Katharina Mahadeva Cadwell’s Post

An interesting article that highlights the concerning absence of patient engagement in the strategies involving the integration of AI into healthcare stating, "Organizations should actively engage consumers to understand their most critical pain points and understand what AI solutions they are willing to use,” they advised. “This can be achieved by gathering direct consumer input and conducting focus groups while iteratively building, testing, and deploying new products. The process should be led with transparency, equity, and collaboration.” In the context of healthcare and AI, patient engagement is indeed crucial for several reasons: 👍 Patient-Centered Care: Involving patients in decision-making ensures that healthcare solutions and AI technologies align with patients' needs and preferences, ultimately leading to more patient-centered care. 👍 Improved Outcomes: Engaged patients are more likely to adhere to treatment plans and follow healthcare recommendations, leading to better health outcomes. 👍 Ethical Considerations: Ethical issues can arise when using AI in healthcare, such as data privacy and bias. Engaging patients can help address these concerns and ensure that AI is used ethically. 👍 Transparency: Involving patients in the decision-making process regarding AI in healthcare promotes transparency, helping patients understand how AI is used and how it affects their care. 👍 Trust Building: Patient engagement fosters trust between healthcare providers, AI developers, and patients. When patients are part of the decision-making process, they are more likely to trust the technology and their healthcare providers. https://lnkd.in/gZP3mZ9E

𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞: 𝐀𝐈 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐔𝐬𝐡𝐞𝐫𝐬 𝐢𝐧 𝐭𝐡𝐞 '𝐍𝐞𝐰 𝐍𝐨𝐫𝐦𝐚𝐥' 𝐟𝐨𝐫 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧! AI Integration in Healthcare: 🏥 2023 marked a significant year for AI in #healthcare, with its integration into mainstream workflows, notably in ambient dictation, task management, and clinical decision support. Regulators are expected to incentivize the use of #AI for comprehensive patient care, enhancing safety and quality. 📈 Meaningful Use of AI: 💡 The concept of "meaningful use" is evolving, with a focus on #AIpowered data summarization and insights to provide a complete patient view. This approach aims to improve patient care quality and safety, with regulatory bodies likely to support this transition. 🎯 Explainable AI as a Priority: 🤖➡️👀 The #healthcare sector is moving towards responsible, explainable #AI, recognizing its role in augmenting clinician-patient interactions rather than replacing them. The focus is on AI systems that provide understandable insights and allow clinicians to make the final decisions, especially in light of concerns about AI-generated inaccuracies. 🛡️ Economic Pressures Accelerating Tech-Enabled Payment Models: 💸📊 The inflationary environment of 2023 is pushing healthcare towards t#echenabled, advanced payment models like value-based care. This shift is driven by rising costs and the need for more efficient care delivery, with health plans and providers increasingly adopting these models to control total care costs. 🔄 Technological Advancements and Value-Based Care: 🚀🏥 The healthcare sector is expected to continue embracing technological advancements and value-based care strategies to manage rising costs and labor shortages. This trend is seen as a response to the macroeconomic pressures and the need for more sustainable healthcare delivery models. 💡 Gregory Stone, Charles Weidman, John Hatchell, Saikat Das, Alexander Poston, Bilal S., Gabriela Pelin, Vian Nguyen, MD, Byron Haney AI in 2024: Welcome to the 'new normal' in healthcare

🔥 𝗚𝗿𝗲𝗮𝘁 𝗪𝗼𝗿𝗸! Check out this piece by Brian Anderson, MD, CEO of the Coalition for Health AI (CHAI). This short read summarizes CHAI’s vision for Quality Assurance (QA) Labs to ensure responsible AI in healthcare. Here are some highlights: 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 💥 “The rapid pace of innovation in artificial intelligence (AI) presents a challenge: how to foster groundbreaking advancements while ensuring their safe and ethical use.” 💥 “Technology vendors, public sector regulators and healthcare systems must come together to agree on standards and approaches so that health systems can make informed decisions on what AI tools are safe for their patients.” 💥 “Healthcare providers are rightfully cautious about adopting novel, under-regulated technologies only with proper validation…Clear and unbiased information is therefore crucial for them to have confidence in new tools, technological or otherwise.” 💥 “AI developers must [also] work with underserved communities to respect data sovereignty and restore underserved communities’ due place in the training and deployment of algorithms.” 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 🎯 “QA Labs are being setup to evaluate models for use in healthcare, using an agreed set of community best practices for trustworthy health AI, such as those developed by CHAI and the National Academy of Medicine’s AI Code of Conduct.” 🎯 “Through rigorous testing, QA Labs will expose populations that models perform badly with, and ensure the use of diverse training sets with representative data.” 🎯 “QA Labs will work in partnership with technology vendors to develop a set of tools and services for lower-resourced health systems to ensure that all stakeholders develop AI that ultimately serves the whole population.” 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 🏆 “QA Labs can be a source of independent validation, based on a set of shared, consensus-driven frameworks for how to measure safety and efficacy, so that AI will enhance, not impede, clinical quality and patient safety.” (Link to source in the comments.)

Great post on an article that highlights the vision of the Coalition for Health AI (CHAI) for establishing Quality Assurance (QA) Labs to ensure responsible AI in healthcare. It discusses the challenges of rapid AI innovation and the need for standards to ensure safety and efficacy in healthcare applications. Quality assurance labs aim to evaluate healthcare AI models, ensuring they meet community best practices for trustworthiness. Innovation in AI requires collaboration among technology vendors, regulators, and healthcare systems to establish safety standards. Community involvement is crucial, especially working with underserved communities to respect data sovereignty and ensure equitable AI deployment. AI can significantly enhance healthcare quality by fostering innovation while ensuring safety and ethical use. However, its crucial that AI involves rigorous testing, using diverse data sets for training, and developing consensus-driven frameworks for measuring safety and efficacy. This approach ensures AI enhances clinical quality and patient safety.

🔥 𝗚𝗿𝗲𝗮𝘁 𝗪𝗼𝗿𝗸! Check out this piece by Brian Anderson, MD, CEO of the Coalition for Health AI (CHAI). This short read summarizes CHAI’s vision for Quality Assurance (QA) Labs to ensure responsible AI in healthcare. Here are some highlights: 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 💥 “The rapid pace of innovation in artificial intelligence (AI) presents a challenge: how to foster groundbreaking advancements while ensuring their safe and ethical use.” 💥 “Technology vendors, public sector regulators and healthcare systems must come together to agree on standards and approaches so that health systems can make informed decisions on what AI tools are safe for their patients.” 💥 “Healthcare providers are rightfully cautious about adopting novel, under-regulated technologies only with proper validation…Clear and unbiased information is therefore crucial for them to have confidence in new tools, technological or otherwise.” 💥 “AI developers must [also] work with underserved communities to respect data sovereignty and restore underserved communities’ due place in the training and deployment of algorithms.” 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 🎯 “QA Labs are being setup to evaluate models for use in healthcare, using an agreed set of community best practices for trustworthy health AI, such as those developed by CHAI and the National Academy of Medicine’s AI Code of Conduct.” 🎯 “Through rigorous testing, QA Labs will expose populations that models perform badly with, and ensure the use of diverse training sets with representative data.” 🎯 “QA Labs will work in partnership with technology vendors to develop a set of tools and services for lower-resourced health systems to ensure that all stakeholders develop AI that ultimately serves the whole population.” 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 🏆 “QA Labs can be a source of independent validation, based on a set of shared, consensus-driven frameworks for how to measure safety and efficacy, so that AI will enhance, not impede, clinical quality and patient safety.” (Link to source in the comments.)

🔥 𝗚𝗿𝗲𝗮𝘁 𝗪𝗼𝗿𝗸! Check out this piece by Brian Anderson, MD, CEO of the Coalition for Health AI (CHAI). This short read summarizes CHAI’s vision for Quality Assurance (QA) Labs to ensure responsible AI in healthcare. Here are some highlights: 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 💥 “The rapid pace of innovation in artificial intelligence (AI) presents a challenge: how to foster groundbreaking advancements while ensuring their safe and ethical use.” 💥 “Technology vendors, public sector regulators and healthcare systems must come together to agree on standards and approaches so that health systems can make informed decisions on what AI tools are safe for their patients.” 💥 “Healthcare providers are rightfully cautious about adopting novel, under-regulated technologies only with proper validation…Clear and unbiased information is therefore crucial for them to have confidence in new tools, technological or otherwise.” 💥 “AI developers must [also] work with underserved communities to respect data sovereignty and restore underserved communities’ due place in the training and deployment of algorithms.” 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 🎯 “QA Labs are being setup to evaluate models for use in healthcare, using an agreed set of community best practices for trustworthy health AI, such as those developed by CHAI and the National Academy of Medicine’s AI Code of Conduct.” 🎯 “Through rigorous testing, QA Labs will expose populations that models perform badly with, and ensure the use of diverse training sets with representative data.” 🎯 “QA Labs will work in partnership with technology vendors to develop a set of tools and services for lower-resourced health systems to ensure that all stakeholders develop AI that ultimately serves the whole population.” 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 🏆 “QA Labs can be a source of independent validation, based on a set of shared, consensus-driven frameworks for how to measure safety and efficacy, so that AI will enhance, not impede, clinical quality and patient safety.” (Link to source in the comments.)

Generative AI in Healthcare: 2024 Survey by Gradient Flow The 2024 reveals varying levels of GenAI adoption across different groups and company sizes. The future of GenAI in healthcare looks promising, with the potential to revolutionize patient care, streamline operations, and accelerate research. However, success depends on navigating challenges, prioritizing ethical considerations, and developing secure, privacy-preserving solutions tailored to the healthcare industry's unique needs. Continued investment, collaboration, and thoughtful implementation will be key to realizing the full potential of GenAI in healthcare. #LLMs #HealthcareLLMs #nocode #GenerativeAI #HealthcareAl #MedicalLLMs #nIp #naturallanguageprocessing #DataSc ience #DataEngineering #DataAnalysis #Datalnsights #TextProcessing #DeepLearning #Dat aScience

Generative AI in Healthcare: 2024 Survey by Gradient Flow reveals varying levels of GenAI adoption across different groups and company sizes. The future of GenAI in healthcare looks promising, with the potential to revolutionize patient care, streamline operations, and accelerate research. However, success depends on navigating challenges, prioritizing ethical considerations, and developing secure, privacy-preserving solutions tailored to the healthcare industry's unique needs. Continued investment, collaboration, and thoughtful implementation will be key to realizing the full potential of GenAI in healthcare. #LLMs #HealthcareLLMs #nocode #GenerativeAI #HealthcareAl #MedicalLLMs #nIp #naturallanguageprocessing #DataSc ience #DataEngineering #DataAnalysis #Datalnsights #TextProcessing #DeepLearning #Dat aScience Generative Al in Healthcare Survey 2024 GenAl Budget Increases

Hey everyone! I’m excited to share our latest HIT Perspectives article, AI in Healthcare: Understanding Its Position on the Gartner Hype Cycle. We dive deep into how AI is transforming the healthcare industry and its place on the Gartner Hype Cycle. Right now, AI is making a difference in reducing administrative burdens, especially in clinical documentation. In many ways, this has been a game-changer in alleviating clinician burnout. However, while these advancements are promising, broader clinical applications of AI still face significant technical and ethical challenges, which means widespread adoption is on a slower track. Here are a few key takeaways: - What's Working Well: AI is making a big impact in administrative tasks, easing the load on healthcare providers. - Areas of Caution: Clinical decision-making applications still face hurdles. - Ethical Considerations: Issues like data privacy and AI bias need careful attention. At POCP, we’re doing more to help our clients understand how AI can fit into their technology roadmaps. We’re also focused on navigating the risks and current policy requirements related to AI. There are no silver bullets, but AI has a lot of potential to help reduce burdens in healthcare. If your organization is struggling with where to use AI and perhaps where to hold off, reach out to me. I'd love to set up time with one of subject matter experts to see if we can help. #HealthcareAI #GartnerHypeCycle #AIinHealthcare #TechInMedicine #ClinicalAI #EthicalAI https://hubs.li/Q02Gb82N0