How Technology is Transforming Value-Based Care

One of the most exciting shifts happening in healthcare today isn't just about detecting chronic diseases earlier—it's about empowering primary care physicians to act on that information in real time. This week, our Counterpart Health subsidiary released new results showing that having a relationship with a primary care physician that uses Counterpart Assistant is associated with meaningfully better outcomes for patients with congestive heart failure (CHF) enrolled in Clover Health’s Medicare Advantage plans:  - 18% lower all-cause hospitalizations - 25% lower 30-day readmissions At first glance, these stats are impressive on their own. CHF is a leading cause of hospitalizations among seniors, and interventions that move the needle even slightly are rare. But the bigger story here is what these results say about the future of healthcare: technology that works with physicians—rather than burdening them—is how we bend both the quality and cost curves. Most healthcare technology has asked physicians to do more: more documentation, more box-checking, more clicks. We've taken a different approach. Counterpart Assistant delivers clinical-grade insights directly into the physician workflow—designed not to add burden, but to augment their decision-making and make high-quality care easier to deliver, not harder. This impact compounds over time. It isn’t just about identifying one disease a little earlier or generating one better HEDIS score. It’s about embedding intelligence into the day-to-day fabric of primary care, so that complex, high-burden diseases like heart failure can be managed more proactively, more thoughtfully, and ultimately with fewer hospitalizations and better patient lives. We believe this kind of physician enablement isn’t optional if value-based care is going to succeed at scale—it’s foundational. And as our latest report continues to show, when you align technology, physician experience, and patient outcomes the right way, you don’t have to choose between better quality and lower cost. You can achieve both. #CloverHealth #HealthcareInnovation #ClinicalAI #PrimaryCare #MedicareAdvantage #ValueBasedCare #CongestiveHeartFailure

NewHealthcare Platforms just released the first pillar of our revolutionary six-pillar VBMT model, and it exposes a $50 billion failure in diabetes technology. Despite more clinical evidence and investment than any other medical condition, most diabetes MedTech delivers minimal real-world value. Continuous glucose monitors, insulin pens, and diabetes apps consistently fail to achieve promised outcomes not because the technology doesn't work, but because we've ignored the human element entirely. Our VBMT-P1 (Value-Based Medical Technology - Pillar 1) framework transforms this paradigm by making patient and consumer engagement the foundation of platform design rather than an afterthought. While traditional approaches focus on device sophistication and hope engagement emerges naturally, VBMT-P1 systematically addresses cognitive, emotional, and behavioral engagement as core platform requirements. Today's newsletter demonstrates how VBMT-P1 transforms diabetes technology outcomes through evidence-based engagement strategies, personalized onboarding systems, and continuous optimization based on real-world usage patterns. This isn't about better sensors or smarter algorithms—it's about building platforms that create sustained behavior change. This is just the beginning. Our six-pillar VBMT model addresses every critical success factor for value-based MedTech, with five more pillars coming in future newsletters.   ___________________________________________ Sam Basta, MD, MMM is a pioneer of Value-Based Medical Technology and LinkedIn Top Voice. As Chief Medical Officer or Strategic Advisor, he guides healthcare startups to translate clinical and technological innovation into business success. With two decades of executive experience, he specializes in working with AI and medical technology companies through critical strategic decisions, product and business development, customer engagement, and venture fundraising. His unique perspective at the intersection of clinical excellence, cutting-edge technology, and value-based care creates compelling value propositions for investors, partners, and customers. A recognized thought leader, his insights shared in posts and a weekly newsletter reach over 25,000 healthcare executives and professionals. #healthcareonlinkedin #artificialintelligence #ai #valuebasedcare #healthcare

The future of value-based care depends on how we manage data—period. I just published a deep-dive on what it really takes to build a Multi-Source Data Warehouse for Risk-Bearing Provider Groups and Payors—the kind that doesn't break when complexity scales. This isn't theory. It's built on real-world lessons from the trenches at Incuvio Health Inc —where we’ve turned data chaos into clarity for organizations managing tens of thousands of Medicare Advantage lives. Eligibility, capitation, claims, labs, HIEs, pharmacy, supplemental data—if you're not stitching it all together in real-time, you're leaving money, compliance, and patient outcomes on the table. I break down the 7 core building blocks, the architectural blueprint, and the common mistakes to avoid. 👊 This is for operators who are serious about transforming their data strategy from a bottleneck into a competitive advantage. 👉 Read it. Share it. Build better. #HealthcareData #RiskAdjustment #ValueBasedCare #DataWarehouse #PopulationHealth #IncuvioHealth #HealthcareInnovation

As I work at the intersection of healthcare design and technology, certain patterns are emerging that suggest profound changes in how we'll deliver care. Here are five shifts I believe we'll see: First, AI won't just assist with decisions - it will transform how we make them. Providers will move from reviewing individual data points to understanding complex patterns across time and populations. Imagine specialists across disciplines having the time and insight to truly collaborate on complex cases: an oncologist and cardiologist deeply discussing treatment implications, supported by AI-surfaced patterns from thousands of similar cases. These rich, cross-disciplinary conversations will lead to more nuanced, coordinated care decisions. Second, as AI manages standard protocols and data analysis, provider time will shift dramatically. Instead of spending hours on documentation and routine analysis, clinicians will focus on the nuanced work of understanding patient contexts and goals. Conversations will deepen. Treatment plans will become more personalized. The human elements of care - understanding individual values, circumstances, and preferences - will take center stage. Third, care delivery will become more proactive and precise. AI will help identify subtle signs of health changes before they become critical, enabling earlier interventions. Care teams will shift from reactive response to proactive planning. Preventive care will become more targeted and effective, based on sophisticated understanding of individual risk factors and social determinants of health. Fourth, the technology itself will continuously evolve based on real-world outcomes. Treatment protocols will adapt in real time based on emerging evidence and individual patient responses. Care pathways will become more dynamic and personalized, learning from each patient interaction to improve future care delivery. Finally, these changes will reshape the physical and operational structure of healthcare. We'll need different kinds of spaces - ones designed for deeper conversations and collaborative decision-making. Workflow patterns will change as routine tasks become automated. Team structures will evolve to support more integrated, proactive care delivery. The future of healthcare delivery will require fundamentally rethinking how we provide care when technology can handle routine tasks and help us see patterns we might otherwise miss. This transformation offers an unprecedented opportunity to make healthcare more human, more proactive, and more effective.

90% of health tech startups fail. CMS has set a goal to have 100% of Traditional Medicare beneficiaries in accountable care relationships by 2030. The opportunity is massive: $4.9 trillion in annual healthcare spending shifting toward outcomes-based reimbursement. Most health tech companies are building for a market they don't actually understand. I've spent 15+ years in healthcare technology and value-based care transformations, and I see the same fatal mistakes repeatedly: ❌ Building products without understanding the buyer journey. VBC purchasing involves clinical, technical, AND financial stakeholders. Miss one, lose the deal. ❌ Underestimating regulatory complexity. What works in fee-for-service doesn't automatically translate to risk-based contracts. ❌ Ignoring the data infrastructure reality. Most health systems are still operating with fragmented, low-fidelity patient data. Your AI solution is only as good as the data feeding it. The uncomfortable reality? You can't just pivot to VBC when the market demands it. The buying cycles are 18+ months. The integration requirements are complex. The compliance standards are non-negotiable. What successful health tech companies are doing RIGHT NOW: ✅ Mapping their product value to VBC metrics (HEDIS, Star Ratings, TCO reduction) ✅ Building for interoperability from day one (FHIR, HL7, payer integration) ✅ Understanding the stakeholder matrix (who influences, who decides, who pays) ✅ Designing for population health outcomes, not just individual patient care The window is closing fast. By 2026, early VBC adopters will have significant competitive advantages. By 2030, it won't be a differentiator—it'll be table stakes. Question for health tech founders: Is your GTM strategy actually built for the VBC reality your buyers are facing? #valuebasedcare #healthcare #healthtech

The transformation of American healthcare is in full swing, with health plans at the forefront of this shift from fee-for-service to value-based care (VBC). As this model evolves, it shows immense potential to improve patient outcomes while controlling costs. But where do we stand today, and what actions are necessary to ensure its continued success? Key Progress to Date ✅ Over 60% of Healthcare Payments Are Value-Based: As of 2023, more than 60% of healthcare payments are tied to value-based models like capitation, shared savings, and bundled payments. This shift is driving a focus on quality, prevention, and cost-efficiency. 📊 Data-Driven Care for Better Outcomes: Health plans are leveraging AI, predictive analytics, and real-time data to identify high-risk patients and intervene early. Predictive analytics alone has been shown to reduce hospital readmissions by up to 30%, demonstrating its critical role in improving care delivery. 💡 Whole-Person Health Approach: Addressing social determinants of health (SDOH) is crucial. Medicare Advantage plans that offer in-home support and transportation have reduced hospitalizations among elderly patients by 27%, showing the impact of care beyond clinical settings. 🔄 Stronger Provider Partnerships: Accountable Care Organizations (ACOs) and risk-sharing models have improved provider collaboration, leading to significant savings and better patient outcomes. In 2022, ACOs in Medicare’s Shared Savings Program generated $1.8 billion in net savings while enhancing patient care. Ongoing Challenges ⚠️ Uneven Adoption Across the Country: While progress is evident, rural and independent providers face challenges in adopting value-based models due to financial limitations and a lack of technological infrastructure. Closing these gaps is critical for broader success. ⚠️ Aligning Incentives: For VBC to truly thrive, there must be alignment across payers, providers, and patients. Current administrative complexities and inconsistent quality measures create friction, hindering the full realization of VBC's potential. ⚠️ Addressing Health Equity: While data-driven care is powerful, disparities in access and algorithmic bias can worsen health inequities. Health plans must focus on equitable payment models and culturally competent care to ensure VBC benefits all populations. The Road Ahead: A Call to Action To accelerate the transition to value-based care, health plans must: • Expand risk-sharing arrangements to support providers of all sizes, including rural and independent practices. • Harness AI and analytics to drive personalized, proactive care while minimizing bias. • Tackle social determinants of health to prevent illness and reduce disparities. • Simplify admin. processes to ease provider burden and improve care efficiency. Value-based care is no longer a distant goal but a present reality. Now is the time to refine, scale, and optimize these models to build a system that rewards better health outcomes.

📣 CMS just released the FY 2026 IPPS & LTCH PPS Proposed Rule, and as someone working at the intersection of health policy, musculoskeletal care, and AI innovation, I see both encouraging momentum — and missed opportunity. ✅ The continued support for the TEAM model and expansion of patient-reported outcomes (PROs) into outpatient settings are clear signals that value-based care is accelerating. It's a shift long overdue — moving from counting services to measuring what matters most: outcomes. The 741 hospitals selected may have angst around this new mandatory bundle, but in my opinion the challenge is also an opportunity for care transformation. 🤔 It wasn't surprising to see the Health Equity Measure proposed for repeal given the new administration's stances. However, I was quite disappointed to see the proposed removal of quality measures related to social care screening. Yes, I understand the collective sigh of relief from institutions that have struggled to operationalize this data collection. Implementation is complex. But stepping away from structured screening risks sidelining some of the most actionable, high-yield information we have for improving outcomes — especially in underserved populations. In my own work, I've seen how structured, proactive engagement around social needs closes gaps and drives meaningful results. My hip fracture patient population has a 19% prevalence of social needs, cutting across race, geographic region, age, and religion. Pairing clinical care with social care at Duke has informed our ability to coordinate care for patients who need additional support and drive outcomes while containing cost. 💡 I believe AI has a critical role to play in care transformation. The reality is that collecting PROs and social needs data at scale — and making it actionable — still feels overwhelming to many health systems. The administrative lift remains high. But with intelligent care orchestration layers and AI agents designed for outbound patient engagement, we have the tools to ease this burden. These systems can now proactively reach out to patients using thoughtful human in the loop AI systems in order to assess, summarize, and route what clinicians need to see — reducing friction and supporting a smoother transition into true value-based care. 🌎 We’re entering an era where value is defined not just by what care is delivered, but how connected, personalized, and responsive that care is. Let’s keep building toward that. 🔗 Link to CMS Rule in the Comments #ValueBasedCare #CMS #AIinHealthcare #TEAMModel #PatientReportedOutcomes #SocialDeterminants #HealthEquity #CareInnovation #HealthPolicy #PopulationHealth #OutcomesMatter #HealthcareAI

🚀 AI Agents for Healthcare Branding in the Value-Based Care Era: Revolutionizing Healthcare Experiences! 🏥💡 The healthcare industry is shifting from traditional volume-based care to value-based care, emphasizing improved patient outcomes, proactive engagement, and cost-efficient healthcare delivery. As patient expectations evolve, healthcare organizations must move beyond conventional service models to innovative AI-driven relationship management strategies that enhance patient engagement, streamline care coordination, and build trust. 🌟 AI Agents: The Game-Changer in Healthcare Branding 🌟 AI agents are reshaping healthcare branding by ensuring consistent, intelligent, and seamless interactions across multiple touchpoints. These intelligent systems go beyond automation—they personalize patient experiences, predict needs, and optimize engagement strategies, making healthcare branding more patient-centric and digitally efficient. 💡 How AI Agents Are Transforming Healthcare Branding 💡 ✅ From Transactional to Relational Care: AI agents enable continuous engagement instead of episodic interactions, ensuring long-term patient relationships. ✅ Proactive & Personalized Healthcare: AI agents analyze patient data, predict health risks, and automate wellness reminders, ensuring early interventions and better outcomes. ✅ Omnichannel Engagement: Whether through hospital websites, mobile apps, chatbots, or voice assistants, AI agents deliver real-time, context-aware, and emotionally intelligent interactions. ✅ Enhancing Trust & Brand Loyalty: AI-driven sentiment analysis ensures empathetic communication, reinforcing patient trust and provider credibility. 📢 Who Benefits from AI-Powered Healthcare Branding? 📢 🏥 Hospitals & Healthcare Providers: AI agents reduce administrative workload, optimize patient engagement, and improve satisfaction scores. 👨⚕️ Doctors & Healthcare Professionals: AI-powered assistants enhance workflow efficiency, automate documentation, and support clinical decision-making. 💰 Payers: AI-driven risk stratification, fraud detection, and personalized health insights improve policyholder engagement and cost efficiency. 🏛️ Government & Public Health Agencies: AI chatbots educate populations, optimize healthcare resources, and expand telemedicine accessibility. 🧑⚕️ Patients: AI-driven healthcare assistants empower patients with 24/7 support, proactive health insights, and personalized treatment recommendations. 🔮 The Future of AI Agents in Healthcare Branding 🔮 As value-based care models expand, AI agents will continue to revolutionize healthcare branding, patient engagement, and relationship management by: 🌍 Strengthening provider-patient relationships through intelligent engagement. 📊 Scaling personalized healthcare branding strategies with predictive analytics. 💰 Enhancing cost efficiency by automating workflows and preventive interventions. #AIinHealthcare #ValueBasedCare #DigitalHealth #AIAgents

Two years ago, generative AI burst onto the scene, and I've been fascinated by its potential in healthcare ever since. Could this technology finally solve some of medicine's biggest problems? Or will it simply become another overhyped innovation, full of promise but lacking real-world impact? I had the opportunity to discuss these questions with Munjal Shah, co-founder and CEO of Hippocratic AI on Fixing Healthcare with Dr. Robert Pearl and Jeremy Corr. Important takeaways: 🔹 AI can finally make value-based care a reality. Shah's company is using AI-powered virtual agents to reach more patients, lower costs and prevent unnecessary hospitalizations—something traditional models have struggled to scale. 🔹 The economics of AI are changing everything. At just $9 per hour (and likely to drop further), this technology is making proactive, preventative care feasible at scale. 🔹 This isn't about replacing clinicians, says Shah. The key is empowering patients and healthcare professionals. Unlike other AI companies focused on diagnostics or clinical decision support, Hippocratic AI is tackling a different problem: expanding access to care, ensuring patients take their medications, and filling critical gaps in chronic disease management. The potential here is enormous. Healthcare has always operated under a scarcity mindset: too few doctors, too few nurses, too little time. But what happens when AI makes it possible to check in on every at-risk patient every day? I recognize AI is a charged topic in medicine today. I encourage you to listen to this episode and learn firsthand about the role AI (and companies like Hippocratic AI) could play for patients, clinicians and the entire healthcare system. 🎧 Listen: https://lnkd.in/dEhVU37v What do you think: Are we finally at the tipping point where AI can meaningfully improve patient care? Let's discuss. ⬇️