Lokavant

Correlation vs. Causation in Clinical Trials: Why It Matters and How #CausalAI Can Transform Our Approach You’ve probably heard it before – correlation is not causation. In the world of clinical trials, understanding the difference between correlation and causation is vital. As we embrace more advanced forms of #AI, this distinction grows even more critical. A correlation may point us in one direction, but without establishing causation, we risk following a misleading path that can waste resources and delay treatment breakthroughs.    This is where CausalAI steps in, enabling us to dissect complex relationships and uncover the true drivers behind trial outcomes. Traditional AI models fixate on repetitive patterns, but CausalAI empowers us to ask deeper questions and get actionable, prescriptive insights by highlighting not just causative factors, but also the expected effects or consequences upon modifying those factors, rather than just coincidental links or historical associations. For instance, CausalAI can address whether we can truly expect improved recruitment rates by loosening a particular eligibility criteria threshold, or whether there are other hidden, confounding factors involved.    Imagine the potential: With CausalAI, trial sponsors can confidently redesign eligibility criteria, optimize country and site selections, and adjust other protocol attributes during prestudy planning and also on the fly. By doing so, we’re not just improving the operational and logistic aspects of trials but ensuring that outcomes are grounded in the true drivers of patient recruitment, participation, and overall operational trial success. This leads to faster, more efficient trials, ultimately delivering effective treatments to patients sooner, and achieving greater value to sponsors and CROs    The promise of CausalAI is clear: it can cut through the noise of mere correlations, guiding us toward trusted, interpretable, and actionable insights. This capability is essential as we strive for better clinical outcomes and more effective therapies in an industry where 90% of new drugs never make it to market. It’s time we harness this transformative power to move from guesswork to grounded insight, improving trial quality and success rates. As clinical trials grow more complex, we must adopt tools like CausalAI to guide our decisions and maximize the value of every insight we gather. Read more here 👉https://hubs.ly/Q02Trr4g0 #ClinicalResearch #DigitalHealth #PharmaInnovation #Biotech #CausalAI #ClinOps 

Mo Ali, Head of Reporting Analytics & Insights at Astellas Pharma, on why investing in alternative, AI-powered data sets to help improve your feasibility efforts is a "win-win" in clinical trials: "I feel with the advent of AI and ML that iterative work in terms of data crawling and identification and mapping, could be done a lot faster."   #ClinicalTrials #AI #ClinicalResearch

Selecting clinical trial sites used to rely on traditional, outdated methods that focused solely on fixed data, like quantitative and categorical data from pick lists. This approach was straightforward but limited. Thankfully, with the rise of #AI in analytical tools, we’re no longer stuck in that box. These innovative tools: 💡 empower us to effectively process and analyze unstructured data 🔎 provide deeper insights into a site’s true potential to successfully enroll a trial 📈 allow us to seamlessly integrate both quantitative and qualitative data. Consequently, these tools deliver a richer, more accurate picture of site capabilities, enabling us to make more precise projections. Otis Johnson, PhD, MPA #clinicaltrials #clinicalresearch

🚨 New Blog Post Alert 🚨 👉 Aaron Mackey, our VP of AI & Data Science, breaks down the difference between correlation and causation, and how Causal AI is helping determine key connections in healthcare and clinical trials. 🚀 While generative AI has been a hot topic for designing molecules and drafting regulatory content, it comes with limitations—mainly, it struggles with distinguishing correlation from causation. And in healthcare, that distinction is crucial. That’s where Causal AI comes in. It’s designed to pinpoint true cause-and-effect relationships, offering deeper insights that can help us optimize trial design, site selection, patient recruitment, and more. Imagine being able to adjust eligibility criteria confidently, optimize visit schedules, and allocate budgets with a precise understanding of how these changes will impact trial outcomes. 📈 Causal AI allows us to see beyond simple correlations, uncovering hidden variables and providing actionable insights that can make the difference between a successful trial and a missed opportunity. Read more here 👉https://hubs.ly/Q02Trr4g0 #ClinicalTrials #AI #clinicalresearch

🚀 Exciting insights from the latest episode of The Scope of Things Podcast! 🎙️ Host Deborah Borfitz dives into how AI can help with diversity in trial planning with Lokavant's VP of AI and Data Science, Aaron Mackey. During this episode, Aaron and Deborah discuss: ❓ The unintended consequences of decisions made during trial planning that can lead to questionable conclusions 🔎 How AI and ML are helping with the diversity issue in trial participation 🚦 His stop gap emergency plan to keep trials on track if there is no digital support available Catch the full episode for a deeper dive into the difference between Correlation and Causation, and how Causal AI is helping determine key connections in clinical trials! https://hubs.ly/Q02SMjk10 #ClinicalTrials #AI #clinicaltrialdiversity

✈️ We're off to the charming town of West Chester, Pennsylvania for the Innovation Network Gathering 2024 conference, happening tomorrow and Friday! We're thrilled to join a vibrant community of industry innovators this year and dive into daily discussions on #AI, technology & service quality, #patientexperience, and much more. 🎙️ Our CEO, Rohit Nambisan, will take the stage as a featured host, sharing insights on how Spectrum™ is pioneering a new frontier in #clinicalfeasibility. 🚀 See you at #thegathering! #innovationnetworkgathering #clinicaltrials

👉 At DPHARM last week, Aaron Mackey SVP of AI & Data Science, delivered an insightful presentation on Causal-AI's impacts in optimizing #ClinicalTrials Feasibility. 📢 We had a great time at DPHARM in Philly last week! It was fun connecting with conference attendees and listening to some amazing speakers about the upcoming innovations in #clinicalresearch. #DPHARM2024 #ClinicalTrials #AI

Reducing protocol complexity is critical to improve the patient experience in #clinicaltrials. Did you know that... 1. The average protocol is 62+ pages 2. Study participant visits have increased >37% in Phase III Trials and 3. There are about 263 procedures per participant Today we can improve the study planning process by leveraging AI-Optimizations and reduce the participant and site burden impacting our trials. Michelle Everill #ClinicalResearch

We can't wait to see you at DPHARM in Philadelphia TOMORROW! 🎙️Don't miss our presentation, "Causal-AI: Optimizing Clinical Trial Feasibility with AI to Determine the Best Course of Action," by Lokavant's SVP of AI & Data Science, Aaron Mackey tomorrow, September 17th at 12:05 PM EST. See you in Philly! 🏙️ #DPHARM2024 #ClinicalTrials #AI #ClinicalResearch