JOAQUIM CARDOSO do rosário’s Post

“How to Make Cardiology Clinical Trials More Inclusive?” Our paper just published in Nature Medicine Link: https://lnkd.in/dafT2EbW Great collaborative effort led by Harriette Van Spall and Faiez Zannad C. Michael Gibson, M.S., M.D. Clyde Yancy Martin Cowie Kamlesh Kunthi Thomas F. Lüscher and many others.

CB Insights released their #Digital #Health #Report for #Q3 2024 • Global digital health funding reached $3.3B in Q3 2024, down 23% from Q2. • Second-lowest quarterly funding level since 2017. • Average deal size in 2024 YTD is $17.8M, up from $11.8M in 2023, indicating investors are concentrating larger sums on fewer, later-stage ventures. • Mega-rounds ($100M+) declined slightly from 9 to 7 QoQ, accounting for 30% of total funding (down from 44% in Q2). • US accounted for 52% of digital health deals in Q3, down from 61% in Q2. • Europe and Asia both saw their deal shares rise to 21% in Q3. • Two new unicorns emerged, both in Europe: UK-based Flo Health and Huma. • Digital health M&A exits continued to climb, rising 23% QoQ to 37. • The largest M&A deal was LetsGetChecked's $525M acquisition of Truepill. • Top equity deals included:  - Flo Health (UK): $200M Series C  - Alan (France): $193M Series F  - Zing Health (US): $140M Series A  - Superluminal Medicines (US): $120M Series A  - Devoted Health (US): $112M Series E • Early-stage deals accounted for 57% of all deals globally in Q3 2024. • US, Europe, and Asia all saw increases in mid-stage deals compared to previous years. • Top investors by company count included Andreessen Horowitz, Jumpstart Foundry, and Excelerate Health Ventures.

This article from News-Medicsl discusses an upcoming lecture at Aston University by Professor Maia Angelova on October 23, 2024, focusing on how AI can transform healthcare diagnosis and treatment. Professor Angelova will highlight AI-driven models that consider individual health profiles, genetics, and lifestyle to predict disease outcomes and personalize treatment. Her research includes applications in sleep disorders, diabetes, and back pain. The lecture emphasizes the importance of interdisciplinary approaches and ethical considerations in using AI for precision healthcare. The lecture is open to all and free to attend either in-person or online, but places must be booked in advance via Eventbrite. https://lnkd.in/e_QPacDq #healthcare #healthcareinnovation #healthcareit #healthcareai #ai #genai #generativeai #llm #ml #bigdata #healthcaredata #PatientEngagement #PatientCare #PatientOutcomes #MathematicalSociety

A new NEJM editorial, "The Failing US Healthcare System,” pulls no punches.   The Commonwealth Fund recently released its 8th report comparing health system performance across 10 high-income countries.   For the 8th time, the US system performed the worst overall despite spending far more than all other countries (see figure).   The US was also last or second to last regarding access to care, administrative efficiency, equity, and population health outcomes.   Our “system” leaves much to be desired. A fortunate few receive world-class care, but most Americans do not. Meanwhile, high costs (mainly due to high prices) drag down our entire economy.   It’s easy to point fingers at rapacious American corporations, inept physicians, dysfunctional teams, and more.   At the same time, it’s important to remember that healthcare only explains ~ 20% of the variance in health [Doi:10.1016/j.amepre.2015.08.024]. For example, US health outcomes are the worst despite the US ranking 2nd in care processes (e.g., preventive care, screening, and counseling).   Other factors – like healthy behaviors, physical environment, and socioeconomic factors – influence health far more than healthcare. Yet, compared to other high-income countries, the US invests relatively little in social services and public health. Maybe that's where we should direct key investments? [reference = Doi: 10.1056/NEJMp2410855]

15-Step Guide To Building High-Performing #Data and #Analytics (D&A) Teams: 1.    Define Clear #Strategy, Vision, And Goals 2.    Identify Key Roles And Structure 3.    Recruit Diverse Talent With Complementary #Skills 4.    Establish A #DataDrivenCulture 5.    Prioritize #DataGovernance 6.    Invest In The Right #TechnologyStack 7.    Focus On Scalable #DataArchitecture 8.    Invest In Continuous Learning And Talent Development 9.    Implement #Agile Methodologies 10. Implement Performance Metrics 11. Foster #Collaboration And Communication 12. Provide Access To Cutting-Edge Tools 13. Align D&A Initiatives With #BusinessStrategy 14. Encourage #Innovation And Experimentation 15. Scale Through #Automation And #AI Details are available in our Premium Content. To subscribe to our Premium Content, please either DM on LinkedIn or email: info@transformpartner.com for subscription details. Transform Partner is your trusted navigator in the turbulent seas of data and digital transformation. In an era where businesses are inundated with information yet starved for #insights, we stand as a beacon of clarity and innovation. Our consultancy brings together a team of seasoned experts with deep industry knowledge spanning government, banking and finance, healthcare, retail, and manufacturing sectors. When you partner with us, you're not just getting a roadmap; you're gaining a co-pilot for your digital journey. We provide hands-on support at every stage, from strategic planning and change management to technical implementation and post-launch optimization. Let's collaborate to turn your digital vision into reality. With Transform Partner, you're not just keeping pace with the digital revolution – you're leading it. Reach out today to schedule a personalized consultation and take the first step towards a transformative future. Image Source: Eckerson Group #TransformPartner – Your #DigitalTransformation Consultancy

Our paper got published in JAMA! 🎉 Earlier this year, Suhana Bedi Yutong Liu and I led a paper at Stanford University School of Medicine that highlights critical gaps in evaluating Large Language Models (LLMs) in healthcare. We categorized all 519 relevant studies from 1 Jan 2022 to 19 Feb 2024 into (1) evaluation data type, (2) health care task, (3) natural language processing (NLP) and natural language understanding (NLU) tasks, (4) dimension of evaluation, and (5) medical specialty. In doing so, we revealed: - Only 5% used real patient care data in their testing and evaluation. - Key tasks like prescription writing and clinical summarization are underexplored. - The focus on accuracy dominates, while vital aspects like fairness, bias, and toxicity remain largely neglected. - Only 1 study assessed the financial impact of LLMs in healthcare. Why does this matter? - Real patient care data encompasses the complexities of clinical practice, and so a thorough evaluation of LLM performance should mirror clinical performance as closely as possible to truly determine its effectiveness. - There are many high-value administrative tasks in health care that are often labor intensive, requiring manual input and contributing to physician burnout, that are currently chronically understudied. - Only 15.8% of studies conducted any evaluation that delves into how factors such as race and ethnicity, gender, or age affect bias in the model’s output. Future research should place greater emphasis on fairness, bias or toxicity evaluations if we want to stop LLMs from perpetuating bias. - Future evaluations must estimate total implementation costs, including model operation, monitoring, maintenance, and infrastructure adjustments, before reallocating resources from other health care initiatives. The paper calls for standardized evaluation metrics, broader coverage of healthcare applications, and real patient care data to ensure safe and equitable AI integration. This is essential for the responsible adoption of LLMs in healthcare to truly improve patient care. And I am delighted that I get to work on implementing the findings of this research at Coalition for Health AI (CHAI). This paper could not have happened without Nigam Shah's constant support, leadership and guidance, and that of our co-authors Dev Dash Sanmi Koyejo Alison Callahan Jason Fries Michael Wornow Akshay Swaminathan Lisa Lehmann H. Christy Hong, MD MBA Mehr Kashyap Akash Chaurasia Nirav R. Shah Karandeep Singh Troy Tazbaz Arnold Milstein Michael Pfeffer. Thank you also to Nicholas Chedid, MD, MBA Brian Anderson, MD and Justin Norden, MD, MBA, MPhil for your guidance and mentorship. And of course, huge shout out to my co-conspirators Yutong Liu Suhana Bedi - you are the best team. This is the first paper I've ever written, and I'm eternally grateful to you all for showing me how it's done. Full article here: https://lnkd.in/eimh9BNV

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O que irá ter a atenção na tecnologoa o ano que vem? No Hype Cycle de 2024 para tecnologias emergentes, examinamos inovações recentes em quatro temas principais: ➡️ IA autônoma ➡️ Produtividade do desenvolvedor ➡️ Experiência total ➡️ Segurança e privacidade centradas no ser humano Saiba o que essas tendências significam para sua organização. Gartner for IT | #GartnerIT #EmergingTech #AI #Privacy