Morgan Cheatham

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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A whirlwind of activity last week at the America's Physician Groups conference. Lots of solid insights shared by health system leaders. Some notable thoughts: +++ Artificial intelligence is intelligent in different ways: By now, we all know ai has a diverse set of use cases, incorporated in different applications and tools. But there are fundamental differences in how the intelligence itself is brought to life. Examples include: - Predictive AI, used to forecast future outcomes by leveraging historical data. Shout out to UCLA’s CKD/ESRD early intervention protocol (Joycee Berin) and Diagnostic Robotics’ intelligence care post-dicharge journeys (Kira Radinsky) - Protocolized AI, used to automating a user’s administrative load by using a set of detailed, pre-defined rule sets. Thanks to ARC for sharing their experience using Elaborate 's automated outpatient lab summaries (Manish Naik) - Generative AI, used to create new content. Great overview of ambient documentation at MGB as well as Cedars’ Sinai talking through its medical ontology model powering acute triage (Caroline Goldzweig) Across system leaders and experts, there was clear agreement that the first step to implementing intelligence intelligently is defining the ‘jobs to be done’. That way, internal and external stakeholders are clear on what they're solving for when they architect the solution.  +++ Scaling AI takes time and rigorous observation: Market leaders describe a phased pilot approach that allows clinicians to build confidence and system leaders to measure ROI before deploying systemwide. Loved hearing Rebecca Mishuris outline MGB’s novel approach of piloting two solutions against one another, to pressure test implementation, workflow, and value creation. +++ Proving ROI continues to be challenging: Lots of vendors using lots of different metrics to prove value, but the majority of leaders agreed that estimating the ROI of these new tools today is part science, part art. Demonstrating the ROI of the “soft” metrics around provider burnout and retention seemed to be a big opportunity, especially for investment in more efficient primary care and PCP wellbeing. After all, PCPs drive referrals to specialty-level care (and higher reimbursements) for health systems. Appreciated Anurang Revri's overview of how working team setup can also impact ROI estimates. +++ Value-based care = tools + humanity: VBC will require not only implementing tools to support clinical documentation, risk capture, upcoding, etc, but also humanizing interactions to engage patients. Countless examples, but one that made me smile was a depression focused chat bot responding in the following interaction: - Context: post-discharge check in Bot: Hi Freida, how are you feeling today after your surgery?  Patient: I’m having a tough time, am in a lot of pain.  Bot: Well, it can’t be that bad, I’m sure it will get better with a positive mindset!    🤣 🤣 🤣 Can't wait for next go-around!

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Q1 2024 was big for Digital Health, with $1.1B in VC funding. A large portion came from major deals like $126M for Transcarent and $108.8M for Rightway. Despite some challenges in digital therapeutics and telehealth, investments in general care ($213.4M) and health benefits navigation ($140.7M) are promising. Read what’s driving the shift from direct-to-consumer to enterprise telehealth models here: https://ow.ly/uNYs50SpXGh

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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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The defining factor isn't the size of the company or startup, but the vision behind it. Take OpenAI, for instance. Until last year, unless you were deeply embedded in the ML world as a computer scientist/researcher, or enthusiast, you might not have heard of OpenAI. Meanwhile, a giant like Google was dominating the scene. However, a few years back, OpenAI was just another small startup. Now, it's rapidly evolving into a major player in big tech. Why and how? It comes down to having a long-term vision, a clear roadmap, patience, and solid execution (i.e., talents). Even in the context of healthcare AI, where (small) startups face many challenges (including the ones mentioned), the principles of vision and strategy apply. While merging with big tech might seem like an option for some, if a startup truly believes in its vision and has a well-defined path to achieve it, it should forge ahead independently. The landscape is evolving, and like OpenAI, today’s small startups with clear, visionary goals could be tomorrow's industry leaders. #starup #founders #openai #google #healthcare

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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

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I always read Rock Health's digital health reports as they give a nice pulse feel for what is happening in the digital health funding space. Good to see funds raised in H1 2024 suggest funding in the sector seems to have stabilized at 2023 levels. Its no surprise the report emphasis the buzz around AI and healthcare. There have been clear winners in the AI healthcare space in the area of process and cost optimization and the R&D space. The impact of AI in care and treatment delivery is so far much more muted. One area I never though to look at AI was in its ability to bring together multiple healthcare pathways and digital point solutions into a more holistic seamless experience for the patient. There is a clear trend away from using stand alone digital health point solutions, and could AI address this challenge by facilitating the efficient integration of disparate processes and technology into unified care programs. I can see this happening with digital health products but this will be more tricky to do with physical care programs involving clinicians and HCPs. “AI is helping us combine multiple parts of the care journey, across all care experiences. In the future, benefits navigation, clinical guidance, and care delivery won’t be delivered by separate companies or point solutions within the healthcare ecosystem. They’ll be features that work in concert within products, and they’ll happen in under 90 seconds for a seamless patient experience.” – Glen Tullman, CEO, Transcarent and Managing Partner, 7wireVentures

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Blue Health Intelligence, a data analytics and software company, undertook an evaluation of almost 170,000 unique GLP-1 users for weight loss, notes less than half of those prescribed stay on the medication for 12 weeks or more. Younger adults were less likely to continue the drug, whereas those who were prescribed the medication by an endocrinologist or obesity medicine specialist were more likely to continue for longer. Indeed, this data states 30% of patients dropped out of treatment after the first four weeks, and 58% at 12 weeks. Those who dropped out are unlikely to achieve clinically meaningful weight loss.  The likelihood of continuance was further exacerbated for those with health inequities or who lived in underserved health regions. The data comes from Blue Cross Blue Shield Association members participating in Plans that provided coverage of these products, from 2014 through to end of 2023. Razia Hashmi, vice president for clinical affairs at Blue Cross Blue Shield wants to ‘��paint a clearer picture of what makes somebody successful.’ The missing piece here is why the patients stopped – side effects, failure to achieve initial weight loss, stigma and discrimination, or coverage issues related to cost and access. My longtime friend, colleague and bariatric surgeon at Stanford University, Dr. Dan Azagury rightly states ‘…The goal is for patients to stay on this for a significant amount of time. Most likely… beyond 12 weeks.’ https://lnkd.in/ediQskQx

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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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LSI, BioscienceLA, #LAMedtechweek, and HLTH's #VIVE2024 event resulted in the latest GTM Pulse Check offering which has successfully taken off! As a result, I am continuing on last week's series of go-to-market tactics. Crafting a robust marketing plan is essential for medical device companies to thrive in today's world. By leveraging these outlined sections, organizations can optimize their marketing strategies and drive meaningful impact.This post takes a step back to outline the sections of a marketing plan. Read on 👇🏼 Looking for support in creating your marketing plan?  Let’s chat! Book here 👉🏼 https://lnkd.in/g92YA9WK _____ P.S. Enjoying my content? ⚕️Follow me for more tips on medical device commercialization and repost to share with your network. #medtech #gotomarket #medicaldevices #healthcareinnovation

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Investing in companies that fill needs for health systems is a growth opportunity for both delivery organizations and start ups. Some systems are jumping in on their own, while others are partnering with like minded systems or with established VC firms. While this is a relatively new trend for systems, health plans have been making similar bets for many years. The benefits of becoming a health system portfolio company can be significant. Data, access to beta populations and real world coaching can be extremely beneficial. Start ups get mentoring and systems have to ability to leverage outside resources to solve relevant business problems. I predict a booming business!

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AI is changing the game in healthcare. A quarter of VC investments in healthcare tech are projected to be AI-related solutions; plus, the US AI healthcare market is projected to receive $11.1 billion in 2024, up from $7.2 billion in 2023. AI isn't just a trend; it's transforming efficiency, boosting patient outcomes, and aiding drug discovery. The stats back it up. 84% of global healthcare leaders believe AI-driven automation can save clinicians' time by reducing administrative burdens, while 88% think it can help alleviate workforce shortages. The convergence of AI and healthcare is not just a promising development; it is essential for the future of value-based care. The potential impact on patient journeys, cost reduction, and overall healthcare delivery could be profound. #HealthcareInnovation #AI #DigitalHealth

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Interesting to see that digital health funding is relatively stable. Only a couple of exits. I think we'll see more deals like DarioHealth's acquisition of Twill, since from a sales perspective, the narrower the solution, the more difficult it is to acquire customers. This difficulty has nothing to do with the quality of offerings, but with the data management and administrative complexity on the customer side.

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We just released our H1 2024 Digital Health funding summary. https://lnkd.in/gAZY64tp H1 2024 Digital Health funding closed at $12.4 billion invested across 719 deals. - Q2 2024 is outperforming Q1 by 34% ($7.1 billion vs. $5.3 billion)! - Digital Health funding has shown sustained investor confidence. Funding in Digital Health in North America is up 14% to $9.5 billion. Investment in Europe is down 10% to $1.52 billion, while funding in APAC dropped by 7% to $1.17 billion. - Partnerships in Digital Health are flat in the first half at 1,730. NVIDIA has been the strongest corporate partner in H1 2024 - 96 M&As, 8 bankruptcies, 5 delistings from the stock market and 3 relistings from NASDAQ to OTC market. The full report will be available for free next week. Feel free to comment and share if you want to receive a copy prior to our official launch 😊 Galen Growth | Insights You Can Trust #funding #digitalhealth #investments #partnerships

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Should you sell your practice to a VC-Backed MSO Startup? tldr: - new VC-backed startups are forming MSOs to roll up independent practices - The pitch is physicians receive early liquidity from the roll up and are granted ESOP shares that can be used for retirement - Problem is the use of ESOPs with independent practices is not a new concept and has been used with large group practices since the 70's - ESOPs do not grant full ownership and in order for the shares to be valuable for retirement, the practice needs to run the same if not better post roll up - With VC-backed startups being primarily operated by non-physicians with zero clinical or medical practice experience, the likelihood of this happening is extremely low making the entire concept high risk However, there is a viable path for continued ownership + liquidity for independent practices. Check out the article to learn more. ⤵ https://lnkd.in/gmGvTYwP

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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

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The future of medicine must incorporate AI. It's imperative to break the bottleneck in healthcare which is causing not only poorer health outcomes in the U.S. but also drastically decreasing provider satisfaction with their jobs, which is now at an all-time low. . With Nihar Ganju, MD CEO of RealDocAI, we turned my 10 years of clinical experience and expertise into an AI-Heather chat feature - something inspired by the thousands of questions that I have yielded over my years of being on social media, seeing patients, and teaching clinicians. As Dr. Ganju says, just dumping a text book into an LLM won't work, its imperative that an AI model is based on an expert that women already know, like, and trust. . This application extends far beyond menopause - essentially any specialty that is primarily dialogue-based (AKA chronic care management). Watch us as we launch the AI-Heather clinician who can consult with patients and begin to make treatment recommendations. Tell me in the comments- how could this drastically change healthcare?

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AI and data go hand in hand, wouldn't you agree? On the podcast, Dimitry Tran, co-founder and CEO of Harrison.ai, highlights the crucial importance of health data – coining it "the new uranium." The delicate nature of health data means that once its primary use is complete, many are hesitant to utilise it further. However, AI cannot function without data. The key is to handle this data with caution, ensuring it can be refined into something that supports AI and drives healthcare innovation. 🎧 Listen to the full episode to hear Dimitry delve into the detail: https://lnkd.in/eq-f2DjD #heathtech #digitalhealth #thehealthtechpodcast #podcast

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