Healthcare

The Centers for Medicare & Medicaid Services has proposed that Medicare Advantage plan revenues will remain flat going into 2027 at a moment when underlying medical costs, labor expenses, and pharmaceuticals continue to rise materially. What does this mean in practice? For beneficiaries: Over time, beneficiaries should expect less generous benefits, tighter utilization management, and narrower provider networks. Access may become more constrained—not necessarily through explicit benefit cuts, but through fewer participating provider groups and more selective contracting. The tradeoff between affordability and choice will become more acute. For brokers and distribution partners: Distribution costs in Medicare Advantage are largely fixed, particularly commissions and marketing infrastructure. As margins compress, plans will continue to reassess how (and how much) they pay for growth. This may include lower upfront commissions, greater reliance on retention-based compensation, or shifts toward more direct-to-consumer enrollment strategies. For provider groups: Provider organizations seeking rate increases will face a much tougher negotiating environment. With plan revenues constrained, upward pressure on provider rates becomes difficult to absorb. As a result, some provider groups may choose to exit Medicare Advantage entirely, while others will narrow participation to fewer plans. The result may be increased network fragmentation and heightened tension between plans and providers over risk, quality expectations, and total cost of care. For managed care company employees: Cost discipline will extend inward. Plans will be slower to hire, more selective about new investments, and may pursue workforce reductions. Expectations will shift toward higher productivity, flatter organizational structures, and doing more with fewer resources. For Investor-backed Medicare Advantage plans: The economics of growth will change. Longer payback periods, lower internal rates of return, and greater regulatory uncertainty will make Medicare Advantage investments less immediately attractive. Capital will still flow to the sector, but it will be more discriminating, favoring scale, operational excellence, and differentiated capabilities rather than growth at any cost. For small and regional health plans: Scale matters more than ever. Smaller plans will struggle to compete. Many may exit the market or seek partnerships, mergers, or acquisitions. Consolidation pressures are likely to intensify as fixed administrative and compliance costs consume a greater share of revenue. Time will tell whether the rate decisions outlined in the Advance Notice hold through the Final Rule. Regardless of the ultimate number, one thing is clear: Medicare Advantage is entering a period of transition. The era of easy growth is ending, and the next phase will be defined by tradeoffs—between generosity and sustainability, growth and discipline, innovation and affordability.

This hospital charges ₹1999/year for unlimited doctor visits and tests - for a family of 4. Here's how they're making money while doing it. Most Tier 1 city hospitals in India are stuck in a broken cycle. They spend ₹2 crores per bed just on land and construction. This debt pressures them to overcharge, overcrowd OPDs, and push doctors to generate more revenue. Superhealth in Bangalore is doing something completely different. And I think it could change healthcare for millions of people. Here's what they've built 👇 ▶ 1. The VIP Pass model ₹1999/year gets a family of 4: - Unlimited doctor consultations - All prescribed tests covered (yes, even MRIs) How is this viable? The B2B cost of common tests is incredibly low. By cutting out traditional markups and billing friction, they can offer it at near-cost. ▶ 2. Slashed infrastructure costs by 65% They don't buy land or buildings. They lease old structures - like shopping malls - and convert them into 50-bed facilities. Construction drops from 3-6 years to just 120 days using standardized designs and prefabrication. So cost per bed? ₹70 lakhs instead of ₹2 crores. ▶ 3. Faster patient turnover Traditional hospitals keep patients for 3-5 days on average (often to maximise revenue). Superhealth's procedures are optimised 1-1.5 day length of stay. This means their 50-bed facility matches the patient volume of a 150-bed traditional hospital. ▶ 4. Fixed salaries for doctors No commissions. No referral fees. No pressure to over-prescribe. Doctors get ESOPs instead, aligning them with long-term patient outcomes rather than short-term revenue. ▶ 5. Transparent, fixed pricing Whether you're paying cash or using insurance, the price is fixed. No surprises. No hidden costs. Discharge happens within 15 minutes of the doctor's approval because billing is already settled. So the real innovation isn't just affordability. It's proving you can build profitable, high-quality healthcare without exploiting patients. They're essentially competing with health insurance by removing the friction and anxiety that plague traditional care. Book appointment on the app. Walk in. See the doctor. Get tests done. Walk out. No waiting. No billing hassles. Super easy. And I think that’s incredible. Do you think this model could work in your city? #entrepreneurship #healthtech #innovation

Polling vs Webhooks As systems grow more complex, choosing the right update strategy becomes crucial. Let me break down the two primary approaches that define real-time data synchronization: Polling: The Traditional Approach • Client periodically requests updates • Predictable but resource-intensive • Full control over request timing • Higher latency, higher costs at scale Webhooks: The Modern Push System • Server notifies client of changes • Event-driven and efficient • Near real-time updates • Better resource utilization Concrete Implementation Examples: Polling Works Best For: 1. Payment status checks 2. Order tracking systems 3. Basic monitoring tools 4. MVP implementations 5. Systems with predictable update patterns Webhooks Excel In: 1. Payment processing (PayPal) 2. Repository events (GitHub) 3. CRM integrations (Salesforce) 4. E-commerce inventory updates 5. Real-time messaging systems Key Decision Factors: - Update frequency requirements - Infrastructure complexity tolerance - Development team expertise - System scalability needs - Budget constraints Currently implementing these in production? Both approaches have their place. The key is matching the solution to your specific requirements rather than following trends.

Surgical robots cost $2 million. Beijing just built one for $200,000. Watch it peel a quail egg: Shell removed. Inner membrane intact. Submillimeter accuracy that matches da Vinci at 90% less cost. Think about that. Most hospitals can't afford surgical robots. Rural clinics? Forget it. Patients travel hundreds of miles for robotic surgery or settle for traditional operations with higher risks. Beijing's Surgerii Robotics just broke that equation. Traditional Surgical Robotics: ↳ $2 million purchase price ↳ $200,000 annual maintenance ↳ Only major hospitals qualify ↳ Patients travel or wait Chinese Innovation Reality: ↳ $200,000 total cost ↳ Same precision standards ↳ Reaches district hospitals ↳ Surgery comes to patients But here's what stopped me cold: Professor Samuel Au left da Vinci to build a network of surgical robots. Engineers from Medtronic and GE walked away from Silicon Valley salaries to build this. They're not chasing profit margins. They're chasing one vision: "Every hospital should have one." The egg demonstration proves what matters: Precision doesn't require premium pricing. The robot's multi-backbone continuum mechanisms deliver the same submillimeter accuracy whether peeling eggs or operating on hearts. What This Enables: ↳ Thoracic surgery in rural hospitals ↳ Urological procedures locally ↳ Reduced surgical trauma everywhere ↳ Surgeon shortage solutions The Multiplication Effect: 1 affordable robot = 10 hospitals equipped 100 deployed = provincial healthcare transformed 1,000 units = surgical access democratized At scale = geography stops determining survival Traditional robotics kept precision exclusive. Surgerii makes it accessible. We're not watching price competition. We're watching healthcare democratisation. Because that farmer needing heart surgery shouldn't die waiting for a $2 million robot his hospital will never afford. Follow me, Dr. Martha Boeckenfeld for innovations that put patients before profit margins. ♻️ Share if surgical precision should be accessible, not exclusive. #healthcare #innovation #precisionmedicine

Insurance Fraud For over last 15 years, I have been highlighting two aspects of Insurance Fraud 1. Fraudulent activities are getting more organized year on year 2. Next decade or so will see more fraud in Critical Illness and Personal Accident/ disability The case below is a live example of both: The appended ECG surfaced as evidence of heart attack in a critical illness claim of first heart attack - in 3 different claims. This ECG was, allegedly, taken in 3 different hospitals on 3 different patients in 3 different districts of two states. Why is it the same ECG? One may counter the allegation that 2 different persons can have the same ECG. Answer to this: - The flat line in V5 (highlighted with a box) is an artifact due to machine error and accepting that 3 different machines will have same artifact is ignoring the fraud (known as leakage in an organization) - Two independent, senior cardiologists have opined that these three ECGs belong to the same person. An ECG is akin to one's finger prints - no two persons can have EXACT same ECGs. Fortunately (unfortunately for the fraudsters) these attempts were made on the same insurer, hence were identified. In our 20 year+ journey in risk management, we have come across same ECG and same TMT being used for different proposals at policy inception stage but 3 cases, 3 districts, 3 hospitals - is first even for us. Insurers have to be more vigilant for critical illness and personal accident claims. Sanjiv Dwivedi Bhaskar Nerurkar Sweetie Salve Rajat Goyal Namrata Jain (Kumar) Manish Dodeja Priya Deshmukh-Gilbile Siddhartha Kansal Dr Sushma Jaiswal Dr Satish Kanojia Imtiaz Shaikh Preeti Desai Vishal Dubhashi #insurancefraud #organisedfraud #criticalillness #fraud management

When you’ve been a patient inside the healthcare system you work in, you start noticing the little things: the silence after a monitor alarm, the hallway conversation you’re not sure was meant for you, the well-meaning “we’ll know more soon." The list goes on. I’ve experienced world-class medicine across the country all thanks to my heart transplant. But the system isn’t only a collection of procedures. It’s also a network of people and pauses. One missed follow-up call or one delay that no one explains? These become mountains when you’re the one in the bed. Yes, design is about technology and efficient throughput, but it's also about how a system feels when you’re scared. When I returned to medicine as a physician, those 'patient experience' memories followed me into every patient encounter. They changed how I communicate, lead, & potentially help design future systems. Good healthcare solves problems. But in my opinion, great healthcare prevents people from feeling like one. If we design for that moment between uncertainty and trust, we design for the kind of system we all want to work in. #womeninmedicine #patientdoctor #doctor

5 key developments this month in Wearable Devices supporting Digital Health ranging from current innovations to exciting future breakthroughs. And I made it all the way through without mentioning AI… until now. Oops! >> 🔘Movano Health has received FDA 510(k) clearance for its EvieMED Ring, a wearable that tracks metrics like blood oxygen, heart rate, mood, sleep, and activity. This approval enables the company to expand into remote patient monitoring, clinical trials, and post-trial management, with upcoming collaborations including a pilot study with a major payor and a clinical trial at MIT 🔘ŌURA has launched Symptom Radar, a new feature for its smart rings that analyzes heart rate, temperature, and breathing patterns to detect early signs of respiratory illness before symptoms fully develop. While it doesn’t diagnose specific conditions, it provides an “illness warning light” so users can prioritize rest and potentially recover more quickly 🔘A temporary scalp tattoo made from conductive polymers can measure brain activity without bulky electrodes or gels simplifying EEG recordings and reducing patient discomfort. Printed directly onto the head, it currently works well on bald or buzz-cut scalps, and future modifications, like specialized nozzles or robotic 'fingers', may enable use with longer hair 🔘Researchers have developed a wearable ultrasound patch that continuously and non-invasively monitors blood pressure, showing accuracy comparable to clinical devices in tests. The soft skin patch sensor could offer a simpler, more reliable alternative to traditional cuffs and invasive arterial lines, with future plans for large-scale trials and wireless, battery-powered versions 🔘According to researchers, a new generation of wearable sensors will continuously track biochemical markers such as hydration levels, electrolytes, inflammatory signals, and even viruses, from bodily fluids like sweat, saliva, tears, and breath. By providing minimally invasive data and alerting users to subtle health changes before they become critical, these devices could accelerate diagnosis, improve patient monitoring, and reduce discomfort (see image) 👇Links to related articles in comments #DigitalHealth #Wearables

This is my face finishing the last pieces of my documentation after my #ER shift. It's a face of frustration after spending way too much time documenting in a less-than-intuitive, inefficient EMR. It's the face of frustration from endless clicks, digital pop-up blockades, and seek-and-find missions for clicking the correct checkbox in an electronic health record to simply discharge a patient. The ultimate price of this inefficiency: compromised patient care, delays, errors, skyrocketing stress for healthcare professionals, and an overall decline in the system's effectiveness. It's time to streamline our processes for the sake of our clinicians and, most importantly, our patients. The problem: EMRs were made as billing platforms with patient care and clinical workflows as secondary considerations. The solution: 1. Put frontline clinicians back in the boardroom to fix these inefficiencies. 2. Reduce and eliminate unnecessary administrative tasks. 3. Utilize trainers to perform frequent check-ins with clinicians to ensure clinicians use the best and most efficient documentation methods. 4. Leverage new technologies (like AI, dictation software, ambient listening software) to reduce screen and keyboard time for clinicians. 5. Create standardized workflows for documentation. The more ways to do the same thing, the more challenging it is to teach and build efficiencies across a team. 6. EMR companies should use practicing, specialty-specific clinicians to guide design decisions. #HealthcareSystem #ClinicianBurnout #TimeForChange Cerner Corporation Epic MEDITECH #EMR ABIG Health #frontlineclinicians #nurses #physicians #hospitals

Advancements in biomarker testing in Alzheimer’s disease, including blood tests and PET imaging, can help to identify disease pathology in symptomatic patients in a less invasive way, potentially opening the door for more timely and accurate diagnosis. With every advancement, I reflect on how this could have affected my mother’s journey with Alzheimer’s disease, and it brings me great hope to know these innovations have the potential to change the diagnostic and treatment landscape for today’s patients. Read more about how the evolving diagnostic paradigm can combat barriers to early diagnosis from Anthony Sireci, MD, MSc, Head of Diagnostics Development & Commercialization: https://e.lilly/4gnjXfg 

Let's build a Real Time ML System to fraud. Step by step 🧵↓ 𝗧𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 💼 Every time your credit card is used online by someone (hopefully you), your card issuer (for example Visa, Mastercard or PayPal) has to verify if it is you the person trying to pay with the card. Otherwise, the transaction is blocked. Now the question is: ““𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗩𝗶𝘀𝗮 𝗱𝗼 𝘁𝗵𝗮𝘁?”” And the answer is… a real time ML system! 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 📐 As any ML system that has existed, exists and will exist, this one can be broken down into 3 types pipelines 1️⃣ Feature pipelines 2️⃣ Training pipeline 3️⃣ Inference pipeline Let's go one by one 1️⃣ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 💾  The feature pipelines are the Python services that produce the inputs (aka features) our ML model needs to generate its predictions. In our case, we have (and I bet Visa has) at least 3 feature pipelines: ▣ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 feature pipeline from recent transactional data. - runs 24/7 - consumes incoming data from an internal message bus (like Kafka, Redpanda) - transforms this data on-the-fly using a real-time data processing engine - saves the the final features in a feature store, like Hopsworks. ▣ 𝗕𝗮𝘁𝗰𝗵 pipeline from historical features in the data warehouse. - runs daily - reads data from the data warehouse/lake, and - saves it into another feature group in our feature store, so it can be consumed by our ML model really fast. ▣ 𝗟𝗮𝗯𝗲𝗹𝘀 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, so the ML model can be trained with supervised ML. Each completed transaction that is not claimed by the card owner within 6 months can be safely called non-fraudulent (class=0). We call it fraudulent (class=1) otherwise. Once we have these 3 feature pipelines up and running, we will start collecting valuable data, that we can use to train ML models. 2️⃣ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🏋🏽 We can use a supervised ML model (a boosting tree model like XGBoost does the job in most cases) to uncover any patterns between > the features available in your Feature Store, and > the transaction class: 0 = non-fraudulent, 1 = fraudulent. The final model is pushed to the model registry (like MLflow, Comet or Weights & Biases), so it can be loaded and used by our deployed model. And this is precisely what the last pipeline in our design does. 3️⃣ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🔮 The inference pipeline is a Python streaming application, that at start up loads the model from the registry into memory and for every incoming transaction > loads the freshest features from the store for that card_id, > feeds them to the model, and > outputs the predictions to another Kafka topic. These fraud scores can be then consumed by downstream services, to > Block the card, and > Send an SMS alert to the card owner, for example. BOOM! No dark magic. Just Real World ML. Follow Pau Labarta Bajo for more Real World ML