Christian Ulstrup

In case you missed it.... We published a great H:F guest episode with Joel Portice, CEO of egnite - delving into the transformative role of data and AI in revolutionizing cardiovascular care. Joel shares his journey of leading five successful healthcare data companies, including the genesis and rapid growth of egnite, which was spun out from Edwards Life Sciences. egnite's flagship platform, Cardio Care, uses advanced analytics, machine learning, and AI to identify and manage cardiovascular patients, saving lives daily. This episode is a strong addition to our Heart Health Series on Health:Further. Please check it out: https://bit.ly/4fKmzCH

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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“Over one million new articles are added to PubMed annually—about two per minute. No physician can keep up.” This highlights a challenge that PCPs face daily, managing countless chronic and acute conditions while adapting to ever-evolving clinical guidelines. We can’t expect our clinicians to act like machines. At RhythmX AI (https://rhythmx.ai), we address this by revolutionizing primary care with our generative AI-native precision care platform, developed collaboratively by our physicians, specialists, and AI teams. Our platform integrates advanced AI and longitudinal data to deliver patient-specific, evidence-based recommendations directly within clinical workflows. These hyper-personalized insights help physicians make informed decisions at the point of care, bridging the gap between research and practice. Trust is at our core. Our physicians rigorously validate every recommendation to ensure clinical-grade reliability, empowering clinicians to confidently rely on the insights we provide. #PioneerPrimaryCare #Hyper-PersonalizedCare #PCPBurnOut #HealthAI #ResponsibleAI

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I had Spencer on the podcast. He's a Vice Chair and Professor of Medicine at UNC. He's also a great writer on LinkedIn about the intersection of healthcare and AI. Here are some of my key takeaways: -Misaligned Incentives for AI Adoption: Spencer pointed out a lack of clear incentives for healthcare practitioners to adopt AI. Unlike previous shifts, like EMR adoption incentivized by the government, providers have no direct financial motivation to integrate AI. AI tools often come with costs but no immediate reimbursement or clear return on investment. Why would a doctor want to use this or be an early adopter? -The Human-in-the-Loop Fallacy: Spencer questioned the effectiveness of having a human review of AI outputs. He noted that humans often become complacent in repetitive tasks. We chatted through the classic example of TSA agent's inability to catch water bottles vs fake weapons. I thought this was a clever reminder that "human in the loop" does not mean it's better; it could mean it's worse. -Specialization in the Age of AI: We discussed how increasing medical knowledge has led to more specialization throughout history. Spencer mentioned that AI could push this trend in two ways: it might enable further specialization by uncovering new patterns or democratize specialized knowledge, making it more accessible to generalists. Both will likely happen. -Current Use Cases in Healthcare: Spencer noted that AI in healthcare is mainly focused on administrative tasks, such as automated note-writing, patient messaging, and coding/billing. The emphasis is on using AI for less risky tasks, away from direct clinical decision-making. -Gradual Adoption: Spencer predicts a gradual integration of AI in healthcare rather than a sudden shift. He expects AI tools to be adopted piecemeal, starting with those that create value in specific contexts. I had a ton of fun with him on the podcast! Let me know what you think of this. Spencer is your person if you're thinking through the intersection of healthcare and AI.

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This past week I had the pleasure of attending a Connector Series event Presented by IntePros, where Wil Reynolds, VP of Innovation at Seer Interactive discussed an “AI Survival Guide." A huge thank you to Kristin Zeigler for the invite. It was a smaller, more intimate forum which allowed for really effective networking and deeper conversations.   The conversation centered around #GenAI, most notably #ChatGPT, and what it means for our workforce, how to use it most effectively and the learning & development opportunities & challenges it presents for our youth.   Some of my key takeaways and insights that I wanted to share: ➡ Authenticity will become a competitive advantage - For both companies and individuals, authenticity will become a competitive advantage in an increasingly AI-assisted world. Employers, customers, coworkers, and the alike are quickly becoming very good at distinguishing what messages are authentic vs. AI-generated, or as Wil would call it "Lazy-GPT'd." People and companies who are over-reliant on GPTs for original thought, marketing messaging, texts, social media posts, emails and other forms of communication will quickly lose trust and credibility. However, those that use GPTs to poke holes in their ideas, as a benchmark for framing a message or learning quickly about something new to communicate authentically will develop better relationships overtime, and therefore, standout amongst a crowded market (I am happy to report I did not "GPT" this post 😁) ➡ Current GPTs "spit out" the average, not the exceptional - Current GPTs are trained to predict the "average" response to a prompt based on the data they are trained on. Most of the GPTs that are commonly used by the public (such as ChatGPT) are trained on such a large and broad corpus of text data from the internet. What this means for marketers is that GPTs are not going to be very good at providing differentiated messaging. However, what it can help marketers do is accomplish specific, siloed tasks such as summarizing large amounts of market research, poking holes in a human-generated message, or conducting a keyword analysis. ➡ Leadership honesty will attract talent - Candidly, the way most executives at fortune 500 companies are thinking about AI is "how can I get the same amount of work done (or more) with 10% less of our current workforce?" Basic business principles will tell you cutting costs is often a more streamlined way of boosting profits as opposed to growing revenue. Wil makes the argument that top talent will be attracted to leaders who "keep it real" with their employees. In an environment where employees understand their leadership's vision for AI, they are more effectively able to plan for their career/talent development and carve a path within the company (or outside of it).

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I'm currently going through some very old SnagIT screenshots from 2015. I'm trying to recall which hype cycle this is from. Which hype cycle was relevant in 2015? #SelfServeAnalytics? Had we reached #BigData yet? The progress in "#reusability" has been remarkable for the cartoon industry. 😂 #SameHypeDifferentDay

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Unlocking Healthcare Insights with AI: 5 Steps to the Right Approach for LLMs Over my 17+ years in IT, working with many organizations I've witnessed how transformative AI can be useful in healthcare. Large Language Models (LLMs) are now revolutionizing patient care by providing deeper insights into population health, predicting disease trends, and personalizing treatment plans. 🏥 However, implementing LLMs effectively isn't just about adopting new technology—it's about finding the right and cost-effective approach. Here are some steps I've found valuable: 1️⃣ Leverage Existing Infrastructure: Utilize your current systems and integrate LLMs without a complete overhaul, saving costs and resources. 2️⃣ Ensure Compliance from the Start: Align with HIPAA and HITECH regulations early to avoid costly adjustments later. 3️⃣ Embrace Interoperability Standards: Implement HL7 and FHIR protocols for seamless data exchange between systems, reducing integration expenses. 4️⃣ Start Small and Scale Responsibly: Begin with pilot projects to demonstrate value before investing heavily, minimizing financial risk. 5️⃣ Collaborate with Experienced Partners: Work with experts who understand both the technical and regulatory landscapes to navigate complexities efficiently. At Arihant Technologies, we combine deep industry experience with practical solutions to help healthcare organizations adopt AI in a way that's both effective and budget-friendly. #ArihantHealthTech #ArihantTechnologies #healthcare #ai #llms #hipaa #hitech #interoperability #costeffective #experiencematters #healthtech #populationhealth #patientcare #dataanalytics #healthcareinnovation #fhir #hl7 #compliance #digitalhealth #medicalai #affordableai

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Digital Health is spinning its wheels and desperately needs an unbiased review process! There are so many digital health companies competing in the same spaces for the same distracted eyeballs. Even worse, many of these companies inflate capabilities to out claim their competitors. "We save you 2 hours." "Up to 4 hours saved per day." Yet, there are usually no stopwatch studies to back them up. Too much noise and fake news. Based on funding, some digital health companies can blitz the market with marketing campaigns and salespeople. That doesn't make them better, just better known. That can sow confusion. For customers, this bombardment can be dizzying and exhausting. For companies, it can be very frustrating when you can't get the time of day from a prospective customer who is too overwhelmed by choices to consider any product. Every digital health company's goal is to get prospective customers to buy their applications. The problem is customers don't want to buy before they try. Worse, physicians and health systems don't have the time, resources and inclinations to just 'try' products. There's a real opportunity cost to do so. No customer wants to spend time on an EMR integration until they are certain the product will not only deliver as promised, but will also not disrupt workflows, be acceptable to users, and deliver value. Yet, most EMRs won't let you integrate until you have one of their customers asking for the integration. It's a classic chicken and egg situation. Finally, many companies represent point solutions. Alone, they will mightily struggle. Combined with synergistic applications, hope springs eternal. Customers want full solutions they know will work as promised. So here's my suggestion. To bring sense and order to this erratic situation, the US needs a non-partisan, non-profit organization equivalent to Consumer Reports to run annual studies of new applications and rank them based on well established criteria that every customer can universally agree is of value. The customer market would pay for these reviews just like with Consumer Reports. For example, SOAP Health would submit its application to a patient intake competitive review. This is important because at the end of the the day healthcare should be about improving patient outcomes. Too much time and money is being wasted on marketing that doesn't do squat. Let the better technologies shine through faster based on objective review and be applied more confidently across our healthcare system. "Winners going to win." Let's just make the process more efficient, fair, and expeditious. Remember, we are all patients wanting the best care from our system and ourselves. What do you think? #digitalhealth #healthcare #medicine #competition #patientoutcomes

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What do you think about AI in senior care? Personally, I think we have to be careful with it. When AI presents itself as human, it can be confusing for seniors who may not have strong digital literacy skills. In fact, if they think they're talking to a person and not an AI chatbot, it can be dangerous if they're having a medical emergency or issue. However, there are some helpful use cases. I think Kristen Fischer's exploration of AI here is really interesting. "Artificial intelligence may be a useful communication tool to help older adults with cancer communicate with their doctors and have more of a say in their treatment," she says. AI can provide more context or explanation about certain treatment plans and translate the patient’s needs to clinicians in situations where the patient doesn't feel understood or heard. AI will never replace professionals, but it could help patients and clinicians better understand each other. Feel free to share your thoughts on this—I'd love to know if you think AI would be an effective tool for these situations. #AI #SeniorCare #FutureOfHealthcare

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Enjoyed this conversation between Maryrose Lyons & Blake Rizk, MBA - the piece about building a knowledge system resonated - that's what we're enabling with NOAN: an AI-optimized knowledge system for every business, no matter how small, to maximise the results you get from Gen AI. https://lnkd.in/eTPccfPR #ai #artificialintelligence #genai #chatgpt

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We're trying a new thing: Company spotlights to help people better understand what various health IT vendors actually do 🔦 We’ll be cutting through the marketing speak to give short, to-the-point descriptions of a company’s product and/or service + some context on how they see their differentiators. Tag any companies in the comments you'd like to see us cover (and follow Elion if you want to stay in the loop!)

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In an Op-Ed for TIME, Sam Altman of OpenAI and Arianna Huffinton of Thrive Global, write about how AI can transform healthcare and also announce the launch of their new AI Health Coach: Thrive AI Health. 🚀 Thrive AI Health, funded by the OpenAI #Startup Fund and Thrive Global, is developing a hyper personalized AI health coach. Available as a mobile app and within Thrive Global’s enterprise products, it will utilize peer reviewed science and Thrive's behavior change methodology, including Microsteps, which are small daily actions leading to healthier habits. The AI will be trained on personal biometric, lab, and medical data you share, learning your preferences and patterns across sleep, nutrition, movement, stress management, and social connection. With a long term memory, it provides real time, personalized recommendations to help you improve your daily health behaviors. This is a great use case for #AI, and it was also great to read how #OpenAI is partnering with Color Health on an AI copilot to assist doctors in cancer screening and in creating treatment plans after a doctor has made a diagnosis. The full article is linked for further reading. #techforgood #aiforgood #artificialintelligence #generativeAI #genAI #techtrends2024 #technews #aihealth #samaltman #aiadoption #emergingtech #womenintech #technology #healthtech #innovation

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I’ve spoken to 50+ CMIOs/CIOs over the past year. Here are 5 thoughts about AI Scribes I'm hearing that many people are NOT talking about👇 1️⃣ Distribution matters more than Product. Most are just buying Nuance because it’s easier to buy. It’s a known entity. It’s an add-on to the Microsoft contract which means less internal hoops. Key question for who wins long-term: will the startups get distribution faster than the incumbent gets innovation? Will the EHRs eventually just release their own native AI scribes? (my belief is yes… which Oracle/Cerner has already done). 2️⃣ There is a clear “phenotype” for the clinicians who actually use AI Scribes: it’s clinicians who don’t care to have control over how their notes look and sound. But for those who really care about controlling the output? Those folks will keep writing their own notes. 3️⃣ The impact of AI scribes on medical education and trainees is very unclear. Some believe trainees should get exposure to AI scribes early on. Others worry that trainees who don’t learn to write notes are going to miss a critical skill that helps clinicians think. And what happens if there’s a cybersecurity incident and the EHR goes down? Will future clinicians not know how to document manually? 4️⃣ ROI is still hard to truly measure. Few see any improvement in “productivity” (i.e. more patients seen). The real value is in improving clinician well-being. However it’s hard to prove AI scribes are lowering staff churn since that is a lagging indicator many steps away and affected by many other factors. 5️⃣ The per clinician pricing model used by most vendors breaks down at scale. If I have clinicians who would only use the AI scribe for 30% of their patient encounters, am I going to buy a license for them? Probably not. What other less talked about learnings or insights are you hearing behind the scenes?

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This California Management Review article explores the ROI of AI ethics and governance investments through two lenses: loss aversion and value generation. While many companies focus on minimizing risks like legal penalties and reputational harm, the article argues that a value generation approach—using ethics to build trust, foster innovation, and create competitive advantages—offers greater long-term benefits. By integrating ethics into strategic planning, organizations can both mitigate risks and enhance overall growth.

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For Anyone In Healthcare AI. 👈 Colorado shook up the healthcare AI game with the Colorado AI Act (CAIA), setting a new national standard. For digital health innovators nationwide, this means a more regulated landscape to try and ensure fairness and transparency in AI use. CAIA applies tough rules to "high-risk artificial intelligence systems" (HRAI Systems) that have a big say in healthcare or insurance decisions. It's all about preventing bias and ensuring these systems play fair. Developers and deployers now need to lay out all the cards—how their AI learns, its limitations, and how they’re tackling any bias. It's a hefty checklist, but necessary for playing by the rules. Why does this matter outside Colorado? Well, other states will be eyeing CAIA like a playbook. Expect similar laws in other states dropping soon, so having a trusted system in place beats trying to DIY your way through compliance. Clinician's, it’s time to start thinking ahead. Get that AI governance plan sorted, because staying ahead of the regulatory curve isn’t just smart—it’s necessary. You'll want to stop using ChatGPT in your personal account and move to a real system before you get your hand smacked. Disclaimer: I'm founder of Storyline (https://lnkd.in/g2FfZFnh) which is the leader in behavioral health AI platform for clinical care. Rebecca E. Gwilt wrote an excellent post on the subject here: https://lnkd.in/gt6DYNaT

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Would you pay $1500 a month for a health app? A cohort of buzzy digital health start-ups is jostling for a piece of the nation’s lucrative healthcare market but are facing questions over their strategies, pricing and efficacy, with some charging nearly $1500 a month before they’ve publicly launched. One health app, Compound, was charging users $995 a month (or $1495 with medication) – more than 10 times the cost of a typical gym membership – before it hit pause on its operations last week. Its executives and engineers are now rebuilding it from scratch. Eucalyptus chief Tim Doyle said that the issues stemmed from scaling up what was a very labour-intensive, manual offering. “Scaling this has been really hard. It’s a concierge service. Really fundamentally, our members get feedback on how they’re doing their gym routines and workouts at a granular level,” Doyle said. Another Australian app, Bright, said it had sold more than 2500 “early access” passes to users globally, half of whom are based in the US. Described as the “last health app you need”, it connects meal logging, sleep tracking, heart monitoring and more into one subscription. Bright is charging $99.99 for its early access pass, which gives users lifetime access, and it will cost $299.99 a year at public launch. In what is already shaping up as a highly competitive space, CEO Bryan Jordan said he remained sceptical of the business model of the likes of Compound, given how expensive its subscription is. “I’ve yet to hear from independent doctors, surgeons or specialists advocating for consumers to spend $1000 per month to monitor their health, and I’ve yet to read any supporting literature from reputable medical journals,” he said. Another high-profile player is Everlab, which has received $3 million in venture capital funding from European fund b2 Ventures, as well as local funds Ten13, Flying Fox, and AfterWork Ventures. Everlab CEO Marc Hermann says that where Compound has largely marketed to young men who could be described as ‘tech bros’ interested in optimising their performance, Everlab’s focus has been on targeting ordinary middle-aged individuals, male and female. Everlab’s business model is an annual subscription of $250 a month, or $3000 a year, for its “preventative health membership”, billed upfront. The company also takes a deposit of $299 to experience the program before committing. The start-up said it had worked with 300 customers to date. It pairs customers with doctors, consolidates the customer’s historical health data, and then uses AI models to assist clinicians with a diagnosis. While the race to build Australia’s “health app of the future” continues apace, Michael Clements, vice president of the Royal Australian College of General Practitioners, warns of several concerns. Read more at The Age and The Sydney Morning Herald. https://lnkd.in/gRiBZtky

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new post - a few thoughts on AI in healthcare: https://lnkd.in/eGjqDtaj -OpenAI and how AI healthcare companies can build defensibility -How AI will create new types of data and what they might be -Why AI in healthcare probably won’t reduce costs + an example

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As the CEO of an AI-driven health-tech company, Labops, I’m constantly evaluating how technology can create the most impact. While Large Language Models (LLMs) are making headlines, I believe Small to Medium-sized Language Models (SMLs) deserve more attention—especially for businesses operating in niche domains like healthcare. Why do I say this? 1️⃣ Tailored Solutions: In healthcare, where precision and specificity are critical, SMLs (40–50B parameters) are more adaptable to domain-specific needs. 2️⃣ Cost-Effectiveness: For startups, running and fine-tuning a SML is more economical than relying on large, resource-intensive LLMs. 3️⃣ Ownership & Flexibility: Open-source SMLs allow businesses to build and deploy models on their terms, giving greater control over data and performance. At Labops, where we’re building solutions to transform diagnostic services, the ability to leverage AI that aligns with our unique requirements is crucial. SMLs strike the right balance between scalability and practicality, making them a natural fit for startups like ours. LLMs have their place, but they’re not always the answer for every use case—especially in sectors where focus and efficiency matter more than sheer size. If you’re in healthcare or another specialized industry, I encourage you to explore the potential of SMLs. They might just be the competitive edge you’re looking for. What’s your perspective on SML vs. LLM? Let’s discuss! 💬 #AI #HealthTech #LLM #SML #Startups #Innovation #Labops

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