Tanzeem Choudhury

Here’s your recap of last week’s health IT news 🗞️ 👇 🥼AI Clinician Assistant • Suki : The AI scribe announced an SDK and APIs enabling developers to integrate ambient documentation into their applications. • Apricot: The gen AI pre-seed startup focused on reducing documentation overhead for home health nurses launched. • Tampa General Hospital + Palantir Technologies: The health system and the software platform are partnering to develop an AI care coordination and decision support platform. ☎️ AI Contact Center • Mediktor + Sensely: The AI symptom triage solution acquired the conversational member engagement platform. 📋  Patient Administration • Oracle (Cerner) + Loyal: The EHR now offers integration with the patient engagement solution through its Healthcare Marketplace. • HubSpot: The CRM and marketing platform announced it will now offer HIPAA support and sensitive data tools. 🏥 Provider and Practice Administration • LeanTaaS: The AI-enabled facility management solution released updates including: mobile-first live view of OR schedule, predictive staffing automation, and a gen AI solution allowing decision-makers to glean staffing and scheduling insights. ⚕️ Clinical Operations • Keragon: The HIPAA-compliant automation platform launched out of stealth with a $3M funding round. 🧑‍💻 Virtual and At-Home Care • AvaSure + CLEW: The virtual care platform is partnering with the AI-powered remote patient monitoring and analytics company to improve patient outcomes. 🍎 VBC • Evolent + Machinify, Inc.: The VBC org acquired AI Authorization assets from the AI operating system platform.    • ProgenyHealth, LLC + Wildflower Health: The maternity and NICU case management solution is partnering with the women’s health VBC enabler to “help health plans fill gaps, collapse silos and integrate into existing clinical and operational workflows.”    • Health Catalyst + Carevīve: The population health data and analytics company has completed acquisition of the cancer care management solution. 🧠 Food for Thought: • This article provided an interesting look at Nebraska Medicine’s efforts to design a next-generation hospital room, including the four technologies they’ve selected to anchor their work. https://lnkd.in/em-u-a5v    • McKinsey released a survey of 200 global health system executives on their investment priorities. Among the notable findings, 88% listed AI as an area of high potential impact, yet only 40% have implemented any solution thus far and over 20% don’t have plans to invest in the next two years. https://lnkd.in/eK3CeqQ2

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🚀 Big tech giants 𝐀𝐦𝐚𝐳𝐨𝐧, 𝐆𝐨𝐨𝐠𝐥𝐞, 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭, 𝐚𝐧𝐝 𝐍𝐯𝐢𝐝𝐢𝐚 are diving into the $11T healthcare market, each with unique strategies. 2024 Big Tech in Healthcare Report delves into their partnerships, strategic investments, and product launches. #HealthcareInnovation #BigTech #Amazon #Google #Microsoft #Nvidia #HealthTech #FutureOfHealthcare

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I'm incredibly honored to represent Kaiser Permanente as a keynote speaker for the upcoming AI + Health Conference - Stanford University's largest convening for AI in health and medicine - organized by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). In my remarks, I plan to discuss the critical importance of trust and how we build trust, and why, without it, even the most advanced AI technologies will fall short of transforming health care. I'll definitely be staying for the entire program with dedicated tracks covering "Clinical AI Applications in Use Today" "Integrating AI into Healthcare Settings" "Fundamentals of AI in Healthcare" and "Pioneering Innovations in Health AI." This is an online conference on Dec 10 + 11th. There is a registration fee to join and CME will be provided for physicians. Hope you can join the conference! https://lnkd.in/gHmDfCHS

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Here's what leadership in healthcare AI looks like.

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Key trends and takeaways from HLTH 2024: 1. AI's Evolving Role: AI dominated discussions, with executives acknowledging its potential while also recognizing challenges in accuracy and clinician trust. There's a shift towards focusing on the practical applications of AI, with a call for partnerships to ensure reliable testing and implementation. 2. Infrastructure Focus: Beyond the glitzy AI tools, there's a growing emphasis on the "picks and shovels" needed for healthcare data accessibility, including infrastructure, compliance, and interoperability. Executives believe that improving data flow will facilitate the effective use of AI. 3. Middlemen Under Scrutiny: Pharmacy benefits managers (PBMs) faced criticism for their role in rising drug costs, with discussions highlighting misaligned incentives and the need for transparency in healthcare pricing. 4. Women's Health Spotlight: Women's health initiatives gained attention, particularly following government funding announcements and personal stories shared by advocates. However, investment in women's health remains minimal compared to overall healthcare funding. 5. Interest in Psychedelic Medicine: The potential of psychedelics for treating mental health conditions garnered significant interest, with discussions about funding and research moving forward, especially within the Veterans Affairs system. Executive Takeaways: Leaders from various sectors emphasized the importance of consumer-focused strategies, collaboration, and the need for responsible AI usage. There was a consensus on the critical role of addressing healthcare accessibility, improving patient experiences, and fostering innovations that genuinely enhance care delivery.

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If you are involved in developing LLM applications, you may find this resource useful. It highlights key strategies for addressing the challenges in LLM application development. #LLMDevelopment #DataQuality #AIApplications 

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One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • athenahealth released Ambient Notes, a fully integrated AI ambient scribe for ambulatory practices, which also supports switching between AI models based on context and individual preference. • Abridge is partnering with OpenNotes to evaluate AI-generated visit summaries. 📞 AI Contact Center • healow health released Genie, a phone-, text-, and chat-based AI conversational chat solution for appointment scheduling, prescription refills, and more. 🏥 AI Facility Management • Kontakt.io released a security app that allows staff to page security personnel with location information, and a Kio Nurse app for locating items with a live map view. 📈 Data & Analytics • Alameda County selected Innovaccer to unify and analyze the data in its Social Health Information Exchange.    • Cornerstone AI announced an additional $5M investment, bringing its total funding to $10M. • emtelligent released their Clinical Workflows solution, which offers both clinical and business users across healthcare organizations an interface experience to interact with their existing LLM and NLP engine. 💼 Provider & Practice Administration • Axuall announced two new health system partnerships—VA-based Inova Health System and IN-based Community Health Network—and a partnership with PayrHealth, which helps providers negotiate contracts and reimbursements. 🔁 RCM • SmarterDx released an AI denials solution that creates AI-generated appeals letters.    • Inovalon hosted its Empower 2024 summit and announced a host of new solutions and updates, including an eligibility verification solution for provider organizations and several other tools for health plans and research organizations. • XiFin, Inc. added two new payers to its Payor Rate Transparency Monitor—Humana and Blue Shield of California—and launched Payor Intelligence Hub to help providers better understand payer policies and behaviors. ⚙️ Clinical Operations • Oracle hosted its Oracle Health Summit and announced a number of new features and product updates, including an AI-native EHR, patient admin tools, and plans to become a QHIN. • eClinicalWorks hosted its National Conference and released AI RCM tools, VBC scorecards, and patient engagement updates. • Altera Digital Health released a new EHR, Paragon Denali—natively deployed in Microsoft Azure —intended for rural, critical access, and community hospitals. 🤝 Staffing, Consults, and Referrals: • Tennr raised a $37M Series B. • Cottage Hospital partnered with ShiftMed to add more flexible per diem options for acute care staff. 🧑‍💻 Virtual & At-Home Care • Brook Health is partnering with Linus Health to provide treatment plans and ongoing remote clinical care for cognitive care patients. --- Want the latest healthcare tech news delivered to your inbox each week? Sign up here 📬 👉 https://lnkd.in/e2UJ3ckP

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There is no better place than Cambridge to scale a company like Qureight This city (that we call home) gives us access to everything we need to supercharge the growth of the business and compete on a global scale - from world-class talent to specialist investors and a dynamic life science ecosystem. Did you know that: 💸The Cambridge tech ecosystem has a combined value of €176bn, 18% of the entire UK’s tech ecosystem 🌡️ in the first half of this year, Cambridge tech companies have raised a record-breaking $1.6bn 🇬🇧Cambridge companies produce 16.9 times in value for every dollar of VC investment, while the UK and London produce 5.4 times and 4.8 times, respectively. It is a honour to be profiled in Silicon Republic, alongside some of the city’s top startups, in this list of Cambridge’s largest tech funding rounds of 2024 Congratulations to our Cambridge neighbours who also made the list Quantinuum Luminance Mission Therapeutics Sano Genetics PlaqueTec Ltd. Monumo Biofidelity Xampla https://lnkd.in/eaaG7THT

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As the discourse shifts from models to compound AI systems / agents, we need better AI benchmarks to evaluate multi-modal and multi-step task performance, especially in healthcare and life sciences. When we wrote the first paper demonstrating ChatGPT's performance on the USMLE, we chose the US Medical Licensing Exam as a benchmark for accessibility, speed, and ease. This benchmark was never intended to represent AI model performance on real-world clinical tasks. Today, I still see so many research teams and startups using benchmarks (like the USMLE) that are ill-fitted for assessing the true clinical or scientific performance and utility of the models they are developing for real-world contexts. Benchmark development may be seen as a "less sexy" area of research, but it is of paramount importance. Years after the rise of the transformer, we still lack adequate benchmarks for so many single-step tasks in biomedicine. With compound AI systems (i.e., architectures that integrate multiple AI models to perform complex tasks) emerging, we need new benchmarks for agentic behaviors. I'd argue that developing an agent with novel capabilities without at least proposing a companion benchmark (if an industry standard does not yet exist) may hinder the adoption of said agent, especially for high-stakes workflows. Designing more benchmarks that capture/simulate real-world clinical and scientific workflows will help us mitigate the major discrepancies observed between in silico and in vivo performance and better support safe + effective deployment of AI in biomedicine. There are already brilliant people focused here, and we need more. DMs are open if you're researching or working in this area of multi-step/multi-modal benchmarking in healthcare and life sciences! #healthcare #ai #artificialintelligence #generativeai

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DeepCura is just 30% away from becoming the github for healthcare, here are some upcoming world's first healthcare innovations.. Here's our latest iteration of how DeepCura organizes tens of notes from multiple providers in a single collapsible multiplayer thread, clinicians can expand all notes or individual notes, notes can be auto-tagged using AI, something cool they can do too is they can use old school control + F to navigate ALL total clinical information from the specific patient or also use AI to query, or generate documents from all patient data. The remaining 30%, we would just have to highlight past edits (removal of clinical data in red, and text additions in green highlighting the author), and that's it, we would be officially the github of healthcare. But our primary goal is August 5th... the world's first clickless EHR AI extension. My hypothesis is half EHR companies that are behind the AI race will LOVE this, and the other half will resent it, however for the latter, DeepCura will NOT invade EHRs without their permission, and clinicians will have to abide to our terms where they will respect their EHR terms and conditions while using our new tool. Another powerful thing about this innovative extension is that, since chrome extensions are quite vulnerable by nature, it will be fed from our web application, however all PHI will go through a process of anonymization first, so all the data used by our chrome extension AI Agent will use only anonymized clinical data giving a 0% risk when it comes to the security risks.

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Advancing healthcare through tech requires playing the long-game in order to access and navigate the needs of the myriad of stakeholders: physicians, patients, payers, regulatory, IT, work-flow infrastructure/operations and more. Excited to join this important panel discussing The Roadmap to Integrate AI into the Healthcare Systems. Congrats to #BentleyUniversity for leading the conversation.

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News Report Finds Racial Bias in Radiology Reports A recent Yale study found that when asked to summarize test results to be understood by nonprofessionals, the Language Model (LLM) produced results at lower reading levels if told the patient was black vs white. This news report has implications well beyond health, it impacts education and other areas. The findings suggest that if our data is biased, then AI's will be biased. Check out the full report here: https://lnkd.in/edSN_iTa #health #AI #education #racialbias #diversityandinclusion

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👉🏼 Evaluation of a Large Language Model on the American Academy of Pediatrics' PREP Emergency Medicine Question Bank 🤓 Sriram Ramgopal 👇🏻 https://lnkd.in/ezHKQ6UX 🔍 Focus on data insights: - 📊 ChatGPT achieved a correct answer rate of 74.5% across 215 questions, indicating effectiveness in PEM knowledge assessment. - 🔄 The model demonstrated consistent performance across three years, suggesting reliability in its responses over time. - 📚 Topic-specific performance revealed strengths in research and medical specialties, while highlighting weaknesses in procedures and toxicology. 💡 Main outcomes and implications: - 🏆 ChatGPT exceeded the minimum passing threshold of 65%, supporting potential integration into medical education curricula. - 💬 Detailed explanations provided by the model can enhance understanding among learners, fostering a deeper grasp of pediatric emergency medicine concepts. - ⚠️ Identified limitations in specific topics may guide future enhancements of LLMs for more comprehensive educational tools. 📚 Field significance: - 🧠 The application of LLMs like ChatGPT could revolutionize medical education, offering dynamic learning resources tailored to various specialties. - 🌍 The findings suggest broader implications for using AI in clinical settings, potentially improving diagnostic capabilities and patient care. - 🔍 Continuous evaluation of LLM performance is essential to ensure quality and adaptivity in rapidly evolving medical landscapes. 🗄️: [#LargeLanguageModels #PediatricEmergencyMedicine #AIinHealthcare #MedicalEducation #DataInsights]

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I’ve spoken with 50+ CMIOs / CIOs over the past year on how they’ve piloted new Health Tech for clinicians (e.g. AI scribes, generative AI drafts for patient portal messages, etc.). There’s no one right answer, but there are 5 main approaches I’ve heard for choosing clinicians to pilot with: 𝟭. 𝗠𝗼𝘀𝘁 𝘁𝗲𝗰𝗵 𝘀𝗮𝘃𝘃𝘆 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 You want to move fast so you look for clinicians who raise their hand and are eager to try the solution. The risk? Tech savvy clinicians may be biased in favour of new tech. So they may not be highly representative of the population. 𝟮. 𝗟𝗲𝗮𝘀𝘁 𝘁𝗲𝗰𝗵 𝘀𝗮𝘃𝘃𝘆 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 Or you do the opposite. If the tech is so intuitive, so easy to use that even the least tech savvy folks can and will use it… then you can be confident it can scale enterprise-wide. The risk? Maybe you don’t actually care if the least tech savvy folks can use it, because even 50% adoption is good enough to have a meaningful ROI. E.g. data I’ve seen shows clinician adoption of AI scribes can range 40% to 80% at health systems. But if you piloted with the least likely users (who don’t end up using it)… you would call the pilot a failure and miss out on the up to 80% who would’ve used it. 𝟯. 𝗖𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗽𝗮𝗶𝗻 𝗽𝗼𝗶𝗻𝘁𝘀 If you’re looking to demonstrate the biggest ROI, you could pilot with the clinicians who would benefit the most. For Generative AI drafts for patient portal messages, this is often primary care physicians as they spend more time in their inbox than other specialties. For AI Scribes you could check EHR data to see who is spending the most time on clinical documentation, who is doing the most pajama time in the EHR, etc. The risk? You might end up investing in a solution that works for one clinical group but not for others. 𝟰. 𝗟𝗮𝗿𝗴𝗲𝘀𝘁 𝗴𝗿𝗼𝘂𝗽 𝗼𝗳 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 More data = shorter time to determining value. So it would make sense to pilot with largest specialty with the most clinicians. The risk? Again you would be assuming a solution or approach that works in one specialty is scalable to all… it may not be. 𝟱. 𝗦𝗼𝗺𝗲 𝘂𝗻𝗶𝗾𝘂𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗮𝗯𝗼𝘂𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘂𝘀𝗲𝗿𝘀 For example some health systems have chosen AI Scribe pilot groups by asking medical office assistants to identify physicians who care the least about how a note is written and thus are more willing to delegate to an AI scribe. The risk? It’s super custom to the situation and it may be hard to replicate this approach with various new tech. 𝙒𝙝𝙖𝙩 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝 𝙬𝙤𝙪𝙡𝙙 𝙮𝙤𝙪𝙧 𝙝𝙚𝙖𝙡𝙩𝙝 𝙨𝙮𝙨𝙩𝙚𝙢 𝙩𝙖𝙠𝙚? #digitalhealth #healthcareinnovation

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • Suki is partnering with Zoom to generate clinical notes as well as other integrated AI features.    • Net Health announced it would integrate AI ambient scribe Tali AI. • Aidoc is partnering with NVIDIA to create a guideline to accelerate AI adoption, the Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE). 📞 AI Contact Center • Authenticx released a new AI assistant that helps users uncover business insights from their data. • Infinitus Systems, Inc. closed a $51.5M Series C with participation from Memorial Hermann Health System and others.   🏥 AI Facility Management • Cognosos, Inc. acquired Cox Prosight. 🌡️ AI Remote Patient Monitoring and Triage • Biofourmis has merged with CopilotIQ. 📈 Data & Analytics • Avandra Imaging launched out of stealth; it plans to sell to biopharma, medical research, and AI companies. • Gradient Health acquired DataAppraisal. • HealthEx launched and announced $14M in Seed and Series A funding. 📋 Patient Administration • Notable released Flow Builder, which allows users to develop their own AI agents for operational workflows within EHRs and other systems. • MIla Health Inc released its flagship AI solution of the same name, and announced implementations at provider organizations including Aayu Clinic, MIMIT Health, and Best Practices Inpatient Care. 🔁 RCM • Oracle’s patient accounting solution has been integrated with FinThrive, which enables improved functionality, performance, and a unified login experience. • Blue Shield of California and Salesforce are partnering on a solution that promises to give patients prior auth responses within the course of their appointment. ⚙️ Clinical Operations • Dyania Health closed a $10M Series A, including participation from Cleveland Clinic Ventures. 📱 Patient Support & Communications • Psych Hub released a new solution designed to support patients seeking mental health support, directing them to both non-clinical and clinical resources, thereby assisting behavioral health providers to reach new patients. • Mayo Clinic has partnered with Lin Health to support patients after treatment for fibromyalgia.   🍎 VBC • WellSky acquired Bonafide Health, allowing it to provide a more integrated platform for providers to run their businesses. 🧠 Food For Thought • 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗮𝗹𝗹 𝘁𝗵𝗲 𝗔𝗜 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀? Andrew Hines, CTO at Canvas Medical, shared a breakdown (plus some hot takes) on all of the many entities currently vying for market share in the AI governance space https://lnkd.in/eMNpXw4t --- Want the latest healthcare tech news delivered to your inbox each week? Sign up here 📬 👉 https://lnkd.in/e2UJ3ckP

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Exciting news from HLTH USA – I’m thrilled to share that Aidoc, in collaboration with NVIDIA, is developing the BRIDGE Guideline, a new framework that I believe will help shape the future of AI in healthcare. BRIDGE is not just another guideline; it’s a strategic approach to overcoming the most critical barriers, scalability and integration, that have slowed AI adoption across our industry. At its core, BRIDGE is designed to support healthcare leaders and innovators as they navigate the complex journey of developing and integrating AI into clinical workflows. Partnering with NVIDIA on this has been instrumental in leveraging their deep expertise in computing platforms, which will help bring a higher level of innovation to healthcare systems. As we look ahead, I believe BRIDGE will play a pivotal role in helping healthcare systems realize AI’s transformative potential, empowering them to scale AI faster, with greater confidence, and deliver more efficient, higher-quality care to patients. Exciting times ahead! https://okt.to/D20fZU

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👉🏼 Chat-ePRO: Development and pilot study of an electronic patient-reported outcomes system based on ChatGPT 🤓 Zikang Chen 👇🏻 https://lnkd.in/ejE_73_2 🔍 Focus on data insights: - Chat-ePRO correctly generates conversation based on PRO forms with a success rate of 95.7%. - The system accurately extracts PRO data instantaneously from conversations with a Macro-F1 score of 0.95. - Subjective evaluations from doctors (>70%) indicate that Chat-ePRO comprehends questions and consistently generates responses. 💡 Main outcomes and implications: - Chat-ePRO demonstrates a higher response rate (90%) and longer interaction time (10.86 s/turn) compared to other methods in the pilot study. - Feasibility of utilizing prompt engineering algorithms to drive Large Language Models (LLMs) for ePRO data collection tasks is validated. - The Chat-ePRO system effectively enhances patient compliance. 📚 Field significance: - Development of Chat-ePRO showcases the potential of LLMs in improving user compliance in electronic Patient-Reported Outcome (ePRO) systems. - The study highlights the importance of leveraging advanced algorithms like prompt engineering for enhancing data collection processes in healthcare settings. 🗄️: [#Chat-ePRO, #patient-reported outcomes, #data insights, #healthcare technology]

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