How Technology Transforms Clinical Trial Monitoring

Reading recent World Economic Forum white paper that explores how Gen AI can transform clinical development and improve patient outcomes. Key challenges in clinical development: • Long timelines: 8-12 years on average • High costs: Over $2.5 billion per new treatment • Low success rates: Only 10-15% of drugs succeed Gen AI's potential impact: ·  Clinical trial design optimization ·  Improved trial feasibility and site selection ·  Enhanced patient recruitment and retention ·  Streamlined data analysis ·  Accelerated regulatory submissions The paper identifies five key processes ripe for transformation: Clinical trial design (highest impact, long-term) • Gen AI to mine unstructured data for protocol development • Creation of digital endpoints and synthetic control arms Trial feasibility and site selection (medium impact, medium-term) • Predictive models for site selection and patient recruitment • Enabling decentralized trials Clinical operations (medium impact, medium-term) • Personalized participant engagement strategies • Automated site burden reduction Data analysis (low to medium impact, medium-term) • Automated data cleaning and integration • AI-powered statistical analysis and code generation Regulatory submission (medium impact, short-term) • Automated generation and validation of submission filings • Predictive algorithms for regulatory success Barriers to implementation: • Data fragmentation and quality issues • Lack of regulatory frameworks • Insufficient incentives for data sharing • Workforce skill gaps • Trust and cultural resistance Key Case Studies • Insilico Medicine's inClinico: AI platform predicting clinical trial outcomes with 80% accuracy, enabling better prioritization of therapeutic programs • Amgen's ATOMIC: AI-driven tool enhancing clinical trial site selection efficiency, optimizing trial design and increasing success probability. •Mass General Brigham's COPILOT-HF: AI application screening heart failure patients for trial eligibility with 100% accuracy, reducing screening costs to $0.11 per patient. • Eisai and Medidata Collaboration: AI-powered platform accelerating data review by up to 80%, enabling scaling of trial complexity while maintaining data quality. • Moderna's RegBot: AI solution streamlining health authority interactions, reducing administrative burden on regulatory affairs teams Recommendations: ·Create standards for data collection and sharing · Establish centralized data hubs · Develop incentives for data sharing · Implement smart AI policies Source: https://lnkd.in/eMMjMJS5 Disclaimer: The opinions are mine and not of employer's: #GenerativeAI, #clinicaltrials, #therapeuticinnovation, #drugdevelopment, #datasharing, #regulatorysubmission, #decentralizedtrials, #patientrecruitment, #trialdesign, #feasibility, #clinicaloperations, #dataanalysis, #healthcarecosts,#innovation

🌟 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗶𝗻 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗧𝗿𝗶𝗮𝗹𝘀: 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗻𝗰𝗶𝗹𝗹𝗮𝗿𝘆 𝗧𝗲𝗰𝗵 🌟 Welcome to the first post in our series on 𝘉𝘦𝘴𝘵 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘦𝘴 𝘧𝘰𝘳 𝘔𝘢𝘴𝘵𝘦𝘳𝘪𝘯𝘨 𝘊𝘭𝘪𝘯𝘪𝘤𝘢𝘭 𝘛𝘳𝘪𝘢𝘭 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺! Today, we’re diving into how ancillary technologies can revolutionize clinical trial execution, saving time, reducing errors, and enhancing outcomes. At CREO, we’ve been pushing boundaries to deliver smarter solutions for CROs, SMOs, and biotechs—here’s a glimpse of what’s possible: 🔹 𝘙𝘰𝘣𝘰𝘵𝘪𝘤 𝘗𝘳𝘰𝘤𝘦𝘴𝘴 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 (𝘙𝘗𝘈): We’ve automated study-specific email routing to eISF and eTMF systems, enabled self-scheduling for participants via CTMS-integrated portals, and used EMR screen scraping to seamlessly update patient records. 𝘖𝘶𝘵𝘤𝘰𝘮𝘦: Eliminated manual tasks, boosting operational efficiency, cutting errors, and speeding up study management. 🔹 𝘗𝘰𝘸𝘦𝘳 𝘉𝘐 𝘧𝘰𝘳 𝘗𝘢𝘵𝘪𝘦𝘯𝘵 𝘗𝘳𝘰𝘧𝘪𝘭𝘦𝘴: By pulling data from eSource, EDC, and eCOA, we created custom patient profile reports tailored for medical monitoring. 𝘖𝘶𝘵𝘤𝘰𝘮𝘦: Standardized, comprehensive profiles that accelerate monitoring, improve accuracy, and drive higher customer satisfaction. 🔹 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘦𝘥 𝘐𝘙𝘉 𝘚𝘶𝘣𝘮𝘪𝘴𝘴𝘪𝘰𝘯 𝘗𝘳𝘰𝘤𝘦𝘴𝘴: We developed a PowerPoint template with Power BI integration to transform reporting data into ready-to-submit IRB decks. 𝘖𝘶𝘵𝘤𝘰𝘮𝘦: Slashed prep time from 3 weeks to 2 hours, reducing costs and freeing up medical monitoring teams for critical work. These innovations show how the right tech can transform trials. At CREO, we’re here to guide you in leveraging tools like RPA, Power BI, and more to unlock efficiency and precision in your studies. 💬 What ancillary tech are you exploring in your trials? Share your experiences in the comments—I’d love to hear your insights! Stay tuned for the next post in our series, where we’ll explore seamless tech deployment strategies. #ClinicalTrials #ClinicalResearch #CRO #SMO #Biotech #eClinical #AIinHealthcare #RPA #PowerBI #CREOSolutions #CREO

The McKinsey Global Institute estimated that #genAI could generate $60 billion to $110 billion a year in economic value for the #pharma industry. Specifically in clinical development, they touted smarter trials, better data, quicker results could create a $13-25 billion opportunity. According to McKinsey, genAI addresses can increase efficiency across the entire clinical-development process, unlocking economic value across three dimensions: - up to 50% cost reductions enabled by the streamlining of clinical-trial processes and auto-drafting trial documents; - a 12-plus month acceleration in the time it takes to conduct a trial; - and at least a 20% increase in NPV, thanks to enhanced health authority interactions, quality control, and improved signal management. They identified 4 use cases that can drive this impact: - Trial performance co-pilot: Gen AI can rapidly analyze vast quantities of structured and unstructured data. It is therefore a powerful study-team companion, which can share insights and suggest effective interventions to improve the outcomes of clinical trials. - Smart data management: Data management today is a highly labor-intensive process, requiring manual trial-by-trial configuration of electronic data-capture systems, as well as detailed review and reconciliation of incoming patient data. By combining traditional and generative AI capabilities, data management can be automated across multiple steps. - Regulatory intelligence engine: Gen AI–enabled intelligence engines can help across three fronts: predicting potential Health Authority Queries (HAQ) patterns for a given submission; rapidly crafting appropriate sponsor responses; and providing deeper intelligence to submission strategies. - Major submission content writer: Drafting the clinical-study reports typically requires eight or more weeks to complete. Gen AI–based tools can cut this time almost in half by generating an “80 percent right” first draft from the underlying protocol, statistical-analysis plan, and tables, listings, and figures. #clinicaltrials #clinicaloperations

AI's entry into clinical trials could bring about a seismic shift in accelerating drug development, promising quicker patient access to new treatments. ➜ A new report from Nature.com (Springer Nature Group) by Matthew Hutson explores how AI is being leveraged to drive more efficient clinical trials by helping write protocols, recruit patients and analyze data. Here are some of the findings: — AI algorithms and large language models like GPT-4 are now pivotal in designing trials, predicting outcomes, and streamlining the selection of drug dosages and patient criteria. — AI can assist in managing vast amounts of trial data more effectively, from predicting patient dropout rates to creating digital patient twins, which reduces the need for control groups. — AI tools like Trial Pathfinder and Criteria2Query can help expedite patient selection, significantly cutting down recruitment times and broadening eligibility, thus accelerating the trial process. Importantly, the article notes that AI in clinical trials faces challenges such as bias, data privacy concerns, and the risk of over-reliance on technology, underscoring the need for balanced and responsible AI use. Continued advancements only amplify the imperative to ensure the technology is employed responsibly. Ensuring unbiased, accurate data is critical in order to realize AI’s full potential without compromising patient safety and trust. Read the full article: https://lnkd.in/eYt3_Y7g #healthcareai #healthcare #healthtech #responsibleai

8 Examples where Pharma is Using AI to Enhance Clinical Trials >> Pharma’s greatest use of AI is in drug development, but optimising clinical trials is an important and growing focus, as these recent examples illustrate 🔘 Bristol Myers Squibb extended its partnership with Medidata Solutions to enhance clinical trial management. BMS will adopt Medidata Clinical Data Studio and explore AI, advanced analytics, and data tools to optimize trial efficiency. This builds on their 2016 collaboration supporting cancer and other trials 🔘 Eisai US also partnered with Medidata Solutions on an AI-driven platform to streamline clinical trial management, reduce errors by 80%, and accelerate treatment development for cancer and Alzheimer's. The platform replaces spreadsheets with integrated data sources, aiming to improve patient experience and data accuracy 🔘 Eli Lilly and Company's Digital Health Hub in Singapore leverages AI tools like Magnol.AI to advance drug discovery for Alzheimer’s, autoimmune diseases, and cancer, while supporting Phase 1 clinical trials and real-time monitoring 🔘 AstraZeneca partnered with Immunai to enhance cancer drug trials using its AI platform, which maps the immune system. The collaboration leverages Immunai's machine learning and single-cell biology to improve clinical decision-making and accelerate immunotherapy development 🔘 AstraZeneca's new business Evinova launched, offering AI and health-tech solutions to enhance clinical trials, with support from Accenture and AWS 🔘 AbbVie collaborated with ConcertAI and Caris Life Sciences to enhance precision oncology by utilizing AI for clinical trials and patient enrollment. 🔘 Sanofi partnered with COTA to use real-world data and AI to enhance clinical trials for multiple myeloma, aiming to speed up the development and improve the design of future studies 🔘 Sanofi in collaboration with OpenAI and Formation Bio introduced Muse, an AI tool to streamline patient recruitment for clinical trials by identifying ideal profiles, generating materials, and ensuring regulatory compliance 👇Links to source articles in comments #DigitalHealth #Pharma #AI #ClinicalTrials