Anwar Hussain

The Billion-Dollar Question: Will Only AI-Powered Healthcare Companies Be Profitable in 10 Years? I always come away with powerful insights from every conference and summit that I attend. By 2033, AI won't just be a competitive advantage in healthcare—it'll be the difference between thriving and extinction. This isn't about incremental change. We're witnessing a paradigm shift akin to the leap from horse-drawn carriages to automobiles. Those who adapt will redefine healthcare; those who don't will be left in the dust. Here's the insight that's keeping CEOs awake: AI in healthcare isn't just about better tools—it's about fundamentally reimagining care delivery. Imagine a world where: Predictive analytics slash hospital readmissions, turning reactive care into proactive health management. AI-driven diagnostics not only improve accuracy but democratize specialist-level insights to every corner of the globe. Personalized treatment plans, powered by real-time data analysis, make one-size-fits-all medicine obsolete. But here's the critical insight: The true value of AI lies not in any single application, but in its network effects across the entire healthcare ecosystem. As AI systems learn and improve, they create a virtuous cycle of better outcomes, lower costs, and increased patient satisfaction. However, this transformation demands more than just technological investment. It requires a seismic shift in organizational DNA. The winners in this new era will be those who cultivate AI-native cultures, where every decision, from the boardroom to the emergency room, is enhanced by artificial intelligence. The most profound change, though, might be philosophical. AI offers us the opportunity to shift from a system focused on treating illness to one centered on maintaining wellness. This isn't just a change in approach—it's a complete redefinition of what healthcare means. As we stand at this crossroads, every healthcare leader must ask themselves: Are we prepared to fundamentally rethink our role in the health ecosystem? Are we ready to move from being providers of episodic care to lifelong health partners? The organizations that answer "yes" and act accordingly will not only be profitable—they'll be the architects of a healthier, more equitable world. #HealthcareRevolution #AIFuture #HealthcareInnovation

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

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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.

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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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WEEKLY HEALTH TECH UPDATE : QUICK READ ‼️ Stay ahead in the Health tech world, follow for regular updates. Google's Med-Gemini Shines in Medical Diagnostics with 91.1% accuracy. Google and DeepMind's recent leap in AI for healthcare brings a smile with its Med-Gemini model, a true medical marvel boasting a 91.1% accuracy in diagnostics! 🌟 Here's the quick scoop: - Multimodal Mastery: Born from the success of the Gemini series, Med-Gemini thrives on language prowess, long-context reasoning, and multimodal understanding. - Highly Specialized: Custom encoders and multimodal fine-tuning tailored specifically for medical applications mean Med-Gemini is not just smart; it's specialized. It's like having a dedicated AI medical resident on your team! - Benchmark Buster: Outshining its predecessors, Med-Gemini dazzled on 14 medical benchmarks, redefining state-of-the-art with superior performance across 25 varied tasks. - Practical Genius: Beyond benchmarks, it excels in real-world tasks like summarizing medical texts and crafting referral letters, sometimes even outpacing human experts. - Interactive Intelligence: Imagine a system that not only understands multimodal medical data but also engages in dialogues, seeking further info as needed—Med-Gemini does just that! Google's foray into specialized healthcare AI like Med-Gemini continues to set impressive benchmarks, potentially transforming the future interactions between healthcare providers and patients. It's not just about numbers; it's about real-world impact. Cheers to smarter AI making a healthier world! 😄

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The same way that vertical SaaS companies shipped POS (point of sale) devices to SMBs in order to drive payments revenue, healthcare infrastructure platforms will distribute AI wearables to independent practices in order to drive more clinical services. This is Telehealth 3.0 which no longer involves just a computer or a phone screen. Now with AI-powered technology, care delivery infrastructure becomes a true companion that moves beyond the old monolithic EHR environment and now into portable devices that handle everything clinical and operations for physicians and clinicians providing care delivery services. It will initially kick off with fractional micro practices who build or hire an AI companion (custom AI agents) that are powered by wearables and other devices, but quickly expand to larger group practices and enterprise organizations. There are millions of physicians and clinicians currently practicing in the United States. What do you think happens when we give each one of them an AI companion? ----- Hi, I'm Dr. Mario Amaro, Founder and CEO Ease, and I talk about all things starting, running, and growing a private practice with AI. Follow me, if you are planning to start one or are currently figuring out creative ways to grow one. #PrivatePractice #SelfEmployment #HealthcareOnLinkedin #HeyEase #IndependentTogether #Fintech #Infrastructure #AIinHealthcare #CareDelivery

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Big Tech is transforming healthcare, and the latest report from CB Insights breaks it down. Amazon, Google, Microsoft, and NVIDIA are not just dabbling in healthcare; they're revolutionizing it. Here is my take on some forensic profiling 😉 (based on the full report). 💡 Amazon is focusing on primary and specialized care, leveraging virtual platforms and partnerships to build a comprehensive healthcare ecosystem. Their goal? Making healthcare more accessible and efficient. 💡 Google is the data powerhouse, turning health data into gold for biotech innovations. With 90% of big tech activity in genetic therapeutics, Google is paving the way for groundbreaking advancements. 💡 Microsoft is the AI enabler, equipping healthcare organizations with cutting-edge tools to enhance clinical research, drug R&D, and care delivery. Their acquisition of Nuance Communications is a game-changer in integrating AI into healthcare. 💡 Nvidia stands at the forefront of smart hospitals and AI-driven drug discovery. Their powerful hardware and innovative AI solutions are set to redefine medical imaging and surgical applications. These tech giants are not just making waves—they're creating tsunamis in the healthcare industry. The future of healthcare is here, driven by relentless innovation and strategic investments. 👉 What are your thoughts on tech’s role in healthcare? 👉 How do you see these advancements impacting patient care and medical research? Let’s dive into this conversation! Share your insights and let’s explore the future of healthcare together. #HealthcareInnovation #TechInHealthcare #FutureOfMedicine

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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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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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MYTH: Designing a user-friendly digital health app with seamless data sharing is nearly impossible for new health tech startups. FACT: Any new startup can create interoperable and efficient digital health systems with this approach. Some time last year, I had a lively brainstorming session with a founder eager to build a personal electronic health record app. The founder had awesome ideas but was getting stuck at the complexities of data sharing and interoperability. We started breaking down the basics. And this Venn diagram is a great summary of the info I shared with them relating to the South Africa National Health Normative Standards Framework. #Protections: First priority is data security. The ISO 22600 standard deals with this well. It keeps the data subject safe basically through privilege management and access control. #Intent: It's important to include explicit statements about intent to share data, and where it’s going to be stored. Vague statements like “Upon request” are generally frowned upon. #Benefits: For the developers, using the HL7 CDA and ICD-10/11 standardized data formats and codes really make life easier. This helps ecosystem understand the data flow. No more broken telephone with data mappings, transformations or APIs. For the data subject, this means accurate communication between healthcare providers, better coordinated care. It also reduces needing to repeat tests or provide the same information multiple times and ultimately, fewer errors in their medical records. #Consent: Patient consent is the golden threat that weaves it all together. that users have the capacity to decide, have full disclosure of information, understand what will happen to their data, and that their consent is given voluntarily. #Performance: Finally, data sharing is necessary because it helps businesses to perform their duties / fulfil their obligations. When data crosses borders it's governed by Cross-border data legislation. The GDPR provides support through its Standard Contractual Clauses (SCCs) framework. #BonusTip: To stay compliant, you should appoint a data champion (data protection officer), and have some sort of data breach notification policy. When you remove the legal-speak, even the most complex aspects of health data design become manageable. My experience has shown that with the right approach, creating a secure, user-friendly, and interoperable system is not just possible—it's straightforward. #digitalhealth #datasharing #interoperability #healthdata

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We may not agree with all, but Vinod Khosla's insights on AI reveal crucial possibilities we face: ➕ Job Displacement: Automation could eliminate up to 80% of current jobs, leading to significant economic challenges. ➕ Healthcare Transformation: AI can revolutionize patient care by enhancing diagnostics and enabling personalized treatments, potentially saving millions of lives. ➖ Widening Inequality: If unchecked, AI might deepen the wealth gap, benefiting a select few while leaving many behind. ➖ Ethical Concerns: Risks include authoritarian surveillance, systemic biases, and the spread of misinformation. ➕ Collaboration is Key: The focus should be on AI augmenting human abilities, not replacing them. ➕ Regulatory Frameworks: There’s an urgent need for ethical guidelines to govern AI development responsibly. Future of AI needs both caution and optimism.

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Ever wondered about the most frequently used J-code among general practitioners? The answer might surprise you – it certainly surprised me. In the value-based care ecosystem, J-codes (HCPCS codes for injectable and non-oral drugs) are crucial indicators of care patterns and cost drivers. These codes encompass everything from expensive specialty medications to popular drugs like Ozempic. Understanding their utilization patterns isn't just about tracking numbers – it's about ensuring appropriate use and identifying opportunities for care optimization. Using the CMS Provider/Supplier Procedure Summary dataset (yes, they are in mimilabs), I analyzed J-code trends among general practitioners. The standout finding? Testosterone cypionate injections (J1071) dominated the landscape. The visualization shows a dramatic spike in 2015, followed by a gradual decline – yet still maintaining volumes significantly higher than other J-codes. Here's the crucial context: J1071 was introduced in 2015, explaining the initial spike. The subsequent downward trend correlates with the FDA's updated labeling requirements for testosterone products that same year, highlighting how regulatory changes can directly impact prescription patterns. This analysis raises some critical questions for healthcare leaders: - How do we identify opportunities and potential concerns in J-code utilization patterns? - What role does provider specialty play in interpreting these patterns? - How do we differentiate between appropriate utilization (like chemotherapy drugs in oncology) and areas that need attention? - What analytical frameworks can we develop to better understand these prescription patterns in the context of value-based care? I'm particularly interested in hearing about your approaches to J-code analysis. How do you factor in specialty-specific contexts? What patterns have you observed in your own data? #HealthcareAnalytics #ValueBasedCare #Healthcare #DataAnalysis #MedicalCoding

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I love Out of Pocket, but their recent article on AI not reducing healthcare costs is a classic strawman argument. Here's why: The author (Nikhil Krishnan) makes two key arguments: 1. AI doesn't reduce healthcare costs because there's no competitive pressure 2. AI diagnostic screening is currently more expensive than MD interpretation for that same screen To the first point, Nikhil Krishnan says that AI won't reduce costs because: 1. healthcare markets aren't competitive 2. lost revenue in one area leads to increased costs elsewhere 3. and AI is only adopted if it increases demand or reduces staffing This view is short-sighted. → It undervalues improved accuracy & patient outcomes. AI systems can have significantly lower false negative rates. Yes, we're paying more now. But we're getting a superior product that saves lives and reduces long-term healthcare costs. → It assumes static healthcare delivery models. AI won't just slot into existing workflows – it will transform them. With better diagnostic capabilities, we can re-think current care models and imagine entirely new paradigms. Many new things become possible once we can deliver hyper-personalized care for ~zero marginal cost. To the second point, Nikhil Krishnan argues that AI-based diagnostics, like diabetic retinopathy screening, are more expensive than human interpretation. (Citing CPT code 92229 as an example) [1] Again, this misses the mark. → It ignores the current regulatory landscape. Most imaging diagnostics legally require human oversight. We're not comparing AI vs. MD; we're comparing MD vs. MD+AI. Of course, an add-on increases costs initially. But as regulations evolve to allow fully automated solutions, costs will plummet. → It's short-sighted. Remember when solar power, or self-driving cars seemed absurdly expensive? We're at the bleeding edge of healthcare AI. As the technology matures and scales, costs will decrease dramatically. So where can AI really lead healthcare? Precision triage → AI can handle routine cases, freeing specialists for complex diagnoses. This optimizes valuable human expertise. Early intervention → By catching issues earlier and more accurately, we reduce long-term complications and costs. Democratized expertise → AI can bring specialist-level insights to primary care settings, improving access and outcomes. Personalized treatment plans → AI can process vast amounts of data to tailor treatments to individual patients. The benefits are clear: 1. Improved patient outcomes 2. Reduced healthcare worker burnout 3. More efficient resource allocation 4. Long-term cost savings through prevention and early intervention We need to think long-term. Just like renewable energy seemed prohibitively expensive at first, the true value of AI in healthcare will be realized over decades, not quarters.

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In healthcare, technology is rapidly advancing – AI, automation, and other digital innovations are making their mark. But there's one thing that remains irreplaceable. One thing that, despite us being at the point of discovery in a new era of tech, remains safeguarded in healthcare. And that is the critical point of communication for HCPs — your device rep. At the heart of every successful hospital operation is a trusted relationship between healthcare providers and their device representatives. And even as we all gravitate towards tech for faster processes, better outcomes, and lower costs, the rep remains the face of personalized service. They understand the nuances of your needs, and no amount of AI, automation, and other digital innovations can replicate that level of care. When technical problems, back-orders, or tech debt arise, they’re the ones you can rely on. Though AI is not the issue, it’s how rapidly we attempt to implement it as a cookie-cutter solution — as a fix-all before it’s ready. Because there’s no doubt these advancements in tech will shape the future, and in many ways for the better, but we’ve all experienced the frustration of yelling, "Customer representative!" into a phone while dealing with automated systems. It’s a reality we’re all familiar with. Though, it’s a reality that, as healthcare technology companies, we can’t allow to take root in healthcare. In an industry where human lives are on the line, a human point of contact is critical. Your rep is that contact – the one who ensures you have what you need, when you need it. That’s why, even as we move forward into a more technology-driven future, one thing will remain constant: the importance of communication between medical device companies and HCPs. At PROXDIS, we know technology will undoubtedly evolve, but the role of the rep in ensuring patient and provider success will never be rendered obsolete. We know there's some things that just stay solid. And we’re here to safeguard the foundation that relationship with your rep represents. Because no matter how advanced technology becomes, nothing will replace the trust, reliability and understanding that comes from having a human point of communication. Technology may explode exponentially — but PROXDIS is on your level, and we’re here to stay.

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Every Health Tech Innovation sounds great in theory… BUT health systems should “stress test” the idea to see if it works for YOU. Here’s how 👇 Suppose you were considering something like SeamlessMD to provide digital care journeys pre/post-surgery, oncology, chronic care, etc. Sure it sounds great on paper to provide every patient with a digital experience to guide them with reminders, education and symptom tracking. But before you get started, here are 5 questions your health system stakeholders need to ask yourselves: 1️⃣ Does the vendor’s product and track record give us confidence in their ability to deliver this great sounding innovation? If not, are we the right health system to take on this level of risk? 2️⃣ Do we have clinical champions across physicians, nurses, allied health, etc. willing to engage in the clinical content review process? Because you need to be aligned on the clinical content being delivered to patients. 3️⃣ Does IT have the resources to integrate with the platform to automate patient enrollment? Or are we willing to use it as a standalone platform and have staff willing to spend 30 seconds to enroll a patient in the backend? 4️⃣ Are we willing to train staff on how to educate and promote this initiative to patients and families? Remember that staff endorsement highly correlates with patient adoption. Digital Health doesn’t sell itself. 5️⃣ Do we want to take full advantage of the platform and enable alerts and dashboards to remotely monitor patients? It’s not a requirement but it has a bigger impact on outcomes, especially readmissions and ED visits. As you work through these questions, you will find out if your team is motivated and willing to engage fully in the Digital Transformation and change management process needed to make the Tech successful. Also, the better a Vendor Partner, the more they can actually guide you through these questions. In many cases they may make it easier for you to get to a “Yes” because they have proven playbooks and processes that make your staff more confident in the change management required. Now if you end up going through this “stress test” and realize the innovation isn’t the right fit, that doesn’t mean you’ll never do it… It just might mean it doesn’t make sense today. But better to realize that upfront than to buy a great Product… and then fail the implementation because you didn’t think about the People and Process aspects beforehand.

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Fascinating insights shared by NEJM AI on the limitations of #LLMs in medical coding! The study by Ali Soroush, MD, MS, and colleagues reveals critical benchmarks in LLMs' ability to map medical codes accurately. Key findings highlight the error-proneness of these models, with #GPT-4 showing the best but still limited performance. The in-depth analysis provided in the study underscores the complexities of medical code querying and the need for extensive training and perhaps model adjustments to ensure precision and reliability. The real takeaway? There's a significant journey ahead in harnessing AI effectively in healthcare administrative tasks. That is why there is a need for medical-specific models trained on medical knowledge and not just random stuff on the internet. 🔗 https://lnkd.in/g2XtCYPC #AIinMedicine #HealthTech #ArtificialIntelligence #MedicalCoding HOPPR

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