From Chart Review to Clinical Capacity Duke's AI Strategy

The $15 Billion Problem Hiding Inside Hospital Workflows

The $15 Billion Problem Hiding Inside Hospital Workflows Everyone wants to talk about AI in healthcare. Greg Miller wants to talk about the work no one sees. In this live conversation from ViVE, Greg Miller, VP at Carta Healthcare, joins the Boombostic Health Podcast to expose one of healthcare's most expensive hidden problems: the billions spent manually abstracting clinical data for registries, quality reporting, accreditation, CMS measures, and benchmarking. This is not basic data entry. It is highly skilled clinical work. Nurses and clinical experts are digging through EHRs, interpreting complex patient records, and making judgment calls that directly impact how hospitals measure quality and performance. Greg calls it "swivel chair interoperability," and it is costing U.S. healthcare an estimated $10–$15 billion every year. But the answer is not just "throw AI at it." Greg makes the case for hybrid intelligence: AI powerful enough to accelerate the work, with clinician oversight strong enough to earn trust. Because in healthcare, accuracy is not optional, workflow matters, and the market is moving past AI hype toward solutions that prove ROI fast. The real question is no longer whether healthcare will use AI. It is whether AI can actually solve the messy, expensive, high-stakes problems buried inside the system. Timestamps 00:00 — Why clinical data abstraction is healthcare's hidden cost center 02:16 — Why registry work still requires clinical judgment 04:37 — The trust gap: why AI needs clinician oversight 05:38 — How Carta Healthcare repositioned around "hybrid intelligence" 10:01 — Why the AI hype cycle is giving way to ROI 11:54 — Why healthcare technology fails when it sits outside the workflow Best Moments Greg breaks down why some registry questions cannot be answered by simply searching the EHR. They require clinical inference, context, and expertise. He also explains why healthcare buyers are getting sharper. A year ago, AI was the shiny object. Now, health systems want practical implementation, workflow integration, and measurable financial impact. Key Takeaway AI will not win in healthcare because it sounds impressive. It will win when it removes burden, protects accuracy, fits the workflow, and earns clinician trust. That is the promise of hybrid intelligence. Featured Guest Greg Miller VP, Carta Healthcare Follow Boombostic Health for more conversations with the leaders building the future of healthcare.

From Chart Review to Clinical Capacity Duke's AI Strategy

How Duke Is Building AI That Actually Works in Healthcare | Matt Cox, Duke Health Healthcare does not need more AI hype. It needs AI that can survive clinical reality: compliance, governance, patient safety, workflow, physician adoption, and the brutal operational pressure every health system is facing. In this episode of Boombostic Health, Bradley Bostic sits down live at ViVE 2026 with Matt Cox, Executive Director for Digital Value Creation at Duke Health, to unpack how Duke is moving from AI experimentation to real-world clinical impact. Matt shares how Duke is building its own AI capabilities, including the Scout LLM platform, to safely support use cases across the health system — from clinical research and chart review to virtual nursing, ambient documentation, operational efficiency, and clinician burnout. The standout example: Duke used Scout to review 200 pancreatic cancer patient charts in less than two hours — work that would typically take a PA or nurse practitioner one to two weeks. That is not theoretical AI. That is capacity creation. That is clinicians working at the top of their license. That is the future healthcare actually needs. In this episode Bradley and Matt discuss: * Why Duke chose to build internal AI infrastructure instead of relying only on outside tools * How Duke evaluates AI through governance, compliance, safety, bias, and clinical decision support * The role of Duke Institute for Health Innovation and EHRX in exposing health data safely * How Scout is helping accelerate chart review, clinical research, and protocol compliance * Why AI's biggest opportunity may be giving time back to clinicians * Duke's approach to investing in startups that solve real internal health system problems * The growing importance of virtual nursing, ambient AI, and operating-room intelligence * Why human factors may determine whether healthcare AI succeeds or fails * What Matt is watching across the healthcare innovation landscape after ViVE Key Takeaways 1. The winning health systems will not just buy AI. They will govern it. Duke is not treating AI as a shiny object. It is building processes to test whether tools are safe, compliant, clinically appropriate, and operationally useful before scaling them. 2. AI only matters if it removes real friction. The Scout example shows the real promise: not replacing clinicians, but removing time-consuming administrative and review work so highly trained people can focus on patients, research, and care delivery. 3. Innovation starts with the problem, not the pitch deck. Duke's venture approach begins with internal pain points — nurse burnout, clinician burnout, access, capacity, and workflow — then looks for companies that can help solve them. Chapters 00:00 — Live from ViVE 2026 with Matt Cox of Duke Health 01:22 — Matt's role leading digital value creation at Duke 04:05 — How Duke built the foundation for healthcare AI through EHRX 05:04 — Why Duke created its own LLM capability 05:52 — Governing AI through safety, compliance, and clinical decision support 07:21 — Inside Scout, Duke's AI platform 08:18 — Reviewing 200 pancreatic cancer charts in under two hours 10:11 — Using AI to free clinicians and expand capacity 11:52 — Duke's shift from reactive innovation to targeted venture investing 12:44 — Why nurse burnout and virtual nursing are top priorities 14:07 — Ambient AI and the fastest adoption Matt has seen in healthcare 15:44 — How Duke prioritizes investment and implementation opportunities 17:42 — What Matt is learning at ViVE: human factors, new roles, and the future of work Featured Guest Matt Cox Executive Director, Digital Value Creation Duke Health Mentioned in this episode * Duke Health * Duke Institute for Health Innovation * EHRX * Scout LLM Platform * Ambient AI * Virtual Nursing * Algorithm-Based Clinical Decision Support * ViVE 2026 About Boombostic Health Boombostic Health puts a spotlight on the people, technologies, and ideas moving healthcare forward. Hosted by Bradley Bostic, each conversation explores the practical innovations reshaping care delivery, diagnostics, data, AI, and the future of healthcare. Subscribe for more conversations with the leaders building what comes next in healthcare.

Why Healthcare AI Needs Better Data Before Bigger Promises

Why Healthcare AI Needs Better Data Before Bigger Promises | Michael Meucci, CEO of Arcadia Episode Description: AI is moving fast in healthcare, but the real question is not who has the newest tool. It is whether the data behind it can be trusted. In this episode of Boombostic Health, Marcus Gordon sits down with Michael Meucci, CEO of Arcadia, to discuss what it will actually take for AI to improve care delivery, reduce friction, and create measurable impact across the healthcare system. Michael shares why healthcare organizations need complete, timely, clinically relevant data to make AI useful and safe. He explains how fragmented information can create risk instead of intelligence, why rural healthcare may become one of the most important innovation opportunities in the country, and how leaders need to rethink workflows instead of simply layering AI on top of broken processes. The conversation also explores the growing role of the healthcare consumer, the pressure facing hospitals and health plans, and why the next 24 months may reshape how care is delivered, measured, and experienced. Key Takeaways: 1. AI in healthcare is only as strong as the data behind it. Michael explains why trusted, complete, timely, and relevant data is essential for AI to support better decisions and avoid creating more noise. 2. Rural healthcare may become a major innovation frontier. With new national attention and investment, rural hospitals and critical access facilities have an opportunity to adopt modern care models, virtual-first approaches, and advanced technology in ways that could reshape access. 3. Leaders need to redesign workflows, not just adopt tools. Michael challenges healthcare leaders to rethink long-standing assumptions around EHRs, operating models, automation, and how teams use technology to manage patients, panels, and performance. Topics Covered: Healthcare AI beyond the hype Why trusted data matters in clinical decision-making The risk of incomplete or outdated data How AI can support providers, patients, health plans, and health systems The role of longitudinal patient data Rural healthcare transformation Health equity and access to care Leadership in an AI-driven operating environment Why workflow redesign matters The future role of the healthcare consumer Timestamps: 00:00 Introduction: Michael Meucci, CEO of Arcadia 00:16 What it means to lead a healthcare data and AI company today 01:18 Why healthcare needs AI to work amid cyber threats and margin pressure 02:48 Trust, efficacy, and data integrity in healthcare AI 03:28 Why AI models need timely, complete, and transparent data 05:00 The opportunity to expand access in rural communities 05:35 Rural health transformation and the future of critical access hospitals 08:05 Leadership, workflow redesign, and changing how teams think about AI 10:27 Rethinking tools like Salesforce, EHRs, and workflow systems 12:56 What is most exciting about the next 24 months in healthcare 13:29 Rural care, data liberation, and the rise of the healthcare consumer 14:57 Closing thoughts Guest: Michael Meucci, CEO of Arcadia About Boombostic Health: Boombostic Health features conversations with the leaders, innovators, and builders shaping the future of healthcare. Hosted by Bradley Bostic, the podcast explores the ideas, technologies, and operating models transforming care delivery, diagnostics, data, AI, and patient outcomes.

Can Diagnostics Solve the Healthcare Cost Crisis?

Unlocking the Future of Healthcare: Diagnostics, Data, and Value-Based Innovation Healthcare transformation will not happen through technology alone. It will happen when diagnostics, data, clinical workflows, and incentives finally work together. In this episode of Boombostic Health, Bradley Bostic sits down with Gary Albers and Steve Serota for a practical conversation on how diagnostics can move healthcare from reactive treatment to proactive, value-based care. The discussion explores why laboratory medicine is often one of the most underutilized levers in healthcare, despite its ability to influence earlier intervention, better medication decisions, lower costs, and more personalized care. Gary and Steve share how health systems, payers, providers, and innovators can use diagnostic intelligence to close gaps in care, improve trust, and create measurable outcomes. From pharmacogenomics and primary care workflow redesign to AI, biometric monitoring, and payer partnerships, this episode goes beyond theory and looks at what it actually takes to make value-based care work in the real world. In this episode: * How diagnostics can drive earlier, smarter, and more cost-effective care * Why laboratory medicine needs a larger role in value-based healthcare strategy * Gary Albers on building high-value outcomes through better diagnostic collaboration * Steve Serota on turning lab data into actionable clinical intelligence * Why fee-for-service adoption is slow and what is accelerating the move toward value-based models * How pharmacogenomics and medication optimization can reduce avoidable costs * The importance of primary care workflows in making diagnostics usable at scale * How AI and continuous measurement can support more personalized interventions * Why trust, privacy, and patient engagement matter in a more connected healthcare ecosystem * The future of healthcare through biometrics, personalized medicine, and integrated care models Key Takeaways: * Diagnostics are not just tests. They are decision-making tools that can help identify risk earlier, guide treatment, and reduce waste across the healthcare system. * Value-based care requires more than new payment models. It depends on better data, stronger workflows, aligned incentives, and measurable clinical impact. * The future of healthcare will be more proactive, personalized, and data-driven, but adoption will depend on trust, usability, and proving value in real-world care settings. Timestamps: 00:00 Introduction: Why diagnostics matter in healthcare transformation 01:01 Collaboration as a driver of value-based care 01:34 Gary Albers on high-value diagnostics and laboratory medicine 04:02 Steve Serota on lab data, patient outcomes, and clinical intelligence 07:19 Why value-based care adoption has been slow 08:36 Policy, incentives, and the strategic role of clinical data 10:52 Precision medicine, medication optimization, and cost savings 12:11 Measuring the impact of interventions in real time 14:05 Reimbursement models and the economics of diagnostics 15:01 Funding innovation and upfront investment in value-based care 16:12 Why primary care workflow is critical to diagnostic adoption 19:10 AI, data, and proactive healthcare decisions 22:38 Building trust with more complete patient data 27:43 Biometric monitoring, patient responsibility, and privacy 38:11 Tech giants, health plans, and the future of cost control 44:25 How diagnostics can deliver actionable, affordable outcomes 46:24 Final thoughts: Building the future through collaboration About Boombostic Health: Boombostic Health explores the people, ideas, and innovations reshaping healthcare. Hosted by Bradley Bostic, the show brings together leaders across diagnostics, data, AI, value-based care, and healthcare transformation to discuss what is working, what needs to change, and where the industry is headed next.

Privacy vs. Progress: Can We Keep Patient Data Safe?

Unlocking the Value of Real-World Data in Healthcare This episode dives into the critical role of real-world evidence and data in transforming healthcare, emphasizing how data privacy, de-identification, and innovative tokenization unlock large-scale insights with minimal risk. Presented by industry veterans, it explores methods to responsibly utilize data for research, clinical trials, and patient care improvements, while addressing common misconceptions and trust issues. In this episode: * The importance of real-world data and evidence for healthcare advancement * How de-identification and tokenization protect patient privacy while enabling large-scale analysis * The benefits of open source tokenization models versus proprietary solutions * Practical steps to start using de-identified data for research and clinical decision-making * The emerging role of AI, wearables, and consumer data in personalized healthcare * The evolving landscape of data monetization, partnerships, and purpose-driven analytics * Future of comprehensive data access, trust-building, and patient ownership considerations * How AI accelerates insights while ensuring data quality and security Timestamps: 00:00 - Introduction: Healthcare innovation in Indianapolis & the focus on real-world evidence 00:34 - Why healthcare data privacy regulations were designed for protection, not suppression 01:03 - Industry veterans John and Julie on safe data usage and innovation 01:56 - The 18-year journey into real-world data and evidence with HC1 02:38 - How de-identification preserves privacy, scales data, and enables AI-driven insights 03:21 - The role of data in addressing diagnostic gaps and patient journeys 03:53 - The necessity of large-scale, unbiased data for research and healthcare delivery 04:50 - Explaining tokenization simply and why it matters 05:06 - The challenge of integrating data from multiple sources without bias 06:17 - How consumer wearables add depth to patient understanding 07:10 - Therapy development, clinical trials, and the power of de-identified data 07:42 - The significance of bias reduction in healthcare data analytics 08:36 - Path to monetization: Purpose-driven data use versus the race to the bottom 09:00 - The importance of aligning with organizations sharing your mission 09:48 - Clinical trials and real-world evidence improving enrollment and outcomes 10:57 - Strategies for building trust and ensuring patient security in data sharing 11:25 - Practical steps for initiating de-identification & tokenization 12:17 - Privacy-preserving record linkage & open source tokenization solutions 13:20 - The significance of rigorous de-identification processes and certification 14:13 - How tokenization connects disparate datasets without compromising identity 15:11 - Open source solutions versus commercial fee-based tokenization providers 16:45 - The importance of responsible data sharing and avoiding exploitative marketplaces 17:41 - Enhancing clinical trials with real-world evidence and reducing risks 20:13 - The impact of regulatory changes and partnerships in trials 21:06 - Enabling precision medicine through aggregated, de-identified data 22:22 - The role of CROs and third-party organizations in trial success 23:20 - Using advanced AI for device tracking, supply chain, and supply chain data 24:18 - Challenges and opportunities with physician notes and unstructured data 25:41 - The ongoing need for AI refinement and risk management in de-identification 27:02 - Addressing the potential consequences of data breaches and errors 28:41 - The technical feasibility and limitations of perfect de-identification 29:46 - Handling physician notes, abbreviations, and unstructured data responsibly 31:07 - The future of diagnostics, genomics, and embedded AI in healthcare standardization 32:07 - How tokenized, integrated data empowers providers and payers 33:13 - The importance of clean, trusted data for AI accuracy 34:24 - Personalized, real-time insights improving patient care and provider decision-making 36:47 - The untapped potential of lab and rare disease data for proactive care 38:49 - The challenge of small data scale in specialty labs and opportunities for collaboration 40:37 - Using de-identified lab data to predict disease progression and improve outcomes 43:13 - Integrating consumer wearable and biometric data into healthcare insights 44:14 - The power of personal health data for early detection and prevention 45:24 - Expanding access to EHR data and overcoming legislative barriers 47:11 - Building a culture of data ownership and creating trust with patients 48:32 - The potential influence of patient incentives, transparency, and societal changes 50:09 - Closing thoughts: Trust, purpose, and technology shaping the future of healthcare data