ctcHealth’s Post

3. AI-Driven Targeting in Clinical Research Advertising 🤖 AI is rapidly transforming clinical trial recruitment by offering more precise targeting. Studies reveal that 86% of clinical trials face recruitment delays, but AI can help reduce these obstacles by efficiently identifying qualified participants. AI tools use data analytics to process vast datasets and predict who is most likely to participate, cutting down recruitment times and reducing costs by as much as 30-40%. AI doesn’t just optimize targeting; it also helps clinical trial teams anticipate and address recruitment challenges before they become major issues. Beyond patient recruitment, AI-driven targeting can be used to personalize engagement with participants, ensuring that communications are tailored to the needs and preferences of each individual. This approach leads to higher retention rates and better trial outcomes. ISOCRa educates teams on how to harness AI to not only find participants faster but also keep them engaged throughout the trial process.

Healthcare Global Enterprises Limited and Accenture collaborate on cancer research and care using AI Link in the comments Dr. B S Ajaikumar Senthil (Sen) Ramani #mediabrief #HealthcareGlobalEnterprises #Accenture #AI #digital #technology #marketing #media

Sanofi collaborates with OpenAI and Formation Bio to Introduce Muse, a first of its kind AI tool to accelerate patient recruitment in drug development: 💊Muse is an AI-powered tool designed to improve Patient Recruitment in Clinical Trials, reducing the time required for recruitment strategy and content creation from months to just minutes 💊Muse uses multiple OpenAI models to compile research on diseases, demographics, and the therapeutic landscape, analyzes the data to identify ideal patient profiles and recruitment strategies, and auto-generates customized recruitment materials and prescreening questionnaires adaptable for different languages and styles 💊Muse incorporates Institutional Review Board (IRB) and regulatory guidelines to ensure that the recruitment materials comply with best practices AI safety and data privacy are also embedded, with no use of personally identifiable information 💊Sanofi will be the first to implement Muse in Phase III studies for multiple sclerosis (MS) Formation Bio will also use it in upcoming trials, with the broader goal of applying it across multiple therapeutic areas 💊Muse aims to expand clinical trial participation, particularly among underrepresented populations, capturing broader data and providing more generalizable insights that better reflect patient needs 💊The AI-driven approach aims to enhance efficiency in recruitment, thereby accelerating the development of new treatments and potentially speeding up the delivery of new therapies to patients 👇See articles in comments #DigitalHealth #AI #Pharma

🔍 Want to transform your data into powerful insights effortlessly?
Noah AI makes it easier than ever to generate comprehensive market research and clinical trial insights using our advanced AI models tailored for the healthcare industry. 🚀 Integrate the best data analytics capabilities into your applications.
Our platform now features the latest advancements in AI-driven analytics, including: 🌐 Get a Quick Market Understanding of an Indication * Receive structured outlines for various indications, like Type 2 Diabetes, detailing epidemiology, current treatments, and emerging therapies. 🔬 Streamline Principal Investigator (PI) Selection with AI * Identify key PIs for clinical trials with accurate and reliable insights, avoiding common pitfalls like AI hallucinations. 🚀 Explore our platform and see how Noah AI can elevate your healthcare insights: https://www.noahai.co

🏥 34% of HCPs utilize AI/ML to identify under/misdiagnosed patients–HCP Engagement and Patient Identification with Advanced Analytics In today’s data-rich environment, life science leaders are poised with more resources than ever—abundant real-world data paired with cutting-edge analytics tools. Yet, finding and engaging the right customers, whether patients or healthcare providers (HCPs), at the right time remains a challenge. Why is this, and what can be done about it? A survey conducted by Real Chemistry with studioID reveals a surprising gap: only 34% of respondents utilize AI/ML to identify undiagnosed or misdiagnosed patients, whereas 53% deploy these technologies to locate new patients for their products. There is a significant opportunity to better utilize AI and ML tools to tap into previously inaccessible patient segments. Here are some key strategies and insights: 1️⃣ Leveraging Machine Learning for Patient Discovery Most biopharma companies are adept at identifying patients within narrowly defined parameters. However, utilizing AI and ML can uncover patients who might be undiagnosed, misdiagnosed, or about to switch treatments—potentially transforming patient care in areas like rare diseases. 2️⃣ Understanding HCPs Beyond Prescriptions Traditional HCP datasets often lack depth, reducing the effectiveness of engagement strategies. By integrating AI-enhanced real-world data, companies can gain insights into HCPs as individuals, enhancing engagement strategies significantly. 3️⃣ Coordinated Omnichannel Approach Identifying potential patients and HCPs is just the start. The real challenge lies in delivering coordinated, data-driven messaging across multiple channels to activate these targets effectively. Advanced analytics platforms like Real Chemistry’s Swoop are redefining how messages are tailored and delivered, ensuring relevance and timing are optimized. 🎯 Case Study Highlight: AVEO Oncology AVEO Oncology's use of @Real chemistry IPM.ai showcases the power of targeted analytics. By predicting therapy changes 2-3 months in advance, AVEO was able to engage HCPs with timely messages, leading to a significant increase in patient conversion. Approximately 47% of new patient starts in Q1 2022 were traced back to these predictive alerts. Want to dive deeper into the companies that are using predictive analytics to transform patient engagement in oncology? Connect with us. #DataScience #MachineLearning #PatientCare #HCPengagement #RealChemistry #Analytics

We’re excited to share that Chase Feiger, M.D. Feiger will speak this Thursday in Princeton, NJ at the PanAgora Pharma #CXONCOLOGY Summit! Chase will explore how AI and personalization can transform HCP and patient interactions, creating frictionless, high-value engagements. He’ll also discuss how oncology brand teams can leverage AI-powered digital solutions to easily yet impactfully ensure meaningful, actionable results Stay tuned for more insights from this discussion! #AI #Personalization #ProductiveEngagement

Ever wondered how AI is revolutionizing the healthcare landscape? 🚀 Artificial Intelligence is not just a buzzword—it’s transforming clinics in ketamine therapy, psychedelic therapy, PRP, Med Spas, and Stem Cell therapy. Here’s how: 🔍 **Enhanced Operations & Treatments:** AI optimizes how treatments are administered and streamlines clinic operations, ensuring efficiency and precision. 🤖 **Personalized Patient Engagement:** With AI-driven marketing automation, clinics can tailor their communication, standing out in a competitive market and fostering meaningful patient relationships. 📊 **Data-Driven Decisions:** Leveraging AI for data analysis enables clinics to make informed decisions, target marketing efforts effectively, and allocate resources wisely. 💬 **Revolutionary Patient Communication:** AI chatbots provide instant responses to patient inquiries, enhancing their experience and freeing up staff for more complex tasks. ✍️ **Advanced Content Marketing:** As traditional ads lose traction, AI helps create engaging, localized content that resonates with patients, driving organic reach and trust. At Alcyone Marketing, we blend engineering precision with artistic creativity to deliver AI-powered solutions like chatbots and booking bots. Our tools help clinics streamline operations, enhance patient experiences, and boost visibility without the hassle of traditional advertising. Ready to elevate your clinic with AI? Let’s embrace the future together and set your practice apart as a leader in health and wellness. 🌟 #HealthcareInnovation #AIinHealthcare #ClinicMarketing #PatientEngagement #AlcyoneMarketing

Gilead Sciences Expands AI Partnership with Cognizant to Enhance Internal Efficiency and Accelerate Drug Development >> 🔘Gilead Sciences is expanding its partnership with Cognizant aiming to enhance customer service, employee engagement, and overall business value through advanced AI-driven solutions 
🔘The expanded agreement incorporates machine learning and generative AI to streamline Gilead’s operations, enabling cost savings and productivity improvements 🔘The IT company will also take charge of programs intended at expediting digital transformation for boosting client experience and quicker time to market for medicines of Gilead. 
🔘Cognizant’s Neuro AI platform will be implemented to optimize IT and business processes, transforming the user experience with custom-built automation and AI solutions 
🔘While this partnership focuses on digital transformation rather than digital health, Gilead aims to accelerate the development and market launch of treatments for serious diseases, including cancer, HIV, and viral hepatitis. The resulting cost savings can also be reinvested into its core life sciences business 
🔘The usage of GenAI is intended to offer improved transparency about Gilead’s medicines to patients as well as internal staff across the globe 👇links to related articles in comments below #digitalhealth #pharma #ai

AI is transforming healthcare—but are we empowering patients enough? Generative AI tools like ChatGPT and Perplexity are giving patients newfound autonomy, creativity, and agency in managing their health. We believe the focus should shift from controlling patient use of AI to collaborating with them to reshape healthcare. Patients aren’t just managing their care—they’re driving progress and reimagining the system. Here’s how we can make it happen: ✅ Trust patients’ ability to use AI responsibly. ✅ Ensure equitable access to inclusive, user-friendly AI tools. ✅ Collaborate with patients to co-create healthcare solutions. ✅ Build trust with transparency and education. ✅ Celebrate the progress patients are already achieving. Generative AI is a tool for partnership, not oversight. Together, we can harness its potential to create a more innovative, inclusive healthcare system. 🔗 https://lnkd.in/dDZrSid9 #PatientsUseAI #HealthcareInnovation #AIHealthcare #healthcareAI #ResponsibleAI #HealthTech

My LinkedIn feed is so dichotomously split with AI in healthcare, but I noticed two main character classes for crunching data with AI: 1) Those engaged in simply talking about AI or create “thought leadership” and “hype” to sustain the market valuation and role - we can smell you from miles away. 2) Those who understand technical strengths and limitations are leveraging its strengths to build and test. AI a power tool in your tool bag, you use it for necessary tasks, but when you have the insight you need hammer, you switch. I generally don’t feel fancy algorithms itself can be a “product” especially in knowledge-evidence-pairing is often “strict typed” - as in industries like healthcare need a pairing of some discretized feature values / thresholds-to-action. (e.g. starting diabetes medication at A1c > 8 vs A1c = 5.0 🤨 - can turn into medicolegal matter) If clinicians find that some fancy algorithm does a good job of risk stratifying, we usually find out why - by studying patient characteristics of scored patients that’s actionable and responsible. The goal turns into dual task of gut check of “did the algorithm work” and “what features are important” - breaking down the algorithm and then slicing and dicing all the features that an advanced academic center AND that run-down safety net hospitals both have access to (e.g. eGFR for CKD, histology based grading for cancer like Gleason score, and simpler measurements like lung cancer tumor size > 7.0cm) This is how we come up with things like cancer staging - based on survival stratification (5 year survival - stage 1-4) which is adjusted every decade or so (typically by AJCC/WHO) with recent trend being in applying molecular markers for additional characteristics. When a the new category (e.g new cancer stage of Stage 2A) is defined - there’s no need for the actually algorithm that did the stratification work and possibly had clustered cohorts groups together. Algorithm helps us already delivered the key insight, but doesn’t have to be a product that’s used in the vast majority of cases. (Netflix algorithm challenge comes to mind). It should definitely be in some workflows to study and understand disease, patients, and to confirm whether our standard of care could be better. But doesn’t have to be a product. Feel like it’s become my personal goal to try to help understand healthcare tech companies and patients recognize this artifact that arises from the gap of business vs medicine.