Technology in Healthcare

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

India has 150 million+ people above the age 60 and there is a massive opportunity to keep them healthy & fit. But everyone’s focused on Gen Z and no one’s building for their parents. It’s a hard business but a big one. We’ve invested in two companies. Here’s why it’s tough and how one should crack it. Understand the reality first. 1. Elders don’t think of “health” as proactive. They’re conditioned to wait until something breaks before acting. You’re selling a solution to a problem they don’t know they have yet. 2. The 65-year-old needs it but their 35-year-old child pays for it. You're not selling to the elder. You’re selling to their guilt-driven kids in Gurgaon or US. The buyer ≠ the user. 3. Trust is everything and you don’t have it. Indian elders trust: Their doctor, astrologer & their neighbour Not apps. Not tech bros. Not AI. You can't growth hack trust. You earn it slowly, locally. 4. They don’t want new habits. They’ve had the same breakfast for 40 years. You’re not selling a product. You’re undoing decades of routine. 5. Distribution is hyperlocal. Elders don’t click Insta ads. They talk to the uncle in their colony. You scale building by building not by user cohorts. Yes, 150M+ elders. But it’s not one market. It’s a thousand tiny tribes. Different languages, cultures, food habits, family structures, and tech comfort levels. If it were easy, Tata or Reliance would’ve done it already. But it’s wide open now. The one who combines tech + trust + real care will win. So how do you crack it? 1. Think first principles & not trends Don’t build a “senior fitness app.” Ask: Why did they stop moving? What gives them joy? You’re selling independence, not health. 2. Design for peace, not features. One-click help, One daily routine, One trusted face. Great elder products feel like human care not software. 3. Human-first, tech-enable. Don’t replace the daughter. Support her. Train 100 amazing elder coaches. Build tools to help them scale. 4. Don't focus on CAC. Here, it’s about trust per acquisition. You’re not selling toothpaste. You’re asking to be let into their daily life. Start offline. Build trust then tech. 5. You’re in the business of habit change & not selling an app or a pill. Get them to walk 15 minutes a day. Add protein to breakfast. Laugh more. Sleep better. Small wins compound. Don’t build for scale first. Build for consistency. Be in the business of habit change. 6. This isn’t a hackable D2C play. It’s a decade-long trust business. Build for one community. Get to know 100 elders by name. Solve deep, boring problems with elegance. Everyone’s chasing the next billion youth users. But the hidden opportunity lies in serving the first 150 million elders. The elder care market in India isn’t just underserved. It’s misunderstood and needs long-term play. Founders who crack this will build generational companies.

In 2019, a 60-year-old woman walked into a high-end electronics store to buy an iPhone. The salesperson looked at her and said, "Maybe try an Android. It’s easier for someone like you." That one sentence was enough to rile her up. It wasn’t about the phone. It was about how society starts to underestimate you once you cross 60. That moment sparked a fire and a mission. She built GenSxty Tribe, a first-of-its-kind tech platform for the actively ageing 60+ population. Because she believes that 60 is not the end of anything. It’s the beginning of something beautiful. Today, GenS Life helps seniors live independent, joyful, and meaningful lives through: - Live yoga and interactive sessions  - Discounted health checkups with Metropolis Labs - Mental wellness and dementia care - Curated travel packages for older adults - Assistance with everyday tasks like taxis and errands - Adda sessions, doctor talks, and even stand-up comedy - An SOS button for emergency safety - Legal help, estate planning, financial literacy & more And the best part? It doesn’t just help seniors. It also unburdens the sandwich generation, whose in their 30s and 40s caring for both children and parents. For her, this isn't a startup. It's a movement to restore dignity, identity and choice to older adults. She is Meenakshi Menon. She’s building a new story of ageing in India. One filled with freedom, relevance and power. Follow Mohua Chinappa 🎙️ for more such inspiring stories of changemakers #podcastinghost #podcasthost #ageing #ageingpopulation #digitalindia

🏎️💨 How To Design For Aging Population. One billion people aged 60+ live today, and it’s growing faster than any other age group. Key points to consider for more age-inclusive UX ↓ 🚫 Don’t assume that older adults struggle to use digital. ✅ Most users are healthy, active and have a solid income. 🤔 With age, it’s more difficult to focus on close objects. 🤔 Visuals with a similar contrast are harder to tell apart. 🤔 60 years → need 3× more light to perceive same brightness. 🤔 With age, shades of blue/purple, yellow/green look similar. 🤔 Reduced dexterity causes errors with precise movements. ✅ Add UI controls to resize columns, move cards, drag-n-drop. ✅ Always confirm destructive actions, allow to Undo/restore. 🚫 Avoid disappearing messages as toasts: let people close them. ✅ Baseline: large body copy (16px+), color contrast (WCAG AA). ✅ Prefer plain language, large checkboxes, radios (36px+). ✅ Avoid small floating labels and use static field labels. ✅ Show error messages above the text input, not below. 🚫 Don’t rely on accessibility overlays; they are trouble. Accessibility doesn’t have to be dull or boring. It doesn’t come at the cost of oversimplification — it can be bold and passionate, while understanding and respecting the needs of the different audiences it caters to. If anything, it makes boldness more accessible to more people. Conversations about older audiences tend to come with plenty of assumptions and stereotypes — and very often they are simply inaccurate. We overgeneralize and simplify. For example, just like when designing for children, we need to study vast differences in the age groups of 60–65, 65–70 etc. Just like any other group, older users need a reliable, clear product that helps them feel independent and competent. Bring older adults in your design process to find out what their specific needs are. It’s not just better for that specific target audience — good accessibility is better for everyone. And huge kudos to wonderful people contributing to a topic that is often forgotten and overlooked. 👏🏼👏🏽👏🏾 Useful resources: Wise Case Study: Accessible But Never Boring, by Stephanie S. https://lnkd.in/d-hjj_BF Designing For Older Audiences, by Matthew Stephens https://lnkd.in/dAXZ9mp3 Better Microcopy For Older Adults, by Michal Halperin Ben Zvi (PhD.), Kinneret Yifrah https://lnkd.in/evWGFB6u What You Can Learn From Older Adults, by Becca Selah https://lnkd.in/eZdbgRyA Designing Age-Inclusive Products, by Michal Halperin Ben Zvi (PhD.) https://lnkd.in/eQZJwEgS [continues in the comments below ↓] #ux #accessibility

On #WorldHealthDay, it’s worth reflecting on how much the definition of healthcare is expanding. Health outcomes are no longer solely determined by medicine or clinical care. Increasingly, they’re influenced by how effectively we use data, digital technology, and AI to understand disease earlier, make better decisions, and reach patients faster. We’re seeing this shift across the ecosystem – with AI helping identify patterns in complex datasets, more connected systems supporting clinicians with real-time insights, and digital platforms making care more accessible and personalized. The opportunity is fundamentally changing how we prevent, diagnose, and treat disease. But progress isn’t just about technology. It’s about applying these capabilities responsibly, building trust, and ensuring innovation reaches patients everywhere. 

In healthcare, progress only matters when it reaches patients. Digital pathology is helping make that happen. For decades, pathology relied on tissue samples on fragile glass slides and manual workflows. Getting a second opinion could take time and, in some cases, physical samples had to be shipped, with the risk of delay or damage. Digital pathology is changing that. High-resolution imaging and AI can help pathologists see more, faster and with greater precision. They can also help identify patterns beyond what the human eye can detect alone. And once slides are digitised, cases can be shared instantly, anywhere. What inspires me most? How this innovation breaks down barriers. Digital pathology enables collaboration across institutions and borders, helping specialists connect and bringing answers to patients faster. In 2021, Roche launched the Digital Pathology Open Environment, which encourages collaboration among software developers to improve patient outcomes and expand personalised healthcare through innovative image analysis. Today, together with partners such as Dr Dolores Lozano Escario and the team at the Clínica Universidad de Navarra in Pamplona, Spain, we are helping redefine how we study and detect disease. When technology supports human expertise, the impact is real: better insights, greater confidence and ultimately better outcomes for patients.

Today, I'm happy to share a study that I was a part of previously, which just got published; we explored the possibility of predicting relapse in people with depression using smartphone behavioral data. https://lnkd.in/g-_VT6dg This study was particularly interesting because it used passive data, meaning participants didn't need to actively input information, and we relied on the app's existing tracking mechanisms to capture metrics such as step count and device usage. Testing various models, we were able to learn that a combination of activity, activity type, and location was the best way to predict depressive severity ratings and relapse. Ultimately, the value this study demonstrates is the utility of passive behavioral data, which is often unintrusive, constantly being collected, and not particularly sensitive (although the argument could be made for the app tracking your whereabouts). Traditionally, self-report measures can be inaccurate, or people get so busy that they forget to enter their data into mood-tracking apps. But having models that can use automatically collected data without user input allows us to gain more insight into naturalistic patterns of behavior. Moreover, if we're able to predict when an episode of depression occurs through such data, interventions can be introduced before things escalate. I'd argue that even a slightly inaccurate predictive model that overestimates relapse risk can be useful, though the counterargument is that it's not the optimal way to use a tool designed to reduce clinicians' workload while still ensuring the people they support receive quality care. Nevertheless, it's always exciting to see technology used in mental health, and I hope to be part of more projects like this in the future. #mentalhealth #mentalillness #psychology #psychiatry #wellness

Ever wonder why we tend to solve problems the hard way? 🤔 The key is in how we connect the dots. A cancer hospital was facing a major challenge. Patients, often anxious, needed timely care without added delays. Doctors relied on quick access to medical images to make this possible. For most hospitals, loading images within three seconds is the standard. But cancer patients often have extensive imaging records, making this target a significant challenge. This created escalating pressure in an environment that's already stretched to its limits The hospital consulted several firms. They all suggested the same thing: a costly network upgrade that would disrupt daily operations and inconvenience patients even more. The proposed solution was out of the question, the hospital needed something affordable that wouldn’t disrupt patient care. A consulting firm graciously recommended me for the task. I saw the problem from a different angle. IT experts looked at the network. But as a Health Informatician, I focus on using data and technology to design health services that support optimal care delivery. Instead of waiting for doctors to request images, why not load them in advance? By preparing the images during the patient’s wait time, we created a seamless workflow without costly upgrades. The results were immediate and impactful. 😊 The hospital easily met the three-second target, and patients noticed the improvement with shorter wait times. The cost savings were substantial, all without any disruption to care. "Adam, you literally performed magic!” shared the hospital’s clinical operations lead. Sometimes, the simplest solutions make the biggest difference. The key was understanding how health services connect and using technology to support these connections. These days, as a digital health transformation coach, I continue to co-design sustainable, human-centered innovations that improve how information is used to advance health outcomes. Ever found a simple solution to a complex challenge? I’d love to hear your insights and share approaches that make an impact. #HealthcareInnovation #LeadershipLessons #DigitalTransformation

Therabot just passed the world’s first clinical trial for an AI therapy chatbot. Developed by Dartmouth over 5 years, it’s already delivering real, measurable results that could change the face of mental health care. But it wasn’t always this way. When Therabot was first created, its early versions were a disaster: - One chatbot expressed suicidal thoughts. - Another blamed everything on your parents by question five. But the team at Dartmouth didn’t give up — they spent 3 years and 100+ people crafting custom training data from scratch to get it right. And it paid off. The results of the clinical trial? 👉 A 51% reduction in depression symptoms among participants. 👉 People with moderate anxiety saw their symptoms drop to “mild” levels. 👉 Some no longer met diagnostic criteria for anxiety or depression. 👉 And most surprisingly: Participants rated their connection to Therabot as equal to a human therapist. So what matters now? It works. It’s scalable. It’s always available, unlike human therapists who book months out. But if you ask me — I don’t believe it will replace human therapy anytime soon. However, with 70% of people around the world having no access to mental health care, Therabot might be their first real shot at getting the help they need. So the future of therapy won’t be 100% AI. But it could be AI-assisted, AI-augmented, and most importantly, AI-accessible. Because mental health care isn’t broken — it’s just undersupplied. And Therabot might not fix that overnight. But it’s the most science-backed step forward we’ve seen in years. So I’m hopeful. What’s your take on AI therapists? Could they be the solution for those who currently don’t have access to mental health care? #entrepreneurship #healthtech #AI #innovation

𝐒𝐦𝐚𝐥𝐥 𝐝𝐚𝐭𝐚, 𝐛𝐢𝐠 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞. In healthcare, we often celebrate “big data”, but in practice, it’s the right data that changes lives. High-quality, curated datasets power the most trusted #AI models. In radiology, a well-labeled dataset of a few thousand cases can outperform a noisy million. Data governance is therefore becoming as important as image acquisition once was. AI depends not only on the amount of information but on its integrity, diversity, and clinical relevance. As healthcare leaders, our responsibility is to ensure that the data feeding our algorithms represents the people we serve - across age, ethnicity, geography, and condition. Because bias in data is bias in care. Do we focus enough on data quality in healthcare or are we still chasing quantity? #radiology #digitalhealth #artificialintelligence #gehealthcare