Warris B.

The focus at Omada Health is keeping humans at the center of care. We see our members mention this special relationship repeatedly. The latest from Omada proves just that, as the company announced a pledge to Healthcare AI Commitments underscoring its goals of using AI to empower human-led care teams to drive behavior change at scale. This commitment means plans to continue pursuing cutting-edge technologies that enable more personalized and equitable care for members. With billions of actionable health data points to draw on Omada is utilizing AI and machine learning to help scale member/care team interactions. Learn more about this commitment in the latest release: https://lnkd.in/gSCiDgg2

17

M13 is thrilled to lead the Series A in RadiantGraph which is focused on personalization in healthcare. As we continue to invest in early stage companies it is abundantly clear that the biggest indicator of success is the founder and his or her ability to execute on their vision of the future. In this case, I don't think I have seen much better founder-market fit than we have with founder and CEO Anmol Madan. We believe in his vision: in order to have the best patient outcomes in healthcare we need increased personalization and AI is the breakthrough to get us there. Anmol started one of the pioneering digital health companies Ginger and then was Chief Data Scientist at Livongo and Teledoc. He has done it all: early stage, late stage, public company. And he deeply understands the nuances of the healthcare market from all angles which is critical for success. While there are many players building healthcare-specific AI capabilities, RadiantGraph is focused on outcomes: The platform is already processing personalization models for more than 3.5 million people, despite launching just last year. Excited to partner with my friend Adam D'Augelli and the great True Ventures again and Morgan Blumberg who has been deep in the healthcare market with me for many years. More on our thesis in the blog below. https://lnkd.in/evDDmC3W

72

4 Comments

Thank you for sharing this insightful research, Dario Heymann, PhD. The increasing role of Big Tech in Digital Health is undeniable and, as your analysis highlights, their resources and technological capabilities are instrumental in driving innovation forward. The impressive growth rate of partnerships, particularly with a CAGR of 32%, underscores the strategic importance of these collaborations. In my experience, the synergy between Big Tech and Digital Health startups fosters a dynamic environment where advanced technologies like AI, machine learning, and blockchain can be seamlessly integrated into healthcare solutions. This not only enhances healthcare delivery but also aligns with patient-centric values and stringent regulatory requirements. Moreover, the shift of Big Tech overtaking Biopharma in partnership share is a clear indicator of the evolving landscape. It’s essential for startups, academic institutions, and healthcare organizations to leverage these partnerships to stay at the forefront of innovation. #eHealth #HealthTech #DigitalHealth

7

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

65

4 Comments

One of the most important concepts we introduced in our recent Healthcare AI roadmap is the notion of “Modality—Business Model—Market fit.” This concept describes how the delivery method and business model of AI in healthcare directly shape its potential to create value. There are many possible combinations of modalities and business models in healthcare. AI can be delivered as software, copilots, agents, services, diagnostics, or therapeutics, supported by business models ranging from usage-based and volumetric pricing to performance-based and shared savings arrangements. Nailing selection of modality and business model is essential for AI companies seeking large market opportunities and profitable operations. As we describe in an example with computer vision-based diabetic retinopathy screening, modality and business model selection can drive a difference in TAM by upwards of 25x, and has serious implications for overall margin structures. Read more here 👉 https://lnkd.in/eK7rjpJz

139

10 Comments

To follow up on Dede Ainbinder recent post: If we use the current EHR data to build a AI clinical decision support tool it is unlikely to capture our clinical decision making process. EHR's are inherently designed to maximize billing and minimize risk of litigation. A clinical decision support tool based on this data will likely do the same. There is a large sunken cost fallacy here where we continue to hang on to garbage data. Just because the data gathering and storage process was capital and time intensive doesnt mean the data is valuable. To truly capture the clinical decision process we need to capture data which sits outside billing and litigation. Its hard to imagine a future where this data isnt used for billing and cannot be called upon during litigation. #EHR #healthdata #clinicaldecision

17

8 Comments

To “cross the chasm” from Early Adopters to Early Majority in the Health Tech adoption lifecycle, startups must often survive long enough to benefit from unpredictable events (e.g. new reimbursement, public health crisis). Don’t believe me? Let’s look at 4 major examples: 1️⃣ EHRs Epic, Cerner and MEDITECH were all founded before 1980, but just 30 years later in 2008, only ~10% of hospitals had adopted EHRs. It wasn’t until the HITECH Act was passed in 2009 to create financial incentives for adopting EHRs that the category “crossed the chasm”. In 2019, just 10 years later, hospital adoption of EHRs skyrocketed to 80-90%. 2️⃣ Telemedicine Telemedicine was invented in 1959 at the University of Nebraska and billing codes for telemedicine first became available in the 1990s. After decades of slow, steady growth, still in 2019 only 15% of providers were using telemedicine. Just one year later, due to the pandemic, telemedicine adoption soared to 90%. 3️⃣ Digital care journeys This one is very personal to me and SeamlessMD for digitizing patient care journeys with automated reminders, education and symptom monitoring (e.g. pre/post surgery, oncology, pregnancy, chronic care, etc.). We started in 2012 and for many years we were doing most of our business in the U.S. and very little work in Ontario. Even though we had developed tons of clinical evidence, there were just no financial or quality incentives for hospitals to adopt our solution in Ontario for a long time. In 2019, after seven years, still less than 10% of 300+ bed hospitals in Ontario had adopted SeamlessMD. Then in 2020, the pandemic changed everything, as the government started providing funding to hospitals to adopt tech for digital care and remote monitoring to safely discharge patients sooner and cut the surgery backlog. By 2023, almost 70% of 300+ bed hospitals in the province were using SeamlessMD. 4️⃣ AI medical scribes Many folks might think AI medical scribes came into being after ChatGPT was announced to the world in 2022. But actually today’s market leaders like Nuance DAX, Abridge, DeepScribe, Nabla, Suki, Heidi Health, etc. were all founded between 2017-2020. Yet adoption of AI medical scribes only started taking off in 2023… AFTER the ChatGPT moment brought Generative AI into global awareness. In all of these examples, you find that proven benefits and clinical evidence often isn’t enough to drive mass adoption. The friction created by the status quo and misaligned incentives make it a very steep hill to climb. You need a public health crisis, government incentives or some other unpredictable event to “cross the chasm”. I’m not saying Health Tech startups should depend on an unpredictable black swan event to be successful. But what I am saying is that sometimes the best thing you can do to win is just be resilient and survive. Because you never know when your own ChatGPT moment might just be around the corner. #digitalhealth #healthcareinnovation

91

15 Comments

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

53

21 Comments

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

38

4 Comments

2025 will be a referendum year for Digital Health startups - as true “Substance” separates itself from the overfunded “Hype” of 2021/2022 👇 The last few years were both Good and Bad for Digital Health. The Good was that interest in Digital Health accelerated and health systems tried lots of new things. The Bad was that the bar for funding was often low… sometimes SO low that money was wasted - either on health systems paying for stuff that didn’t work or on startups “over-eating” investor funding. But by 2024, the healthcare market started to wake up to the idea that you just can’t pay for or fund cool ideas. You couldn’t just buy-in to Hype. Unfortunately that meant Digital Health startups that viewed pilots with brand name logos and millions in funding from famous investors as “success” started to pay the price. It also meant Digital Health deployments funded by grants, which didn’t solve real problems, started dying off too. Hype is all fun and games… until the reckoning comes. And now that the reckoning is coming, all that matters are Digital Health solutions with real Substance: → Deployments that actually go-live on time → Solutions that actually fit the clinical workflow → Innovations that actually have proven integration partnerships with EHRs → Solutions that deliver proven ROI via improved clinical outcomes and financial performance So yes 2025 will be the year Digital Health swallows some tough medicine. But we will be better off for it. Not only because the money isn’t there anymore to fund the Hype… … but because our patients deserve Substance. Caveat: I might be totally wrong since the AI hype emerged in 2024 - but I trust my CMIO and CIO friends to tame that beast 😁

44

13 Comments

In case you missed it, last week the Coalition for Health AI (CHAI) released a draft consensus framework for responsible health AI. Developed with input from over 100 contributors representing a diverse network of healthcare stakeholders, this guide proposes actionable evaluation criteria throughout the AI lifecycle—from identifying use cases to deployment and monitoring. Key examples outlined in the guide include: • Predictive EHR Risk Use Case (Pediatric Asthma Exacerbation) • Imaging Diagnostic Use Case (Mammography) • Generative AI Use Case (EHR Query and Extraction) • Claims-Based Outpatient Use Case (Care Management) • Clinical Ops & Administration Use Case (Prior Authorization with Medical Coding) • Genomics Use Case (Precision Oncology with Genomic Markers) The draft framework is open for public review and comment for the next sixty days. Please use this form to submit feedback: https://lnkd.in/eYughFYC Kudos to the CHAI team led by CEO & President, Dr. Brian Anderson, MD, on this milestone. Eric Horvitz Jennifer Goldsack John Halamka, M.D., M.S. Michael Pencina Micky Tripathi Nigam Shah Suchi Saria Troy Tazbaz #healthcare #ai #artificialintelligence #generativeai https://lnkd.in/e7VRv6fD

125

1 Comment

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

100

18 Comments

Healthcare tends to lag behind in tech adoption, but that might be its biggest strength as AI transforms our industry. Julie Yoo, General Partner at a16z, recently made a compelling point: Unlike other industries, healthcare isn’t weighed down by billions of dollars of investment in legacy software systems. This lack of "sunk-cost bias" means we have a unique opportunity to leapfrog directly into the benefits of AI without the burden of ripping out decade-old tools. Our industry is slow to adapt not because of a lack of awareness, but because the mindset is deeply rooted in established ways of working. The key to advancement lies in shifting this mindset. Luckily, this opportunity couldn’t come at a better time. Healthcare is facing a critical workforce shortage, with hundreds of thousands of doctors and nurses short of what’s needed to meet growing demand. AI can help fill this gap by scaling clinical expertise, making certain that the best practices are shared widely and that every patient receives the same high standard of care, regardless of location. However, none of this matters if we don’t build trust in the AI tools being developed. Healthcare already has strong regulatory frameworks, with the FDA approving hundreds of AI-driven clinical products. But trust is built slowly, and the human element must remain central to any transformation. At PROXDIS, we’re focused on how AI can drive positive change, without sacrificing the relationships and trust that are the core of patient care. Technology has incredible potential for improving care, but it can never replace the relationships and trust that are at the heart of patient care. As Julie pointed out, healthcare can use this opportunity to jump ahead—but only if we guarantee that this innovation is always centered around the real needs of providers and patients alike. Are we ready to adapt to AI at a pace that allows for meaningful change while keeping the human element front and center? Let me know your thoughts.

14

Just launched my Medium blog where I explore the intersection of personal stories, healthcare innovation and real-world clinical practice – sharing insights from both the frontlines and the latest research that's shaping our field. Follow my journey on Medium [https://obmd.medium.com/ ] and join a community of forward-thinking healthcare professionals as we dive deep into evidence-based medicine, emerging technologies, personal observations, and the future of patient care. https://obmd.medium.com/

6

2 Comments

Still buzzing from the incredible #TEDAI2024 panel on AI and Healthcare! The video of our session is finally out. Click below to watch. It was an absolute honor to share the stage with such forward-thinking leaders: Fawad Butt Monica Chmielewski Aaliya Yaqub, MD and our inspiring moderator Missy Krasner A huge shout-out to TEDAI San Francisco for putting together this fantastic event! During the discussion, we talked about how Generative AI is reshaping patient care—from early detection and personalized treatment, to making our healthcare systems more proactive and patient-focused. I’m humbled by the enthusiasm and expertise everyone brought to the table. As Founder & CEO of HOPPR, I’m excited about what’s coming next. We’re committed to turning cutting-edge AI into real-world solutions that help providers, patients, and communities thrive. Thank you to everyone who attended, asked questions, and sparked some truly dynamic conversations. Here is an article we wrote summarizing all the topics we discussed on the panel: https://lnkd.in/gEwQ3_7A If you’re as passionate as I am about the future of healthcare innovation, let me know your thoughts below. Please share this post and keep the momentum going—your comments, ideas, and personal experiences can help all of us move forward in shaping the next generation of healthcare. #GenAI #HealthcareInnovation #AIFuture #AIandHealthcare #TEDAI2024

25

4 Comments

12+ years working on Health Tech with Health Systems taught me you need 3 types of Champions to be successful 👇 We often talk about the importance of a champion for new healthcare innovations, but given the complexity of healthcare, I’ve found our most successful health system partnerships have MULTIPLE champions… 3 key ones in particular: 𝟭. 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗰𝗵𝗮𝗺𝗽𝗶𝗼𝗻𝘀 Most people know this one. It’s not enough that the solution is paid for and the operational staff are willing to support it. If there isn’t a clinical champion willing to get all the other clinicians on board… the tech will end up gathering dust on the shelf. It’s great if the champion is the Chief of Medicine or Chief of Surgery, but you don’t need the most senior clinicians. More important is a respected clinical leader who is willing to roll up their sleeves and ensure their teammates come to the table. 𝟮. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗰𝗵𝗮𝗺𝗽𝗶𝗼𝗻𝘀 Ultimately someone has to pay for it, or have the ear of the executive who will pay for it. When a CXO or VP is sponsoring the innovation, it gets more resources, more mindshare and more priority as an initiative everyone should care about. Without an executive champion, you can end up with a successful pilot that then simply dies because of the lack of money and resources made available to sustain it. 𝟯. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗵𝗮𝗺𝗽𝗶𝗼𝗻𝘀 Even if the tech is paid for and the clinical champions believe in it… ultimately you need operational leaders to ensure the innovation keeps running. Whose team ensures every patient and care team member is aware and engaged with the tech. This might be the director of a service line or project management leader. This champion has to be motivated to keep the trains of the initiative running on time. Otherwise you end up with tech that everyone says they want… but doesn’t end up being used. That doesn’t stay clicking in the clinical workflow. Because your clinical and executive champions won’t be keeping an eye on the Tech at this level of granularity… someone operational needs to. So if your Health Tech initiative is struggling… it might be worth reflecting on whether you’re missing one or more of these champions.

63

8 Comments

Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Products • Emergency Services, Inc. + Augmedix Go ED: The emergency medicine group adopted the new ED-specific AI ambient scribe solution.    • Color + OpenAI: The cancer care tech company partnered with the GenAI giant to build a AI clinical decision support copilot.    • AKASA Medical Coding: The AI RCM vendor known for its AI prior auth tool AKASA Authorization Advisor released a new GenAI medical coding solution.    • Humata Health: The AI prior auth for providers vendor closed a $25M funding round led by healthcare org-backed funds The Blue Venture Fund and LRVHealth.    • Wellsheet + Concord Hospital Health System: The AI-enabled Smart EHR UI solution announced a client-reported 16.3% reduction in length of stay and 40% decrease in time spent in the EHR via its partnership with Concord Health, as well as the release of its new LLM-generated handoff summary feature. 🍎 VBC • BSIM Healthcare Services + Innovaccer: The healthcare provider—embarking on its first VBC contract—adopted the pop health data platform for analytics. 🩼 Durable Medical Equipment • Parachute Health: The DME e-prescribing and ordering solution released an update that integrates prior authorization. ⚕️Clinical Staffing • Matchwell + Indiana Hospital Association: The clinical staffing service partnered with the hospital member org to create the first state-wide resource pool for temp staffing. 🧑‍💻 Virtual and At-Home Care • Deaconess Health System + KeyCare: The Indiana-based health system adopted the Epic-based virtual care provider for around-the-clock virtual urgent care.    • UC Davis Health + Current Health: The health system launched a new RPM program for high blood pressure in partnership with Best Buy Health’s care-at-home platform. 🧠 Food for Thought: We came across a couple of good resources this past week on the current state of healthcare data information exchange: • This article in Health Affairs from Deven McGraw and Tina Grande provides a comprehensive overview of some of the patient health data abuses we've seen come to light in recent months, as well as a number of policy recommendations to improve the health information exchange landscape for all participants. https://lnkd.in/e_GEQ-JJ  (h/t Alya Sulaiman)    • This episode of Health Gorilla's InteropTalk gives a helpful update on the current state of affairs, with a particular focus on the state of trust across the health data exchange community and how the definition of "Treatment" may evolve. https://lnkd.in/e96AJG-5 (h/t Brendan Keeler) --- Reminder: you can sign up for the Elion Briefing to get the latest healthcare tech news delivered to your inbox each week 📬 👉 https://lnkd.in/e2UJ3ckP

36

2 Comments

https://tcrn.ch/3xszfxx Interesting piece in TechCrunch about what #generative AI could mean for healthcare. Some key points: ⭐ The article described the potential of using generative AI in #radiology. ⭐ Worth noting that both examples given in the radiology section aren't actually #generativeAI though (both examples are machine learning). ⭐ Despite enthusiasm from the investor community, patients and clinicians are less sure about its value ⭐ Only 53% of surveyed consumers thought it could add value in healthcare ⭐ A paper in JAMA Pediatrics found ChatGPT made errors diagnosing pediatric diseases 83% of the time Despite reservations, I think there's no doubt that generative AI could be game-changer, especially for routine, mundane tasks. Be interesting to hear what others think ! #radiology #AI

17

8 Comments

⚕️ Why Do So Many Healthcare Innovators Miss the Mark? ⚕️ Last week, the sudden shutdown of Forward—a $650M health tech unicorn—made waves. Forward promised to revolutionize care with AI-powered, clinician-lite CarePods, but as Jay Parkinson put it, it became “a $400M mistake in understanding the demand.” This isn’t just about Forward. It’s about a pattern we see time and again in healthcare: well-funded ideas with merit on paper that fail in practice. Why? Because healthcare isn’t practiced in a vacuum. It’s unpredictable, personal, and often counterintuitive. In this week's The Surgeon's Record presented by Commons Clinic, I explore: 👉 Why some of the most hyped health tech solutions fail to gain traction 👉 What healthcare leaders and innovators can learn from my patient who walked into clinic with a broken hip 👉 How my NFL fandom offers a surprising analogy for the disconnect between watching and doing in healthcare I also suggest a simple but powerful solution: If you’re building in healthcare, spend time on the frontlines. See the challenges and humanity of care delivery up close. That’s where the real solutions are born. #medicine #healthcare #healthtech #health #healthcareinnovation

19

2 Comments