Badri Raghavan

👉🏼 The Digital Technology in Clinical Medicine: From Calculators to ChatGPT 🤓 Somarajan Anandan 👇🏻 https://lnkd.in/gtRmmq8z 🔍 Focus on data insights: - Utilization of digital technology in clinical medicine - Impact of digital information technology on healthcare delivery 💡 Main outcomes and implications: - Advancements in clinical medicine due to digital technology - Need for balance between technology, clinical expertise, and humanistic values 📚 Field significance: - Regulatory push for improvements through clinical trials - Projects like "World Health Organizations Guidelines for Digital Interventions" and "Food and Drug Administration's Digital Health Center of Excellence" highlighting efforts in digital health services 🗄️: [#digitaltechnology #clinicalmedicine #healthcaredelivery]

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🏥 🔬 KPIs (Key Performance Indicators) for Generative AI Application Output & Performance Evaluation in Healthcare and Life Sciences A successful GenAI adoption strategy would warrant tracking KPIs for all stages of the application design - LLM Evaluation, Prompt Engineering & RAG Architecture, and GenAI Application Output Quality. In this 3-part series, I am covering KPIs for LLM evaluation (https://shorturl.at/fjGTZ), Prompt Engineering & RAG Design (https://shorturl.at/sDIR2), and GenAI Application Output quality. In the healthcare and life sciences industry, leveraging generative AI (GenAI) applications can significantly enhance patient outcomes and research initiatives. To ensure your GenAI applications deliver value, consider tracking and evaluating some of these Key Performance Indicators (KPIs): 1. 📈 User Engagement: Monitor end user interactions with your application, assessing active users, session duration, and retention rates. High engagement signals a valuable, user-friendly application. 2. ⏱ Response Time: Digital transformation and the widespread adoption of social media have raised user expectations from all software applications. Measure the time it takes for your application to respond to user queries, striving for faster response times to improve adoption. 3. 🎯 Successful Completion Rate: Track the number of queries successfully completed by your application, ensuring your application delivers accurate, reliable information to support healthcare and life sciences professionals in their work. Add thumbs up/down buttons to solicit feedback per inference. 4. 🛑 Error Rate: Minimize errors to maintain trust and reliability. Regularly log and monitor failed queries to ensure optimal performance. 5. 🔍 Personalization: Measure the application's ability to adapt its responses to individual users' needs and preferences. Tone and output format style flexibility that is tailored to individual's needs, will lead to better stickiness and adoption. 6. 📊 Scalability: Evaluate your application's capacity to handle increasing demand, enabling seamless adoption across the user base. Well architected review is a must! 7. 📝 Regulatory Compliance: Ensure your GenAI solution adheres to industry regulations and GxP guidelines. Monitoring compliance KPIs is essential for product safety, efficacy, and overall quality. 8. 🤝 Human-in-the-Loop Testing: Incorporating human expertise during testing & validation phases will help refine application performance and build confidence for real-world healthcare and research use. 9. 🔗 Workflow integration: Incorporating the application within existing clinical workflows will encourage purpose-built scope, higher adoption, and opportunities to enhance/evolve the application rapidly. 💬 Share your experiences and insights on tracking performance of GenAI systems in the comments below! 📢 Subscribe to my newsletter for insights on GenAI adoption: https://lnkd.in/g3bdneR7 #genai #ai

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Great to see KP on the list of health systems in the US "that are pioneers in demonstrated, outcomes-based AI solutions." While so many companies are tripping over themselves in positioning themselves as "AI-first" the secret for KP's success is that we're decidedly NOT AI-first. We're a people-first organization. Our core mission is delivering high-quality, affordable and equitable care for our 12.5M members and to make KP the best place to work for our 230k+ employees. We deploy technologies to drive those outcomes and we use AI, sparingly and cautiously, when simpler technologies can't get the job done. AI is also a team-sport and KP's success in this area reflects the work of thousands of employees across the organization. People like Jennie Shin who led the national IT team deploying our ambient scribe technology to 25,000 clinicians or Anna Davis and Carol Cain who led our quality assurance testing for our roll-out. And it's particularly gratifying to be recognized alongside my close colleague and friend Vincent Liu. His leadership in AI at KP has been both sustained and truly outcomes-driven. There are many other physician leaders that also deserve to be recognized - Khang Nguyen, Kristine Lee, MD, Brian Hoberman MD MBA, Dr. Ainsley MacLean, Chris Cable, MD, Dinh Nguyen, MD, MSHI and so many more.

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👉🏼 Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications 🤓 Khadijeh Moulaei 👇🏻 https://lnkd.in/e9N9Y2CK 🔍 Focus on data insights: - GAI models like ChatGPT, Google Bard, and Microsoft Bing AI were frequently employed in healthcare. - Various applications of GAI in healthcare were identified, including offering insights on health conditions and disease diagnosis. - Benefits and challenges of GAI in healthcare were explored, highlighting the importance of data accuracy and information reliability. 💡 Main outcomes and implications: - Providing rapid access to information and valuable insights. - Improving prediction and diagnosis accuracy. - Addressing challenges related to generating inaccurate content and ensuring data reliability. 📚 Field significance: - GAI has the potential to revolutionize healthcare by enhancing diagnosis, treatment, and care through advanced data insights. - Understanding the benefits and challenges of GAI is crucial for maximizing its potential in healthcare settings. 🗄️: [#artificialintelligence #healthcare #datainsights #applications #challenges]

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AI tools, especially newer GenAI tools, can augment clinical diagnosis in so many ways - it can help patients seek care faster, it can improve communication between patients and their providers, it can also augment clinical decision-making to avoid premature closure and other such cognitive biases. On the other hand, these tools can also inadvertently exacerbate existing health disparities and result in patient harm. Getting this right is critical. Diagnostic errors are the number one cause of medical errors experienced by patients in the US, with 1 in 20 US adults experiencing a delayed or inaccurate diagnosis EVERY YEAR. Using new technologies to improve clinical diagnosis is a must, but we need to proceed with caution and vigilance. Join us on July 25th for a free, virtual webinar hosted by the The National Academies of Sciences, Engineering, and Medicine exploring the role of digital health and AI technologies to improve diagnosis. It's been a huge privilege serving as chair of an esteemed steering committee of national experts in clinical diagnosis and AI technologies - including Michael Howell, MD MPH, Julia Adler-Milstein, Judy Gichoya, Maia Hightower, MD, MPH, MBA, David Larson, Gene Harkless and Thomas Cudjoe, MD, MPH, MA. We've got an incredible line-up of speakers - including Michael Howell (Google), John Whyte (WebMD), Jonathan H. Chen (Stanford), Kadija Ferryman, PhD (Hopkins), Eric Horvitz (MSFT) and my boss Andy Bindman (Kaiser Permanente)! More info and registration can be found ⬇ Hope you can join us! UCSF Coordinating Center for Diagnostic Excellence (CoDEx)

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🚨New #JAMA Viewpoint 🚨 “Misaligned Pharmacy Incentives in Value-Based Care” The authors discuss how rising #prescription drug spending has been particularly challenging in the context of value-based contracts between payers and providers. This is partly due to confidential #rebates, which create misaligned incentives between payers/PBMs and hospital administrators. Read more below: 🔗 https://lnkd.in/dwzBT4WD #marketaccess #pbm #access #pricing #payer #valuebased #valuebasedcare #patientjourney #patientaccess #pharmacy #managedcare #pharma

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I had the privilege of moderating a webinar on AI in health care with international leaders representing the perspectives of regulators, care delivery systems and industry / developers of AI technologies. The panelists were Khang Nguyen, Chief Clinical Transformation Officer at SOUTHERN CALIFORNIA PERMANENTE MEDICAL GROUP, David Rhew, M.D. Global CMO at Microsoft Health, and Erick Lin, MD, PhD, MBA - Medical Officer at the FDA. In our wide-ranging conversation, a few takeaways: 1) we discussed the importance (and dearth) of high quality evidence to demonstrate safety and effectiveness of AI tools (and whether the FDA's bar for evidence is too low); 2) the potential of using AI to empower patients to better self-manage their care (with guardrails) 3) the work of collaborative efforts like TRAIN - to develop technology solutions to enable responsible AI programs at health systems of all sizes. If you didn't get a chance to attend the webinar, I'm happy to share a recording of the conversation here ⬇ https://lnkd.in/gb3xDBbB Kudos to Karin Cooke, Vivian Tan and the excellent team at Kaiser Permanente International for hosting this important conversation.

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Health care systems often face persistent communication barriers that tend to impact marginalized patients. But by analyzing a broad range of data, AI has the potential to identify at-risk patients early, predict outcomes, and optimize treatment pathways. This proactive approach ensures that disadvantaged groups receive timely, tailored care, reducing hospitalizations and improving health outcomes. AI is only as good as the data it's trained on, however. in order to avoid unintended biases in AI deployment, there needs to be careful, constant oversight from AI developers, clinicians, and other relevant parties. #AI #Healthcare #HealthEquity

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Glad to see AdventHealth in this top 25 list, an intriguing study from CB Insights creating an AI Readiness Index. While there are objective methods used to estimate both #Innovation and #Execution, these may not reflect the recent efforts of systems like AdventHealth to accelerate AI transformation: 🩺 Clearly defined use cases to improve the workflow of clinicians, leading to less burnout and better patient experience, such as adoption of Nuance Communications for smarter approaches to documentation. 🩻 Seamless integration into diagnostic and EHR platforms; our single instance of Epic is a major accelerant. 👨‍💻 Investment in data scientist, AI engineer, and process engineer talent to accelerate change significant technical talent from UCF College of Engineering & Computer Science. ⚖️ Governance and risk frameworks to create trust, enabling speed and scale in adopting AI tools for well-defined use cases; and for the counsel of advisors like David Rhew, M.D. along the way. 🤝 Deepening partnerships with tech or other enablers of AI adoption such as AdventHealth’s continued work with Microsoft, Coalition for Health AI (CHAI), Siemens Healthineers, Premier Inc., Vizient, Inc, Trilliant Health, and others. #ai #aireadiness #leadership #healthcare #hospitals #digitaltransformation #processengineering #datascience #machinelearning #transformation #changemanagement AdventHealth Central Florida AdventHealth Colorado AdventHealth West Florida UChicago Medicine AdventHealth #responsibleai

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UAE: Pioneering the Future as the Global Hub for AI in Healthcare In recent years, the UAE has rapidly positioned itself as a global leader in Artificial Intelligence, with a particular emphasis on revolutionizing healthcare. As a senior healthcare professional with a passion for real-world evidence, innovation, and digital business development, I am thrilled to witness and contribute to this transformative journey. The UAE's strategic vision is clear: harnessing the power of AI to enhance healthcare outcomes, streamline operations, and create patient-centric solutions. This vision is being realized through substantial investments in AI infrastructure, fostering a robust ecosystem for startups and innovators, and establishing collaborations with leading global technology firms and educational institutions. Key initiatives, such as the UAE's National Strategy for Artificial Intelligence 2031 and the establishment of the AI Lab by the Dubai Health Authority, are testaments to the nation's commitment to integrating AI into the healthcare sector. These initiatives aim to reduce the burden of chronic diseases, predict and manage health crises, and ultimately, improve the quality of life for citizens and residents. Prominent healthcare organizations like M42 and PureHealth are at the forefront of this AI revolution. M42, known for its cutting-edge technology solutions, is driving significant advancements in healthcare delivery through AI integration. PureHealth, the UAE’s largest healthcare network, is leveraging AI to enhance patient care, optimize operations, and deliver personalized health solutions. The UAE's forward-thinking approach is also evident in its regulatory framework, which is designed to support the safe and ethical implementation of AI technologies. This balanced approach ensures that innovation thrives while patient safety and data privacy remain paramount. As we move forward, I am excited about the endless possibilities that AI brings to healthcare. From predictive analytics and personalized medicine to advanced diagnostic tools and automated workflows, AI is set to redefine the healthcare landscape. The UAE's proactive stance not only sets a benchmark for other nations but also attracts global talent and investments, further solidifying its position as the epicenter of healthcare AI. In conclusion, the UAE's journey to becoming a global hub for AI in healthcare is a remarkable example of visionary leadership, strategic investment, and a relentless pursuit of excellence. Organizations like M42 and PureHealth are pivotal in this journey, showcasing the UAE’s commitment to pioneering healthcare innovation. I am honored to be part of this journey and look forward to the groundbreaking innovations that lie ahead. #UAE #AI #HealthcareAI #Innovation #DigitalHealth #FutureOfHealthcare #AIinHealthcare #GlobalLeadership #Vision2031 #M42 #PureHealth

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This isn't a post about #AI, but about data foundation and #interoperability in #healthcare. The demonstration of AI through #LLM is not sexiest part of this presentation. There's no silver bullet, we should know that at this point. Instead, I draw your attention to the rich discussion around the applicability of #OpenEHR and #HL7 #FHIR standards, and the importance of understanding why each of these #standards was developed and what for. Data definitions, data schemas, and how we apply standards to structure and represent information, both in storage and transportation, are key. Any further comments from me would be spoilers. I hope you enjoy the presentation and share your point of view, folks. Have a great week!

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Great insight by my colleague David Devine 🚀 Transforming Healthcare with Data Analytics 🚀 The North American healthcare analytics market is projected to soar to $11.4 billion by 2023, up from $3.13 billion in 2018. Despite this growth, many healthcare providers are still catching up with the advancements seen in other sectors, such as medical devices and payers. Key Highlights: Real-Time Decision Making: Leveraging basic patient data to make immediate care decisions. Predictive Analytics: Using machine learning to predict and reduce hospital readmissions, saving billions annually. Combating the Opioid Crisis: Data tools are crucial in monitoring e-prescribing practices and integrating with state prescription drug monitoring programs. Future Strategies: Moving beyond traditional data sets to include less structured data, such as genomic and social data, for a comprehensive analysis. Healthcare leaders must think, plan, and act differently to harness the full potential of data analytics. By advancing data strategies and focusing on both structured and unstructured data, we can transform patient care and outcomes. Let’s embrace the power of data to create a more efficient, patient-centric healthcare system! 🌟 https://lnkd.in/ebu35PAh #Healthcare #DataAnalytics #MachineLearning #AI #BigData #HealthTech

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