Raul Mora

Market Access Value Design through Design Thinking The complexity of challenges is escalating, and uncertainties are mounting. The introduction of #designthinking, a method anchored in the core principles of empathy and iteration, provides an additional tool for crafting innovative solutions in the healthcare sector. This is particularly relevant for market access value design, fostering co-creations, and considering #businessecosystems, all aimed at diminishing the degree of uncertainty from high to low. Topics where Design Thinking can be beneficial: 🔶Value Design: Demonstration of drug value to all stakeholders, including patients and healthcare providers 🔶 Market access: Ensuring access to new therapies by overcoming authorisation and reimbursement hurdles. Minimising risks in the approval of medicines 🔶 Holistic approach: Consideration of the entire patient pathway, from drug development to application 🔶 Patient-centredness: Active involvement of patients in research and development 🔶 Patient perspectives: Integration of patient experiences in the planning and implementation of studies 🔶 Technology-enabled approaches: Utilising technological advances to capture health outcomes and patient preferences 🔶 Creative passion: Merging patient voices with scientific rigour for relevant and impactful study designs 🔶 The aim is to create patient care that is optimal and deeply human I’m glad you found inspiration in the book” The Design Thinking Toolbox: A Guide to Mastering the Most Popular and Valuable Innovation Methods" Michael Lewrick, Larry Leifer, Patrick Link The book is available in several languages e.g.English and German #DesignThinking #HTA #pharmabusiness #AMNOG #pharmapatientcentricity #healthcareshapers

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Excellent article by my colleague Julia Sundari, highlighting the significance of sustainability in clinical trials and how intentional digital adoption can enhance it. If you are exploring digital solutions in this space, this is a must-read. Consider leveraging your investment to further your green initiatives. #clinicaltrials #sustainability #digitaladoption #appliedclinicaltrials

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🌟 First Lesson Taught in Harvard MBA in 18 Minutes | Thales Teixeira 🌟 I recently watched Thales Teixeira’s insightful video on the first lesson taught in Harvard’s MBA program, and it’s packed with valuable takeaways for marketers and entrepreneurs. Here are my thoughts on some key lessons: 1. Structured Innovation is Key: It’s easy to rely on gut feelings, but a structured approach to disruption can make all the difference. It increases your chances of success! 📈 2. Understand the Customer Journey: Mapping out the customer value chain helps us pinpoint weak links that are ripe for innovation. Where can we make things easier for our customers? 🔗 3. Decouple Activities for Impact: By separating value-creating activities, we can focus on what truly matters—delivering an amazing customer experience. This can lead to exciting new business models! ⚙️ 4. Attracting Investment: Startups that disrupt the value chain often catch the eye of investors. It’s a reminder that innovation is attractive to those looking to fund new ideas. 💰 5. Addressing Pain Points: Understanding where customers struggle—be it cost, time, or effort—opens the door to creating solutions that really resonate. 🕵️‍♂️ 6. Using AI Strategically: AI can be a game changer if we use it to enhance value where customers face challenges. Making things easier and faster is always a win! 🤖 These insights remind me that in marketing, we have the power to disrupt and innovate. Let’s create real value for our customers! If you're interested, check out the full video here: https://lnkd.in/gyS-EPHu #DigitalDisruption #Marketing #CustomerExperience #Innovation #Startups

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Publicly available AI models at the current stages cannot be trusted at all! Depending on the way your prompt is phrased, you can get incorrect/biased responses. This lack of objectivity can further strengthen biases and incorrect information on the Internet. Accountability is a MUST for AI models to become reliable.

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Where Data-Driven Decision-Making Can Go Wrong Five pitfalls to avoid By Michael Luca and Amy C. Edmondson Published in Harvard Business Review, September–October 2024 Decision-making and problem-solving Summary by the authors “When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.” https://lnkd.in/eBvcdmA2 #Data #DataDriven #DataDrivenDecision #DataDrivenDecisionMaking #DataApproach #DataAnalysis #DataOutcome #DataDecisions #DecisionMaking #ProblemSolving

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Advances in AI have caused a seismic shift from a world in which answers were crucial to one in which questions are. The big differentiator is the ability to craft smart prompts. Authors share a practical framework for the five types of questions to ask during strategic decision-making: investigative, speculative, productive, interpretive, and subjective

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Despite the emphasis on effective communication, listening often plays a more critical role than speaking. In the latest PMI Newsletter, I shared my thoughts on the art of listening with the speaker's ears. Would like to hear from you how we can enhance our listening skills to foster better communication #empathy #communication #initforlife #hovione #PMIPortugal

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Unleash the power of diverse perspectives with the Seven Questions technique! 💡 - Conduct interviews under the Chatham House rule to ensure anonymity and honest feedback. - Ask the Seven Questions to uncover key strategic issues and areas of agreement or conflict. - Use the insights to inform your policy development and decision-making. Frank Kumli - thank you for insightful and thought provoking post. #strategicplanning #stakeholderengagement #sevenquestions #policydevelopment #innovation #leadership

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🔍 FAIR is dead. Long live FAIR ! No matter the terminology—cleaned, linked, open, or shared data—the FAIR principles (Findable, Accessible, Interoperable, and Reusable) are key to unlocking the full potential of both legacy and modern datasets. FAIR principles apply on data, not IT systems. Measuring FAIR maturity is essential to showcase its value. Unfortunately, the term has been misused, with "fake FAIR" spreading and diluting its significance. #Biomedima sparks a conversation summarising the history and evolution of FAIR principles and associated requirements to yield #AddedValue. ➡️ Read more here: https://lnkd.in/eJVhaKji 🌟 Latest Episodes of BioMedima Digital Transformation Series:  - Episode #1 - https://lnkd.in/eFiYqJec  - Episode #2 - https://lnkd.in/eJc-ifed  - Episode #3 - https://lnkd.in/eGi4bBvP #FAIR #FAIRprinciples #Digitaltransformation #DataStrategy #Knowledgegraph #Sustainability #ProjectManagement #Collaboration #Harnessingcomplexity #DigitalProduct

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#Science could solve some of the world’s biggest problems. Why aren’t #governments using it? A Nature Magazine global survey finds that most specialists are unhappy with systems to provide science advice to #policymakers. Killer #viruses. #Artificialintelligence. Extreme #weather. Microplastics. #Mental health. These are just a few of the pressing issues on which governments need science to inform their policies. But the systems that connect scientists with politicians are not working well, according to a Nature survey of around 400 science-policy specialists around the world. Eighty per cent said their country’s science-advice system was either poor or patchy, and 70% said that governments are not routinely using such advice. “Every country is asking how we can do science and scientific advice,” says Jeremy Farrar, chief scientist at the World Health Organization in Geneva, #Switzerland. Five years after the #COVID-19 #pandemic exposed the importance of strong links between scientists and policymakers, the challenges to providing advice have grown. Spiralling mis- and disinformation risks obscuring science advice, while anti-science sentiment is eroding trust in experts and evidence — a phenomenon that scientists worry will worsen in the #USA . #science #research #policy #STEM #extremeweather #mentalhealth #vaccines #trump

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🔍 Unlocking the Power of Context Modelling ! Having examined the intricacies of defining impact and the added value of an endeavor, BioMedima is now reflecting on the significance of modeling and considering context in all business decisions. This approach is essential for any business aiming for more sustainable practices. ➡️ Read more here: https://lnkd.in/eJc-ifed 🌟 Latest Episodes of BioMedima Digital Transformation Series: Episode #1 - Added-value - https://lnkd.in/eFiYqJec #Biomedima #Digitaltransformation #Datastrategy #Knowledgegraph #Sustainability #ProjectManagement #Collaboration

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🚀 Transforming Drug Discovery: The AI Revolution In the rapidly evolving landscape of pharmaceutical R&D, artificial intelligence (AI) is proving to be a game-changer. A recent published in Drug Discovery Today has highlighted some groundbreaking trends that underscore the potential of AI in revolutionizing drug discovery and development*. Exponential Growth in AI-Driven Discoveries Over the past decade, there has been a significant surge in the number of drugs and vaccines discovered through AI. This growth is not just in numbers but also in the speed with which these discoveries are moving from the lab to clinical trials. AI-native biotech companies, often in collaboration with big pharma, are leading this charge, showcasing an impressive annual growth rate in clinical entries*. Doubling Success Rates One of the most striking findings from the study is the success rate of AI-discovered molecules in Phase I clinical trials, which stands at 80-90%. This is nearly double the historical average*. Such high success rates in early trials are indicative of AI's capability to design molecules with optimal drug-like properties, potentially transforming the economics of drug development. Accelerating Drug Development AI is not only improving success rates but also significantly speeding up the drug discovery process. Traditional methods take approximately 4-6 years to discover new small molecules. With AI, this timeline is dramatically reduced. For instance, a recent example from the industry showed that a drug candidate was developed in just 8 months using AI, compared to the years it would typically take**. The Future is Now As we look to the future, the integration of AI in drug discovery promises not only faster development times and improved success rates but also a potential shift in how we approach complex diseases. With every pharmaceutical giant now investing in AI, the next decade could see a dramatic transformation in how new therapies are developed, tested, and brought to market. Call to Action For those of us in the life sciences sector, the message is clear: embracing AI is no longer optional but a necessity to stay competitive and lead in innovation. The opportunities to improve patient outcomes and reduce development costs are immense and within our grasp. Let's discuss how we can further integrate AI into our strategies to not only keep up with the pace of innovation but to set new benchmarks in drug discovery and development also with tools such as our #AIDDISON solution #Digital #AI #ArtificialIntelligence #DrugDiscovery #DrugDevelopment #Technology #Innovation #creativity #futurism https://lnkd.in/gdrdCNfC * https://lnkd.in/gbaiwB4X ** https://lnkd.in/gfF2kuAk

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“Most organizations are dipping a toe into the AI pool—not cannonballing. Slow progress toward widespread adoption is likely due to cultural and organizational barriers. But leaders who effectively break down these barriers will be best placed to capture the opportunities of the AI era. And—crucially—companies that can’t take full advantage of AI are already being sidelined by those that can, in industries like auto manufacturing and financial services. To scale up AI, organizations can make three major shifts: ….”

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Data Governance Act explained The economic and societal potential of #data is enormous: it can enable new products and services based on novel technologies, make production more efficient, and provide tools for combatting societal challenges. In the area of #health, for example, #data can contribute to providing better #healthcare, improving personalised treatments and helping cure rare or chronic diseases. It is also a powerful engine for #innovation and new jobs, and a critical resource for start-ups and SMEs. However, this potential is not being realised. #data sharing in the EU remains limited due to a number of obstacles (including low trust in #data sharing, issues related to the reuse of public sector #data and #data collection for the common good, as well as technical obstacles). In order to truly capitalise on this enormous potential, it should be easier to share #data in a trusted and secure manner. The Data Governance Act (DGA) is a cross-sectoral instrument that aims to regulate the reuse of publicly/held, protected #data, by boosting #data sharing through the regulation of novel #data intermediaries and by encouraging the sharing of #data for altruistic purposes. Both personal and non-personal #data are in scope of the DGA, and wherever personal #data is concerned, the General Data Protection Regulation (GDPR) applies. In addition to the GDPR, inbuilt safeguards will increase trust in #data sharing and reuse, a prerequisite to making more #data available on the market. #data #health #healthcare #innovation

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𝗔𝗜 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗛𝗶𝘁𝘁𝗶𝗻𝗴 𝗮 𝗪𝗮𝗹𝗹? 𝗡𝗢 🚀 This is just media hype,guess they need something to fill pages during the slow holiday season 😉 𝗙𝗼𝗿 𝗮𝗹𝗹 𝗺𝘆 𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗳𝗿𝗶𝗲𝗻𝗱𝘀: 𝗜𝗚𝗡𝗢𝗥𝗘! Here’s what’s really going on: 1️⃣ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Yes, there are discussions (including the recent NeurIPS talk by Ilya) suggesting that training efficiency might be approaching limits. 2️⃣ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: However, we are nowhere(absolutely nowhere) near hitting any limits on inference scaling. Inference is the phase where AI generates outputs and insights is how we all consume AI. And there’s still massive room for improvement here. 3️⃣ 𝗔𝗜 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗜𝘀 𝗡𝗼𝘁 𝗦𝘁𝗮𝗹𝗹𝗶𝗻𝗴: Far from it! We are light years away from reaching inference limits. By the time we do, we’ll have discovered the next transformative architecture or breakthrough to push progress forward. 4️⃣ 𝗧𝗵𝗶𝗻𝗸 𝗼𝗳 𝗜𝘁 𝗧𝗵𝗶𝘀 𝗪𝗮𝘆: 𝗛𝘂𝗺𝗮𝗻 𝗕𝗿𝗮𝗶𝗻: Training is like learning new concepts, it's resource intensive and time consuming. Inference is applying that knowledge in real-life situations. During inference, we build on what we’ve trained and use additional brain power to adapt and solve tasks we were not explicitly trained for,like reasoning through a new problem or making a split-second decision. We are not constrained by our training. 𝗔𝘁𝗵𝗹𝗲𝘁𝗲: Training is countless hours of practicing skill. Inference is game day, where the athlete adapts in real time to expected & unexpected challenges. They leverage what they’ve trained for but also rely on their ability to process new inputs on the fly to achieve peak performance. AI is the future and here to stay! #AI #ScalingLaws #ArtificialIntelligence #GenerativeAI #TechLeadership #DigitalTransformation #Lifesciences #Pharmaceutical

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