How to Prioritize Early Detection in Healthcare

𝐓𝐞𝐥𝐞𝐡𝐞𝐚𝐥𝐭𝐡 + 𝐇𝐨𝐦𝐞 𝐂𝐚𝐫𝐞 + 𝐑𝐞𝐦𝐨𝐭𝐞 𝐏𝐚𝐭𝐢𝐞𝐧𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 (𝐑𝐏𝐌) + 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐡𝐞𝐫𝐚𝐩𝐞𝐮𝐭𝐢𝐜𝐬 (𝐃𝐓𝐱) 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐂𝐚𝐫𝐝𝐢𝐨𝐯𝐚𝐬𝐜𝐮𝐥𝐚𝐫 𝐇𝐞𝐚𝐥𝐭𝐡 represents a forward-thinking, patient-centric approach that tackles the multifaceted nature of chronic heart conditions. 1. Personalized and Continuous Care DTx and RPM Integration: DTx provide personalized, evidence-based interventions tailored to each patient’s unique needs. Simultaneously, RPM devices offer continuous monitoring of vital cardiovascular metrics such as ECG, blood pressure, and heart rate. The integration of these technologies ensures that care plans are dynamically adjusted in real-time based on continuous patient data, providing a truly individualized treatment approach. Telehealth and Home Care Synergy: Telehealth facilitates remote consultations and follow-ups, making it easier for patients to receive consistent medical guidance without leaving their homes. Home care services complement this by offering in-person support, including medication management, physical therapy, and regular health assessments. 2. Enhanced Patient Engagement and Adherence Patient Empowerment: The use of intuitive RPM devices and accessible telehealth platforms empowers patients to actively participate in their care. With real-time feedback and educational resources readily available, patients are more likely to adhere to their treatment plans and make informed lifestyle choices. 3. Early Detection and Timely Intervention Proactive Management: The combination of real-time data from RPM devices and AI-driven insights from DTx platforms enables healthcare providers to detect early signs of deterioration in a patient’s condition. This allows for timely interventions that can prevent escalation, reduce the risk of severe cardiovascular events, and potentially avoid hospitalizations. Virtual Acute Care: Telehealth platforms offer rapid escalation of care when necessary, connecting patients with specialists or emergency services in response to alarming data detected by RPM. This ensures that urgent medical needs are addressed promptly, reducing the risk of complications. 4. Cost-Effectiveness and Accessibility: Reducing Healthcare Costs: By reducing the need for frequent in-person visits and minimizing hospitalizations, this integrated care model helps lower overall healthcare costs. Shifting care from costly hospital settings to more efficient home environments not only reduces expenses but also optimizes the use of healthcare resources. Expanding Access: This model is especially advantageous for patients in remote or underserved areas, providing them with access to specialized cardiovascular care that might otherwise be unavailable. Telemedicine and RPM bridge geographical gaps, ensuring that high-quality care is accessible to all. #DigitalTherapeutics #RemotePatientMonitoring #Telehealth #Cardiovascular

We Have the 🛠️ Tools. The Potential 💡 Is Clear. Let’s Rethink ❤️🩹Cardiovascular Care ❤️🩹at Scale. A compelling review by Aline Pedroso, PhD and Rohan Khera in Nature Portfolio’s Cardiovascular Health. Great outline on how AI-powered wearables, PPG/ECG sensors, point-of-care ultrasound, and edge-AI models can and are transforming cardiovascular care—extending reach, reducing friction, and bringing precision to the front lines. 👉 Article: https://lnkd.in/eCNVj8_F Why this matters: ✅Community-based detection of arrhythmias and structural heart disease is feasible now. ✅Multimodal sensor + AI fusion improves prediction, risk stratification, and monitoring. ✅Cloud and edge tech enable privacy-preserving integration into clinical workflows. ✅Tools like AI-guided echocardiograms with GE HealthCare’s Caption Guidance (FDA-cleared for use by any medical professional) allow earlier, scalable echo screenings—no sonographer required. ✅These shifts are especially powerful in under-resourced or preventive care settings. Call to action for Health Systems, Payers, MedTech and Innovators: 1️⃣ Advance interoperability—connect consumer and bedside data with clinician workflows. 2️⃣ Fund pragmatic RCTs to validate outcomes, not just signal accuracy. 3️⃣ Build reimbursement models that reward early detection and smarter triage. 4️⃣ Design inclusively—this must close gaps, not widen them. 💡 We’re past proof of concept and evolve the platform. Time to implement boldly, equitably, and at scale. #DigitalHealth #AIinHealthcare #CardiovascularCare #HealthEquity #Wearables

“If you catch it early, and treat it early, you can end up with many more years of healthy life expectancy.” Heart disease is the leading cause of death in the United States. Yet half or more of adults with hypertension do not have their blood pressure under control (depending on the measure used). These new recommendations are a positive step, but they alone cannot solve the problem: that the U.S. healthcare system is not designed for early access. Multiple barriers stand in the way of Americans catching and treating their hypertension early: the time and cost it takes to reach the nearest provider, copays and deductibles that push patients to stay home and wait. Kaiser Northern California achieved a hypertension control rate of 87% by 2011, to my knowledge the best in the nation. They laid out a solution to this problem more than a decade ago in their 2013 study published in JAMA. They made 4 changes to solve the problem: 1) They removed copays 2) They proactively searched for patients with elevated blood pressure, and brought them in. 3) They relied on teams, not just doctors. Patients had blood pressure visits with medical assistants, and escalated treatment using a set algorithm. That meant no long wait times for doctors between visits. Each visit above goal, the patient took a step up the treatment ladder. 4) They proactively brought patients back in every 2 weeks until they hit and maintained their blood pressure goal. https://lnkd.in/gt5A3DU9 Improving heart disease outcomes in America will depend on design: building a different kind of health care that reaches patients early, and often. https://lnkd.in/gt5A3DU9

Bringing Hope on Wheels: The Power of Mobile Lung Cancer Screening Lung cancer is often silent in its early stages—by the time symptoms appear, it can be too late. But with early detection, lives can be saved. That’s where mobile lung cancer screening comes in. Imagine a clinic on wheels, equipped with low-dose CT technology, traveling to communities where healthcare access is limited. These mobile units bring life-saving screening directly to patients—no long commutes, no need to take time off work, and no barriers to care. Here’s how mobile lung screening makes a difference: • Increases access in rural, underserved, and urban communities • Detects cancer earlier, often before symptoms appear • Improves outcomes with more patients eligible for curative treatments • Reduces disparities in cancer detection and care • Builds trust by meeting people where they are Know someone at risk? Help us spread the word. Early detection saves lives. #LungCancerAwareness #MobileHealth #EarlyDetection #LungScreening #CommunityHealth #Pulmonology #SavingLivesOnTheGo

Today, on World Cancer Day, we recognize the profound impact cancer has on individuals and families worldwide. My father had stage IIIB adenocarcinoma of the lung, with his left upper lobe removed, and my uncle succumbed to small cell lung cancer. Both were non-smokers. These stories underscore the urgency of advancing our detection methods. It's a personal mission for many, driven by the hope that through technology, particularly the fusion of Knowledge AI and Big Data AI, we can unveil these silent killers early enough to make a difference. Here's a proposed 10-step protocol for deploying an algorithm capable of early detection of solitary lung nodule cancer, leveraging blood biomarkers, radiology, and other modalities: Data Collection and Integration: Gather extensive datasets covering various patient demographics and stages of lung cancers. Big Data Infrastructure: Develop efficient data handling for structured and unstructured data. Knowledge AI Models: Utilize medical knowledge to enhance AI models. Machine Learning and Deep Learning: Apply AI techniques for identifying early-stage cancer patterns. Radiology Image Analysis: Train AI for advanced image recognition of lung scans. Blood Biomarker Detection: Develop algorithms for non-invasive blood test analysis. Predictive Modeling: Personalize risk assessments using predictive models. Clinical Validation: Ensure model accuracy through extensive clinical trials. Integration into Clinical Workflows: Collaborate with healthcare providers to incorporate AI into existing processes. Continuous Learning and Improvement: Establish a system for regular AI model updates based on new data and discoveries. By following these steps, we can harness AI's power to transform early lung cancer detection, potentially saving countless lives. The fusion of Knowledge AI and Big Data AI offers hope, turning silent stories into beacons of progress. Through early detection, we aspire to beat cancer.

Can AI Help Catch Lung Cancer Earlier - and Save Lives and Costs? That’s exactly what the Budget Impact analysis showed in Vietnam. What did the analysis find? - Over 5 years, using AI can help detect an additional 3,155 patients - Early detection means better chances of treatment, like surgery - As a result, the model predicts it could prevent 4,742 premature deaths over a 5 year horizon What about the cost? - Over 5 years, the AI system is expected to be cost-neutral, and possibly even save money, especially if advanced therapies are included - In the first year, costs go up because more patients are diagnosed and treated earlier - But after that, costs go down, because fewer patients need expensive late-stage cancer treatment - AI aided IPN detection requires an initial investment but leads to cost savings in subsequent years Why this matters? Early detection means more lives saved, fewer families negatively impacted, and less strain on hospitals. It’s a rare win-win: better care for patients, and smarter spending for healthcare systems. This analysis shows that AI can help health systems act earlier, treat better, and do more with less. Our chest X-ray AI – qXR is used to read chest X-rays and detect lung nodules early - which are often the first signs of lung cancer. Normally, these small nodules go unnoticed unless a patient has symptoms. But qXR helps find them incidentally, even when people are getting an X-ray for something else. If you’re building a smarter, earlier cancer detection program - this is what the future looks like! Read more here: https://lnkd.in/dy7aQ6BK AstraZeneca Deniz Koksal Dr.Arunkumar Govindarajan Hari Kishan Gonuguntla Mohamed Helmy Zidan Ti Hwei How Nick Passey潘尼克 Susan Mccutcheon Pei-Chieh Fong Ashwini Saha Bruna Dirodi, MSc Viraj Rajadhyaksha Sabine Luciani Nipun Jain Qure.ai Saniya Pawar Prashant Warier Samir Shah, MD, MMM FACR Pradeep Kumar Jim Mercadante Georgina Wright Preetham Putha Nate Hunter Rohitashva Agrawal, Ranjana Devi Shamie Kumar 🇬🇧 🇦🇪 #budgetimpact #healtheconomics #lungcancer #realworldevidence #healthcare #cancercare