Improving Clinical Trials

Clinical Research Needs a Reality Check, R3 Is Here Wake-Up Call: The new ICH-GCP R3 guidelines just dropped, and if you’re still running trials like it’s 2010, you’re already behind. R3 demands risk-based approaches, decentralized elements, and true patient-centricity. Yet, the industry keeps dragging its feet. Why? Because disruption is uncomfortable. What Needs to Change, Now: 1. Stop Wasting Time on Outdated Monitoring R3 prioritizes risk-based monitoring (RBM). If you’re still obsessed with 100% SDV, you’re part of the problem (minus some early phase oncology- if you know, you know). Solution: CRAs need to evolve into data-driven strategists. Equip yourself with skills in data analytics and centralized monitoring tools to spot trends before they become risks. Learn to read the signals, screen failure rates, dropout patterns, and query spikes tell a story. CRAs who identify these trends early will be the ones leading trials, not just monitoring them. 2. Decentralized Trials Are the Standard, Not a Nice-to-Have Still forcing patients into endless site visits? R3 says adapt or get left behind. Solution: Break into roles shaping the future: - Decentralized Trial Coordinator - Telehealth Study Manager - Remote Monitoring CRA 3. Patient-Centricity: Less Lip Service, More Action R3 is clear: trials must fit patients, not the other way around. Solution: Target roles like: Patient Engagement Lead, Design protocols around real lives. Your Next Move: Master R3: Knowledge of ICH-GCP R3 guidelines = competitive advantage. Target Future-Proof Roles: RBM specialists, DCT experts, and patient-centric strategists are the future of research. Think Like a Trendspotter: The best CRAs don’t just report data, they predict the next move. The Real Question: Are you disrupting the industry, or waiting to be replaced by those who will?

🚨 4,609 Studies on LLMs in clinical Medicine. Only 19 were randomized trials. A new Nature Medicine systematic review just mapped the entire evidence landscape for clinical AI -> 4,609 studies identified, but only 1,048 used real patient data and just 19 were RCTs. Most relied on simulated scenarios or exam-style tasks. -> LLMs outperformed humans in only 33% of 1,046 head-to-head comparisons, with performance dropping on more realistic tasks and against more experienced clinicians. -> OpenAI models dominated 65.7% of evaluations, and at least 25% of studies had sample sizes under 30. 👉 We need robust validation through RCTs and real-world evidence before LLMs enter clinical practice. But here's the challenge: with trial and regulatory timelines averaging many years, the model under evaluation may already be obsolete by the time results are published. So how do we generate the rigorous evidence we need for efficacy and safety without falling behind the technology curve? This is the defining question for clinical AI governance right now. How should we adapt our evidence generation and regulatory frameworks to keep pace with generative AI? Ref: Chen et al. LLM-assisted systematic review of large language models in clinical medicine. Nature Medicine 2026

> 90% of strategies claim to be patient-centric. Most don’t involve a single patient. We speak to KOLs and call it patient insight. That is still second or third hand info. Too much gets lost in translation. If patients are the people we serve, their words must shape what we plan and do. Here are five ways that may help have a more patient-centric strategy: 1️⃣ 𝗟𝗶𝘀𝘁𝗲𝗻 𝘄𝗵𝗲𝗿𝗲 𝗽𝗮𝘁𝗶𝗲𝗻𝘁𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘀𝗽𝗲𝗮𝗸 → Reddit, YouTube, Facebook, disease forums etc. → Notice recurring phrases on pain, fatigue, stigma, costs → Capture three quotes that reveal needs you had not mapped → Run a misalignment check vs your patient journey map e.g: your map says “adherence drops at month 3” Patients say “week 2 side effects make work impossible” 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗰𝗶𝗿𝗰𝗹𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗵𝗲 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 → Pharmacists see access barriers and workarounds → Nurses hear what short consults do not surface → Caregivers share logistics, burnout, hidden costs → Treat caregiver burden as its own need in your plan e.g: mornings are chaotic for carers Once-daily dosing is not nice to have. It is essential 3️⃣ 𝗣𝗮𝗿𝘁𝗻𝗲𝗿 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀 → Join forces on their events within compliance rules e.g: join the walk, offer venues, or materials they requested → Build public disease awareness with trusted groups e.g.: sponsor a short symptom video shared via appropriate channels → Co-create small tools the community asked for e.g.: plain language explainers, clinic checklists, question cards 4️⃣ 𝗕𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝘃𝗼𝗶𝗰𝗲 𝗶𝗻𝘀𝗶𝗱𝗲 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 → Create a patient word bank of real phrases and use it in slides → Run patient advisory boards to hear directly from the patients → Invite advocates or caregivers to safe internal learning sessions → If permitted and compliant, do a short observership in clinic e.g: invite a patient advocate to your monthly company townhall to raise awareness on the disease 5️⃣ 𝗠𝗮𝗸𝗲 𝗶𝘁 𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗶𝗻 𝗲𝘃𝗲𝗿𝘆 𝗽𝗹𝗮𝗻, 𝗻𝗼𝘁 𝗼𝗻𝗲 𝘀𝗹𝗶𝗱𝗲 → Tie each tactic to a documented patient need → Close the loop by showing what changed because of input e.g: include more specific patient reported outcomes in RWE Patient-centricity is not a campaign launched once a year. It is a habit that changes how we work. What else would you add from your therapy area or market? --- Follow Kalyx Medical and Myriam Cherif, PhD for more posts like this.

AI could make clinical trials faster, cheaper, and more inclusive, but success depends on explainability, interoperability, and trust. 1️⃣ 80% of trials face recruitment delays, and 50% of datasets contain quality issues; AI aims to fix both. 2️⃣ Machine learning improves protocol design accuracy (80% vs. 65%) and accelerates site selection and feasibility assessments. 3️⃣ AI tools boost enrollment by up to 65% and cut screening time by 78%, though real-world deployment can be costly and complex. 4️⃣ NLP and digital systems help identify underrepresented groups, supporting more diverse and inclusive recruitment. 5️⃣ AI-driven digital biomarkers enable 90% sensitivity in real-time safety monitoring, improving adverse event detection. 6️⃣ Risk-based monitoring powered by AI detects data integrity issues within 48 hours, much faster than manual reviews. 7️⃣ Predictive models achieve 85-90% accuracy in forecasting outcomes and enable adaptive, personalized trial designs. 8️⃣ High-dimensional, noisy, and heterogeneous data challenge AI systems; success requires strong data harmonization and validation. 9️⃣ Regulatory gaps, stakeholder distrust, and lack of explainability remain major barriers to clinical adoption. 🔟 Real-world trials show AI's promise, but also its high cost, customization demands, and integration hurdles. ✍🏻 David Olawade (MPH, FRSPH, FHEA), Sandra Chinaza Fidelis (RN, BNSc, MSc, MPH), Sheila Marinze, Eghosasere Egbon, Ayodele Osunmakinde, Augustus Osborne. Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions. International Journal of Medical Informatics. 2026. DOI: 10.1016/j.ijmedinf.2025.106141

Here’s a truth about clinical trials we don’t talk about enough: (They don’t have to be slow and costly.) You’ve likely seen it before: “Recruitment takes too long.” “Data analysis is complex.” “Improving trial outcomes is challenging.” And sure, clinical trials are complex. But here’s the game-changer—AI is transforming the process, making trials faster, smarter, and more efficient. AI in clinical trials isn’t just a trend; it’s reshaping the entire process by: → Optimizing Recruitment With AI-driven tools analyzing electronic health records, over 30% of trials are expected to use AI for faster, more precise recruitment by 2024 (source: ObvioHealth). → Enhancing Data Analysis AI processes complex data to uncover insights that traditional methods often miss. In fact, 80% of trials using AI report major improvements in reaching their primary goals (source: The Lancet). → Improving Trial Design AI-driven trial designs can reduce overall costs by up to 20%, creating safer, more effective protocols and saving valuable time and resources (source: Roots Analysis). The takeaway? AI isn’t just speeding up trials—it’s enhancing every phase, from recruitment to data analysis and beyond. As AI continues to advance, clinical trials are poised to become faster, more effective, and more impactful. Are we ready for the future of smarter trials?

I just watched an AE lose a $1.2M deal after running a "successful" product trial that the prospect LOVED. After 8 weeks of work, the CFO killed it with five words: "Let's try our current vendor." This happens because most reps treat trials as product demos instead of what they actually are: RISK ELIMINATION EXERCISES. After analyzing 200+ enterprise sales cycles at companies like Salesforce, HubSpot, Thomson Reuters, and Workday, I've identified the exact framework that separates 80%+ trial conversion rates from the industry average of 30%. Here's what most reps get wrong: They skip qualification and jump straight into the trial. Big mistake. Before any trial, ask these 3 questions: → "What happens if you don't solve this problem in the next 90 days?" → "How have you tried solving this before?" → "Who else is affected by this problem?" These eliminate 68% of unqualified trials before they start. Next, define success upfront: → Technical requirements that must work → Business metrics they expect to see → Timeline for implementation → User adoption patterns needed Get confirmation: "Just to confirm, if we demonstrate these criteria, you'd be ready to move forward with purchase by [date]. Correct?" Map every stakeholder: → Technical buyers (include every trial user) → Economic buyers (CFO/budget holder) → Political influencers (who can kill deals) → Current solution advocates (who benefits from status quo) For each person, document their personal win/loss scenarios. Have legal review agreements BEFORE starting trials. "We typically have legal review the agreement structure ahead of time so there are no surprises later. Would you be open to having them review a blank agreement while the trial is running?" Finally, handle the current vendor objection upfront: → "Have you discussed these challenges with your current vendor?" → "What was their response?" → "What specific capabilities do they lack?" Document these answers to build your business case. Results from this approach: ✅ Trial conversion: 32% to 83% in 60 days ✅ Deal size increased 40% ✅ Sales cycle shortened 37% ✅ Forecast accuracy improved 92% ✅ 43% less time on unsuccessful trials Stop running trials. Start running risk elimination exercises. — Sales Leaders! Your reps don’t need another training. They need a Revenue OS™. Check this out: https://lnkd.in/ghh8VCaf

Stop treating your CRO like a vendor - and start treating them like a partner. CROs aren't just service providers you hire and forget. Instead, they are strategic partners who can make or break your study success. Instead of: "We hired them to execute our plan."   Think: "We partnered with them to achieve our shared goals." But - what does make a sponsor-CRO relationship successful? Trust: The basis for solving problems together. When a site is struggling with enrollment, the partners brainstorm solutions as a team rather than playing the blame game. Transparency: The best sponsors give their CROs full context and not just task lists. The better I know the sponsor's goals, the better I can manage (my/your) our study. The partners have a common goal. Flexibility: We need to acknowledge that protocols may change, timelines shift, and unexpected challenges arise. The better the risk assessment, the higher the accepted need for flexibility. Respect: We must not forget that success is collective. Partnering on the sponsor side means: Choosing CROs based on capability and cultural fit, not just the lowest bid.  Investing time in relationship building, not just contract negotiations. And providing regular feedback, not just when problems arise. And CROs? They should think like owners, not contractors. They bring solutions and consult in case of challenges. They communicate proactively, especially when things go wrong. Let us be honest: Most CRO professionals entered this industry for the same reason as pharma, biotech or medtech professionals: Namely to help bringing life-changing treatments to patients. What does partnership look like in your sponsor-CRO relationship? #ClinicalResearch #SponsorCRO #Partnership #ClinicalTrials #Collaboration 

Patient‑centricity in healthcare has grown up. And that’s a good thing.   In healthcare and life sciences, we’re moving from engagement to co‑creation.   Patients are no longer being “looped in” late. Co‑creation isn’t an occasional workshop anymore—it’s becoming part of trial‑design muscle memory. When patient input is embedded early, clinical trials see ~25% faster enrollment and significantly fewer late‑stage amendments. Decentralized, patient‑friendly designs are also delivering ~20% higher retention. That’s impact—not intent.   The second shift is equally important: we’ve moved from good intentions to measurable outcomes. Patient experience is now treated as an operational lever. It’s measured, tied to KPIs, and discussed alongside timelines, cost, and risk. That signals true maturity.   The third evolution is how we use technology. We’re seeing a move from digital tools for novelty to responsible AI with purpose—designed to reduce patient burden, not add complexity. Simpler protocols. Smarter scheduling. Better listening to patient signals.   Taken together, this marks a fundamental change in mindset. Patients are being recognized for what they truly are— co‑experts in healthcare design, not just end users.   The question for leaders is no longer why patient partnership matters. It’s how deeply we’re willing to embed it into how we work, decide, and build. #PatientCentricity #PatientExperience

🧠 What if doctors could talk to clinical AI tools like a colleague? That’s the vision behind a fascinating new paper out in Frontiers in Artificial Intelligence — and it's one of the most compelling cases yet for how LLMs could transform clinical workflows, not by replacing doctors, but by making AI tools actually usable. Instead of LLMs making risky predictions on their own, this study explores a smarter role: 👉 LLMs as interactive interfaces that guide clinicians through trusted tools like QRisk3, AutoPrognosis, and clinical guidelines — all via natural language. Here’s why this matters: 💬 Usability is the real bottleneck Digital tools fail when they’re clunky. A conversational interface that can explain, adapt, and assist — in real time — is a usability leap forward. 🔍 Trust comes from transparency The LLM doesn't make the call — it pulls from validated models and guidelines, showing its sources along the way. That’s huge for explainability. 📉 Hallucinations? Nearly gone. By grounding responses in external tools and documents, the system answered >99% of clinical questions correctly — vs. ~44–75% for standalone LLMs. 📊 It works — and scales From calculating personalized risk to simulating “what if” scenarios, the system supports deep, patient-specific reasoning — all in plain language. 🔄 From tool → teammate It’s not just about automation. It’s about augmentation — giving clinicians a smarter, more natural way to work with digital tools. This feels like a glimpse of where clinical AI is actually headed: Not flashier algorithms — but better interfaces. 📄 Full paper attached #LLMs #AIinHealthcare #DigitalHealth #ClinicalAI #NLP #HealthTech #ExplainableAI #HumanCenteredAI #MedicalInnovation #MachineLearning

🔬 Towards Decentralized and Privacy-Preserving Clinical Trials 🧠💡Register, learn and build Decentralization in clinical research is not just about scalability or cost-efficiency. It’s a cryptographic transformation that redefines trust and data sovereignty in medical innovation. Technologies like Zero-Knowledge Proofs (ZKPs) and Fully Homomorphic Encryption (FHE) are enabling a new paradigm in decentralized trials: ✅ Privacy without compromising verification: With ZKPs, patients can prove eligibility (inclusion/exclusion criteria) without revealing their full medical history. Compliance is validated without exposing sensitive data. ✅ Computation over encrypted data (FHE): FHE allows researchers to run statistical analyses and predictive models directly on encrypted datasets. No need to decrypt—privacy is preserved even during processing. Ideal for multicenter trials or pharmacogenomic studies. ✅ Traceability without surveillance: Combining blockchain with ZK/FHE enables immutable and auditable recording of clinical events (dosage, adverse effects, outcomes) without identifying the patient. 🌐 In this new model: Data stays where it’s generated (edge computing, patient devices) No centralized data hoarding or exposure risks GDPR and similar regulations are met by design, not workaround 📣 If you're working at the intersection of digital health, cryptography and clinical innovation, this is the future: crypto-technology powering secure, precise, and ethical research. #ZKProofs #FHE #DeSci #DecentralizedTrials #PrivacyByDesign #Web3Health #DigitalTrust #Blockchain #ClinicalResearch #HealthTech Anthony Joaquim José Daniel Dr. Hidenori Vivek Helena Lars Yousuke Carlos Iker Paris João Domingos