Healthcare

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

Hospitals are healing patients faster with 30-year-old Australian technology. Most healthcare facilities still operate in the dark. SolarTube skylights channel natural sunlight through reflective tubes directly into patient rooms and treatment areas. No electricity needed. Just free healing light all day. The healthcare transformation numbers: ↳ Faster patient recovery rates documented ↳ 15% staff productivity increase ↳ Reduced eye strain for medical professionals ↳ Lower patient anxiety during procedures Think about that. Tigoni Medical Center in Kenya installed SolarTubes in their COVID-19 facility. Healthcare workers reported less fatigue, increased alertness during long shifts. Patients showed dramatically improved morale and energy levels. At Rogaska Medical Center, natural daylight flooded clinics without unwanted heat. Staff comfort improved. Patient outcomes followed. Italian dental offices meeting occupational daylight standards found something unexpected: patients felt less anxious. Procedures became more comfortable. Natural light calmed nerves that fluorescent bulbs couldn't. Traditional Healthcare Lighting: ↳ Fluorescent tubes causing eye strain ↳ High electricity costs ↳ Artificial environments ↳ Staff fatigue increases SolarTube Healthcare Reality: ↳ Natural light reduces stress hormones ↳ Serotonin production increases ↳ Circadian rhythms regulate properly ↳ Recovery accelerates naturally But here's what stopped me cold: We're medicating depression while keeping people in artificial light. Jim Rillie invented this solution in the 1980s. Launched Solatube International in 1991. Now 2 million units worldwide bring natural light indoors. Healthcare facilities that adopt it see measurable improvements. Staff wellness increases. Patient satisfaction scores rise. Recovery times shorten. The Multiplication Effect: 1 hospital = hundreds healing faster 100 facilities = thousands of staff energised 1,000 installations = healthcare transformed At scale = medicine working with nature VCC in the UK experienced enhanced well-being building-wide. Staff and patients reported feeling calmer, healthier, happier. Simply from abundant daylight. We're not just installing skylights. We're installing wellness. One beam of natural light at a time. Follow me, Dr. Martha Boeckenfeld for innovations that heal environments and people. ♻️ Share if you believe healthcare should harness nature's healing power.

This is another example of how pragmatic, unglamorous AI applications are often the most useful—using NLP to classify and route messages rather than Gen AI to answer them. Since the pandemic, doctors have been drowning in patient portal messages. So, naturally, when ChatGPT launched in late 2022, many of us thought, "Let’s use LLMs to generate drafts!" (Justin Norden, MD, MBA, MPhil and I soon wrote an article about this). Fast forward to 2025 and experience shows that ChatGPT isn’t great for drafting message responses. Studies out of Stanford and U of Colorado showed clinicians use only 12-20% of GPT-generated drafts, ignoring the rest. [doi:10.1001/jamanetworkopen.2024.3201] [doi:10.1001/jamanetworkopen.2024.38573] Additionally, a study from UCSD showed that PCPs using Gen AI drafts paradoxically spend 20% more time responding to messages. [doi:10.1001/jamanetworkopen.2024.6565] Why? Because it’s much easier to type a response than edit an AI-generated response. (I also believe patients want to hear from their actual doctor, not a canned response). However, looking at the broader workflow, we see that one key challenge is getting the message to the right teammate. For example, doctors should only see clinical messages that only they can answer. We don't need to see messages about changing pharmacies, sending refills, or scheduling appointments, etc. Despite this, messages consistently route to the wrong teammates, leading them to pass around (forward) the message like a hot potato. (Often staff forward a message 5+ times before someone resolves it). So, I was excited to read this newly published NEJM AI article. [doi: 10.1056/AIoa2400354] Switchboard, MD and Emory University developed, fine-tuned, and deployed an NLP model to classify messages into one of the following categories: urgent, clinical, refill, scheduling, or forms. Next, they used the output to route the message to the right team member. For example, messages classified as “schedule” were routed to the scheduling group. Their model was 98% accurate for predicting message type. They also found that, compared to a control group, staff responded to NLP-processed messages 1 hour faster, resolved conversations 22.5 hours faster, and had two fewer touches. This shows the importance of applying AI to “solve” for the right workflow pain points. While NLP classifiers may be far less glamorous than Gen AI drafted responses, they are far more helpful.

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

Imagine a health worker arriving at a community health center only to find that vaccines – which must be kept at the right temperature to be effective – have spoiled due to a lack of refrigeration. In regions with unreliable power, this is a harsh reality.  “Solar-powered” refrigerators are changing that.  These units (see the blue fridge in the photo below) connect to solar panels installed on clinic rooftops instead of relying on batteries or fuel, which keeps vaccines at the right temperature and ensures immunizations continue – even in the most remote areas or when power is disrupted, such as during cyclones and other climate-induced emergencies. Thanks to partners like Gavi, the Vaccine Alliance and UNICEF, these innovations are making a real impact. Continued investment in solutions like this means fewer wasted vaccines and more children are protected from deadly diseases.  Read more about how these fridges are making a difference in countries like Malawi: https://lnkd.in/gWYKRQ87

Most healthcare AI doesn't stall because models underperform. It stalls because infrastructure is fragmented. We are no longer constrained by algorithmic creativity. We are constrained by data silos, privacy governance, interoperability gaps, compute access, and the operational friction of translating retrospective research into prospective clinical impact. This brief examines this structural bottleneck through the Mayo Clinic Platform. The authors focus on something foundational: building an AI-ready ecosystem designed to accelerate real-world clinical research at scale. The platform provides a secure, cloud-based research environment built on de-identified, standardized EHR data from more than 15 million patients. Key capabilities include: ⭐ OMOP-aligned data models for interoperability ⭐ Structured and unstructured data ⭐ Cohort-building and schema exploration tools ⭐ Integrated workspaces with scalable CPU/GPU infrastructure ⭐ Both no-code and advanced coding environments Unlike traditional institutional repositories, Mayo Clinic Platform enables access for external researchers, supports federated multi-institutional data contributions, and embeds analytics within a privacy-preserving architecture. The paper highlights four applied studies conducted within MCP: 1️⃣ RCT emulation for heart failure drug efficacy using observational data 2️⃣ Validation of antihypertensive medications and reduced dementia risk 3️⃣ Deep learning prediction of mild cognitive impairment progression to Alzheimer’s disease 4️⃣ Neural network prediction of major adverse cardiovascular events after liver transplantation Extracting a cohort of ~15,000 patients took approximately one week. Training and running a deep learning model required roughly 10 minutes on moderate compute resources. When infrastructure friction is minimized, research velocity changes materially. Competitive advantage in healthcare AI is increasingly defined by: 💫 Data harmonization at scale 💫 Federated, privacy-preserving architectures 💫 Reproducible research pipelines 💫 Integrated compute environments 💫 Lower barriers for clinician engagement The authors also point toward multimodal expansion (notes, imaging, genomics), large-scale cross-institutional validation, and “Clinical Trials Beyond Walls” models that broaden participation and diversify real-world evidence. For those shaping AI strategy in health systems, pharma, or digital health, this paper offers a concrete example of production-grade, AI-ready infrastructure. The future of healthcare AI will not be won by isolated models. It will be won by platforms that integrate data, governance, compute, and workflow into a coherent operating system for translational impact. John Halamka, M.D., M.S. and team, great work! #HealthcareAI #HealthSystems #RealWorldEvidence #ClinicalResearch #DigitalHealth #TranslationalMedicine #PrecisionMedicine #HealthData #AIInfrastructure #MedicalInnovation

Health plans and health systems and a growing number of startups often pride themselves on “care management.” But a few decades into a career in healthcare, it’s increasingly clear that care management means different things to different people. Some observations: Good Care Management -leverages a pre-existing relationship with the patient and feels connected to the clinicians and other health care professionals who are caring for them -uses all available data to build a composite view of what is happening with a patient and communicating that view (helping patients feel seen) -delivers proactive continuous management of needs based on deep knowledge of a patient’s clinical condition -allows creative problem solving (“whatever it takes”) to go above and beyond when needed Bad Care Management -assigns strangers to call strangers -fails to use existing data about a patient to inform engagement -focuses on regulatory compliance and checking boxes in a check lists -delivers episodic engagement by a cast of strangers who are relearning the patient’s situation every time -leverages “in the box,” perfunctory thinking that ignores or glosses over complex situations when they don’t fit into a neat category Fundamental to the problem of care management is building and scaling a culture of caring that supports and empowers compassionate and skilled care managers to do the real work of helping people rather than documentation for its own sake. But “caring” is too often constrained by “operations” that fail to enable the flexibility and agency needed to truly demonstrate care. Does “care management” really make a difference? Where have you seen “good” or “bad” care management? What made it good or bad?

Dr. S M Ziaur Rahman, a medical professional, has made a commendable transition from a high-paying position in Delhi to establish a vital healthcare center in rural Bihar. His initiative directly addresses a critical gap in rural medical infrastructure, exemplified by his nominal charge of just ₹250 per patient visit. This significantly contrasts with typical urban healthcare costs, ensuring that financial barriers do not prevent individuals from receiving necessary medical attention. Dr. Rahman’s decision was deeply influenced by a poignant experience: witnessing the plight of a patient from Bihar who had to travel to Delhi for treatment due to the severe lack of adequate facilities in their native region. This encounter underscored the urgent need for local, high-quality medical services. By establishing this center, Dr. Rahman is not only providing essential care but also significantly contributing to the improvement of public health outcomes and fostering hope among countless underprivileged individuals in rural Bihar. His exemplary action highlights the transformative potential of dedicated medical professionals addressing critical healthcare disparities in underserved regions. LinkedIn LinkedIn News LinkedIn for Learning