Remote Health Monitoring

The future of diagnostics was supposed to be “less invasive.” Nutromics looked at that brief and said, “𝐂𝐨𝐨𝐥. 𝐖𝐡𝐚𝐭 𝐢𝐟 𝐰𝐞 𝐣𝐮𝐬𝐭 𝐫𝐞𝐩𝐥𝐚𝐜𝐞 𝐭𝐡𝐞 𝐥𝐚𝐛?” Their wearable lab-on-a-patch isn’t another fitness gimmick counting steps or guilt-tracking your sleep. This patch uses 𝐃𝐍𝐀-𝐛𝐚𝐬𝐞𝐝 𝐛𝐢𝐨𝐬𝐞𝐧𝐬𝐨𝐫𝐬 to continuously monitor multiple biomarkers... not one token metric to make investors happy. We’re talking ICU-grade monitoring on the skin. Real-time signals for sepsis risk. Dynamic antibiotic dosing. Metabolic markers that normally need vials, tubes, centrifuges and a very patient phlebotomist. The brilliance here is the stack: 1. 𝐃𝐍𝐀 𝐚𝐩𝐭𝐚𝐦𝐞𝐫 𝐬𝐞𝐧𝐬𝐨𝐫𝐬 that bind selectively to target molecules 2. 𝐌𝐢𝐜𝐫𝐨𝐟𝐥𝐮𝐢𝐝𝐢𝐜 𝐜𝐡𝐚𝐧𝐧𝐞𝐥𝐬 that analyse biomarkers without blood draws 3. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐬𝐚𝐦𝐩𝐥𝐢𝐧𝐠 instead of “once every six hours when staff has time” 4. 𝐂𝐥𝐨𝐬𝐞𝐝-𝐥𝐨𝐨𝐩 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐞𝐚𝐦𝐬 that can actually inform clinical decisions, not just dashboards If this works at scale, hospitals won’t just get better data.... they’ll get 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐩𝐡𝐲𝐬𝐢𝐨𝐥𝐨𝐠𝐲, which is the holy grail of critical care. Imagine sepsis caught hours earlier. Imagine antibiotic dosing that reacts to biology, not guesswork. Imagine remote monitoring where the patch becomes the lab. While most wearables are busy telling you your “stress score,” Nutromics is out here quietly rewriting the diagnostic playbook. Deep tech isn’t coming. It’s sticking itself to your skin. #MedTech #DigitalHealth #Wearables #Biosensors #HealthcareInnovation #RemoteMonitoring

🚀 How Digital Biomarkers are Transforming Remote Patient Monitoring 🩺 Digital biomarkers are reshaping healthcare by enabling real-time, data-driven insights into patient health. These measurable signals—captured via digital tools—empower personalized and proactive care, especially for chronic and complex conditions. --- What Are Digital Biomarkers? 🧬 Digital biomarkers are objective, quantifiable data points collected from devices like wearables, smartphones, and sensors. They monitor: - Heart Rate Variability (HRV) 🫀: Tracks cardiac health and stress. - Activity Levels 🏃: Monitors steps, exercise, and recovery. - Sleep Patterns 😴: Evaluates sleep quality and disturbances. - Blood Glucose 🩸: Tracks diabetes through continuous glucose monitors. - Oxygen Saturation (SpO2) 🌬️: Assesses respiratory or circulatory health. - Speech & Gait Analysis 🗣️🚶: Detects neurological changes in conditions like Parkinson’s or Alzheimer’s. --- How Digital Biomarkers Are Used in RPM 📡 Digital biomarkers provide personalized care by monitoring patients in real-world settings. Examples include: 1. Cardiology ❤️: - Wearables detect arrhythmias and track HRV to predict cardiac events. 2. Diabetes Management 🍭: - Continuous glucose monitors optimize insulin therapy by providing real-time glucose readings. 3. Neurological Disorders 🧠: - Smartphones analyze speech and gait to monitor Parkinson’s progression or stroke recovery. 4. Pulmonary Conditions 🌬️: - Devices measuring oxygen saturation and respiration detect COPD exacerbations or sleep apnea. 5. Mental Health 🧘: - Apps track voice tone, typing patterns, and screen time for depression or anxiety indicators. --- Evidence Backing the Impact 🔬 - Cardiac Monitoring: A European Heart Journal study showed wearables can reliably track heart failure progression, correlating strongly with patient-reported outcomes. (https://lnkd.in/eNwZt7mv) - Neurological Insights: RADAR-base uses longitudinal digital biomarkers for epilepsy, depression, and MS monitoring, revealing new care pathways. (https://lnkd.in/efN5wWWk)) --- Why Digital Biomarkers Matter 🌟 1. Early Detection 🚨: Identifies health issues before symptoms escalate. 2. Personalized Treatment 🎯: Adjusts care plans using real-time data. 3. Patient Empowerment 🤝: Engages patients in their health management. 4. Cost Efficiency 💰: Reduces emergency visits and hospitalizations. --- Challenges to Overcome ⚖️ Despite their potential, digital biomarkers face hurdles: - Data Privacy 🔒: Ensuring secure handling of sensitive data. - Interoperability 🔗: Integrating data seamlessly into healthcare systems. - Adherence 📋: Encouraging consistent device use by patients.

🎯🎯 Empowering Health: Innovations in Wearable Health Tech 🎯🎯 Wearable health technology is transforming the way we monitor and manage our health, especially for those with chronic conditions. These innovative devices are making it easier for patients to stay on top of their health, providing real-time data and actionable insights. Here’s how wearable health tech is revolutionizing patient care: 1. Continuous Health Monitoring:   🟢 Real-Time Data: Wearable devices like smartwatches and fitness trackers continuously monitor vital signs such as heart rate, blood pressure, and oxygen levels, providing real-time health data.   🟢 Early Detection: By detecting abnormalities early, these devices can alert users to potential health issues before they become critical, enabling timely medical intervention. 2. Chronic Condition Management:   🔴Diabetes Management: Wearable glucose monitors help diabetic patients keep track of their blood sugar levels throughout the day, making it easier to manage their condition and avoid complications.   🔴 Cardiac Care: Heart rate monitors and ECG-enabled devices provide detailed cardiac data, helping patients with heart conditions monitor their heart health and share data with their healthcare providers. 3. Enhanced Patient Engagement:   🔵 Personalized Insights: Wearable tech offers personalized health insights based on the user’s data, encouraging healthier lifestyle choices and better disease management.   🔵 User-Friendly Interfaces: These devices are designed with user-friendly interfaces, making it easy for patients of all ages to understand and use the technology effectively. 4. Integration with Healthcare Systems:   ⭕ Seamless Data Sharing: Wearable devices can seamlessly share data with healthcare providers, ensuring that doctors have up-to-date information to make informed decisions about patient care.  ⭕ Remote Monitoring: Healthcare professionals can remotely monitor patients’ health, reducing the need for frequent in-person visits and allowing for continuous care. 5. Innovations on the Horizon:   🔘 Advanced Sensors: The development of advanced sensors is expanding the range of health metrics that wearables can track, from hydration levels to respiratory rate.   🔘 AI Integration: Artificial intelligence is being integrated into wearable tech to provide more accurate predictions and personalized health recommendations. Wearable health tech is empowering patients to take control of their health like never before. By providing continuous monitoring, personalized insights, and seamless integration with healthcare systems, these innovations are enhancing patient care and improving outcomes. #WearableHealthTech #HealthcareInnovation #ChronicConditionManagement #DigitalHealth #PatientCare #HealthTech #FutureOfHealthcare #SmartWearables #RemoteMonitoring #PersonalizedHealth

7 wearable and sensor innovations pushing health beyond “wellness” tracking this month: 🔘 Sibel Health is developing an AI-enabled wearable that tracks scratching behaviour in people with atopic dermatitis, turning something usually seen as a subjective symptom into a measurable clinical signal that could also support drug development. 🔘 CranioSense is working on a non-invasive approach to measuring intracranial pressure, which today often requires invasive procedures, and if validated could make brain pressure monitoring safer and more continuous in routine clinical care. 🔘 University of Technology Sydney researchers are developing AI-powered sweat sensors that can decode body chemistry in real time, tracking hormones, medication levels and potential early warning signs of disease, potentially offering a non-invasive alternative to some forms of blood testing 🔘 ŌURA rings are being used within Medicare Advantage Plans, with around one-third of eligible members opting in and sharing biometric data, which is already leading to improvements in sleep and light activity and is paving the way for deeper clinical use cases such as hypertension monitoring 🔘 Samsung Electronics is preparing to launch an AI Brain Health tool that uses data from smartphones and wearables, including speech, movement and sleep behaviour, to help detect early signs of dementia while aiming to keep the experience privacy-aware and clinically relevant 🔘 Researchers at the University of Arizona have created a wearable mesh sleeve that monitors gait and subtle movement patterns to identify early signs of frailty in older adults, with the goal of shifting care from reacting after a fall to proactively supporting prevention through continuous remote monitoring 🔘 And China is testing “smart urinals” that analyse urine in real time for markers like glucose and protein, which opens up interesting conversations about passive health screening, consent, and how health data might be gathered in everyday environments. 💬We are steadily moving from episodic health snapshots to passive, continuous and contextual signals across movement, sleep, behaviour and even body chemistry. The technology is getting closer. Now the real work is around validation, governance, reimbursement and making sure the data actually makes a difference in peoples lives 👇 Links to articles in comments #DigitalHealth #Wearables #AI

This paper explores the transformative impact of wearables and AI on healthcare workflows and patient care, focusing on enhanced efficiency, personalization, and cost-effectiveness. 1️⃣ IoMT (Internet of Medical Things) market is rapidly growing, projected to increase from $50.3 billion in 2020 to $135.87 billion by 2025, highlighting a significant shift toward digital health adoption. 2️⃣ Wearables have diverse applications, monitoring both biological factors (e.g., saliva, sweat) and utility-based measurements (e.g., smart fabrics, implants) to enhance patient data collection. 3️⃣ Real-time monitoring through wearables and AI supports early disease detection and continuous tracking, facilitating better treatment adherence and fewer hospital visits. 4️⃣ Patient interest in remote monitoring is strong, with 79% willing to use mobile ECG tools, and 74% feeling safer with constant monitoring, demonstrating growing acceptance of self-managed care. 5️⃣ AI-assisted monitoring with wearable sensors achieves high accuracy, including 97% accuracy in detecting atrial fibrillation, outperforming traditional methods. 6️⃣ AI models like deep learning and neural networks enable predictive diagnostics and personalized treatments, demonstrating 80% accuracy for heart disease, 80% for blood infections, and 94% for cancer detection. 7️⃣ Integration challenges include data management, EHR integration, privacy, bias, and transparency, all of which must be addressed to foster trust among healthcare providers and patients. 8️⃣ Automation potential is significant, with AI transforming tasks like medical billing, coding, and lab workflows, reducing errors and freeing up resources for patient care. 9️⃣ Future healthcare will increasingly depend on AI and wearables, reshaping patient management, especially for aging populations, and enabling personalized, real-time care delivery. 🔟 AI and wearables promise a comprehensive transformation of healthcare, enhancing efficiency, personalizing treatments, and reducing costs while overcoming obstacles to data integration and physician-patient trust. ✍🏻 Perry LaBoone, PE, CPA, PMP, Oge Marques. Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology. International Journal of Information Management Data Insights. 2024. DOI: 10.1016/j.jjimei.2024.100294

Remote monitoring is often touted as a way to improve patient care and increase revenue, but does the data support that claim? Our new paper in Health Affairs, led by Mitchell Tang and with co-authors Felippe Marcondes, and Ateev Mehrotra, investigates this question using a 100% sample (!) of U.S. Medicare claims data. What we find: 1. RPM reimbursement directed substantial funds to adopting practices. On average, these practices saw 20% higher Medicare revenues, driven both by direct RPM reimbursement and increased care management and outpatient visit volumes. 2. Contrary to concerns that the added time and resources to provide RPM to some patients might compromise access for others, we found that RPM practices cared for more patients overall - especially those with higher disease burdens who were often non-White and dually eligible for Medicare and Medicaid.    The takeaway: RPM can make chronic disease management more accessible and patient-centered, but it also carries real cost implications if adopted widely and injudiciously. These tensions are shaping payer policies today. In sharp contrast to Traditional Medicare's current broad coverage, UnitedHealthcare recently announced plans to dramatically limit RPM coverage in 2026, including the complete exclusion of the two most commonly monitored conditions: primary hypertension and diabetes. (Mario Aguilar covered this thoughtfully last week in STAT!) We believe there is a better balance to be struck. In this and prior work (Annals of Internal Medicine) we call for smart guard rails that promote high-value RPM, such as focusing on patients with poorly controlled conditions and time-limited monitoring (e.g., 6 months). AND: For those attending Frontiers Health, I will be giving a talk during this afternoon’s Digital Health Policy Summit on “The Power of Data: Building the Evidence Base for Policy and Innovation with Real-World Evidence” (4:15pm) , during which I’ll talk more about this and other research. Then I'll sit down for a fireside chat with Alberta Spreafico, PhD, MBA and Nick Schneider to talk about making better evidence-based digital health policy! Peterson Health Technology Institute (PHTI) Hasso Plattner Institute Link to paper: https://lnkd.in/dpWrBe4N Link to STAT article: https://lnkd.in/dRGcMUgJ

UnitedHealthcare recently announced their decision to restrict coverage for RPM. I wanted to share my thoughts on the decision and a path forward. The underlying rationale for RPM coverage by CMS was straightforward: remote capture of physiological data, combined with clinical oversight and timely intervention, can prevent deterioration, reduce avoidable utilization, and improve patient experience among patients with chronic conditions or those recovering from recent hospitalizations. That rationale has not changed. But with any powerful tool comes responsibility. RPM has, at times, been adopted inconsistently and without the clinical rigor it warrants. Variation in practice, patient selection, and program integrity is real, and guardrails are appropriate, even necessary. We should welcome higher standards that ensure RPM is deployed thoughtfully and delivers measurable value. What is harder to reconcile is the implication that RPM has "no proven role" in conditions such as hypertension, diabetes, COPD, etc. The peer-reviewed evidence says otherwise. Multiple high quality and real-world studies have shown meaningful improvements in blood pressure control, glycemic management, COPD symptom stability, and more. Uncontrolled hypertension and poorly managed diabetes can lead to heart attacks, stroke, kidney failure, heart failure, and hundreds of millions in avoidable healthcare spending each year. To argue that there is no role for remote monitoring and timely clinical intervention for these patients is misaligned with both science and lived clinical experience. At the same time, it is clear that the original model of RPM is no longer sufficient. Simply collecting data without timely medical intervention or verification of medical necessity will not produce measurable benefit. It is time for the industry to evolve toward “RPM 3.0”: remote patient management, where data and insights are paired with medical decision-making, medication management, personalized education, and proactive care pathways. That is the model capable of delivering predictable clinical and economic outcomes. The path forward is not to eliminate RPM, but to elevate it. Stronger accountability, clearer guardrails, and thoughtful patient stratification can ensure this modality is used responsibly and sustainably. The goal is better care, not less care. And RPM done right, as remote patient management, remains an impactful tool for proactive, personalized chronic care. I hope UnitedHealthcare is open to a dialogue with providers, researchers, health economists, policymakers, and patient advocates to further refine the role of RPM and align on success metrics to proactively measure its clinical and economic benefit across patient populations and clinical conditions. Ateev Mehrotra William Gordon Ariel Dora Stern Margaret-Mary Wilson MD, MBA, MRCP, FNMCP Mehmet Oz Chris Klomp American Heart Association American Diabetes Association COPD Foundation Fierce Healthcare

🏥 Two #wearable companies. Combined valuation: over $20 billion. And we're just getting started. WHOOP has just raised $575 million in a Series G round at a $10.1 billion valuation. What is especially notable is not only the size of the round, but the signal behind it: investors include Abbott and Mayo Clinic. That suggests wearables are increasingly being seen not merely as consumer wellness products, but as strategically relevant assets in the future of healthcare. Meanwhile, ŌURA has been reported at roughly an $11 billion valuation, reinforcing the scale of market confidence in continuous, consumer-facing health monitoring. What makes this shift important is not just the hardware. It is the growing clinical relevance of continuous, real-world data. Recent literature shows that wearable technologies are moving beyond lifestyle tracking into more serious remote monitoring use cases. A new Nature Portfolio study demonstrated that #smartwatch-based monitoring can support the remote assessment of heart failure patients using continuous physiologic and behavioral data. A JMIR mHealth and uHealth systematic review further showed that wearables are increasingly used for chronic disease monitoring, especially in cardiovascular and neurological applications. At the same time, the real acceleration comes from analytics. As #AI-enabled interpretation improves, wearable data is becoming more actionable: not just raw signals, but contextualized information about recovery, stress, rhythm, activity, and deterioration risk. A JMIR systematic review on AI-enabled medical devices highlights wearable monitoring as one of the domains where AI is enabling more continuous, #personalized health management. This is why wearables are becoming strategically relevant beyond consumer tech. They are helping to push healthcare away from a model that mainly reacts to illness, and toward one that increasingly supports prevention, early detection, and continuous management. A recent European Heart Journal – Digital Health review describes wearable technologies as part of a transformation in cardiovascular care through continuous monitoring outside traditional clinical settings, while also making clear that large-scale impact still depends on validation, workflow integration, and governance. For those of us working in healthcare IT, the key question is no longer whether wearable-generated data will matter. The real question is: Are our health IT systems ready to receive, contextualize, and operationalize this data? #DigitalHealth #Wearables #RemotePatientMonitoring #PreventiveCare #AIinHealthcare #HealthcareIT #Interoperability #DigitalTransformation #Virgobit

AI in healthcare is changing where care happens, making virtual clinics and remote monitoring part of daily medicine. A new peer-reviewed review in Artificial Intelligence in Medicine (Oct 17, 2025) maps how remote care is changing right now. With the right design, AI can: ✅ Triage symptoms so urgent cases see clinicians faster ✅ Turn wearables and home sensors into early-warning signals ✅ Keep patients engaged with tailored reminders and check-ins Where it helps most: ✔️ Faster access for rural and hard-to-reach patients ✔️ Safer chronic-disease management from home ✔️ Lower burden on clinics for routine monitoring What still needs leadership: ◾ Privacy and security for continuous health data ◾ Digital access and literacy so no one is left out ◾ Bias checks so models work for every population ◾ Clear human oversight for complex or sensitive cases The study shows that remote AI succeeds only when technology amplifies clinical judgment instead of trying to replace it. Pair speed with safety, convenience with consent, and automation with accountability. 🔁 Save this for your next virtual-care roadmap. 🔔 Follow Rizwan Tufail for evidence-based playbooks on AI, remote care, and clinical governance.

Using AI, your phone or smartwatch may detect Parkinson’s or Alzheimer’s years before a clinical diagnosis. ⌚️🩺 In our new Nature Reviews Bioengineering paper, we examine how digital biomarkers from everyday technologies can measure brain health continuously rather than only during clinic visits. Paper Link 🔗: https://lnkd.in/gwnjvPkE Paper PDF 🗒️: https://rdcu.be/ffDk2 These signals already capture changes in movement, behavior, physiology, and environment. Parkinson’s stands out because its motor symptoms are directly measurable with everyday sensors, which has accelerated validation. The harder problem is validation, since clinical standards are defined in controlled settings while these signals are collected in daily life. Even relatively simple measures have taken years to reach clinical acceptance. Regulation is starting to adapt, with emerging validation and reporting frameworks, but it is still early. The bigger shift is economic. Reimbursement is moving toward remote and continuous monitoring through RPM and RTM pathways (e.g., CPT 99453/99454/99457; 98975–98977, 98980), but these are still structured around episodic care. This leaves an open question of how continuous, longitudinal signals fit into billing structures defined by time and discrete interactions. While DBMs can greatly expand brain health access to underserved populations, they must be validated across diverse populations and should not assume everyone has the same technologies, connectivity, or digital literacy. What is getting really exciting is the combination of new sensing and more personalized models. Smart fabrics, BCIs, molecular signals from sweat and breath, and miniaturized implantables are beginning to integrate with models that learn each individual’s baseline. This makes it possible to track how the disease evolves at the level of the individual. Grateful to an incredible set of collaborators who made this work possible across Stanford University School of Engineering, Stanford University School of Medicine, Stanford Institute for Human-Centered Artificial Intelligence (HAI), Wu Tsai Neurosciences Institute, & Stanford Knight Initiative for Brain Resilience: My advisor Prof. Ehsan Adeli, Dr. Narayan Schutz, Prof. Qingyu Zhao, Prof. Christine Gould, PhD, ABPP, Prof. Arnold Milstein, Prof. Kevin Schulman, Prof. Victor Henderson, Prof. James Landay, Prof. Fei-Fei Li, and Prof. Feng Vankee Lin. #DigitalHealth #DigitalBiomarkers #NeurodegenerativeDiseases #Neurotech #AIinHealthcare #PrecisionMedicine #HealthcareInnovation