential.
What advancements have you made in clinical trial management and data analytics?
We enhance clinical trial management and data analytics by turning artificial intelligence into human intelligence. We understand that every algorithm and every insight is a conduit for delivering real, tangible benefits. But it is just about technology – it's about leveraging AI-driven tools to turn insights into action and reshape the landscape of clinical trials.
We will launch two new products at DIA this year, both of which demonstrate our human/machine partnership approach to enhancing the safety, accuracy, and efficiency of clinical trials.
The Site Profile & Oversight Tool (SPOT) enables adaptive site monitoring, or the ability to adjust site visitation plans according to emerging risks or evolving operational needs. SPOT consolidates the data, delivers the analytics, and generates the visualizations, making it easy to spot patterns and outliers that require human interventions. It is the people who take those insights and use them to make the informed decisions that give trials their best chance of success.
Likewise, the Intelligent Medical Coding tool enhances the abilities of talented data experts. By seamlessly integrating a sophisticated machine learning (ML) model into data collection, it generates accurate suggestions for team members to either accept or reject, drastically minimizing the laborious task of manual coding, while also enhancing coding uniformity for greater accuracy and efficiency upstream.