The Beat

Healthcare organizations are under increasing pressure to make better use of data, AI, and automation but none of those technologies can reach their full potential without reliable access to the right patient information. In this episode of Interop Now, host Sandy Vance chats with Dima Goncharov, co-founder & CEO of Metriport, about the evolving healthcare interoperability landscape, health information exchanges, FHIR, QHINs, and the challenge of bringing fragmented clinical data together. They discuss why interoperability is becoming table stakes in modern healthcare, the limits of building connections in-house, and how a stronger data foundation can help providers move from simply exchanging information to using it to improve care. They also explore how AI and large language models answer complex patient questions in real time when the underlying data is trustworthy, accessible, and properly organized.In this episode, they talk about:Why healthcare interoperability continues to evolve and why connecting to a single network still isn't enough to create a complete patient picture.How health information exchanges, national networks, and emerging frameworks such as TEFCA fit into today's interoperability landscape.Why healthcare data transformation, patient matching, record location, and deduplication remain major challenges even after connectivity is established.Why Metriport believes interoperability should be treated as table-stakes infrastructure, rather than a project healthcare organizations have to continually build and maintain themselves.How clean, accessible longitudinal patient data creates the foundation for AI, automation, and modern care models.Why AI and LLMs could help providers synthesize complicated healthcare data and answer patient-specific questions in real time.Real-world examples of interoperability helping providers surface previously unavailable information, avoid unnecessary testing, and make faster treatment decisions.A Little About Dima:Dima Goncharov is the Co-Founder and CEO of Metriport, an open-source platform transforming healthcare data exchange. With a background as a software engineer, Dima has built everything from aerospace and defense mission systems to highly available cloud database services at Amazon Web Services (AWS). Dima’s passion for transparent, secure, and accessible health data solutions is evident in Metriport’s mission and the APIs they’ve developed for seamless medical record retrieval. Dima is an alumnus of both British Columbia Institute of Technology and of Y Combinator’s S22 cohort. Now based in San Francisco, he leads Metriport’s efforts to modernize and open up healthcare interoperability for providers everywhere. 

Passwordless authentication sounds like a solved problem, until you try to roll it out across a hospital full of shared clinical workstations. In this episode, host Sandy Vance sits with Dawud Gordon, Ph.D. Founder & CEO at Twosense.AI, to unpack why passwordless projects in healthcare so often stall out and what it actually takes to get them across the finish line. Dawud explains how continuous, invisible authentication powered by behavioral biometrics can eliminate passwords for clinicians without adding friction to patient care, and why the real risk in shared workstation environments isn't malicious hackers but accidental session sharing between coworkers. He also shares hard numbers from a 90-day passwordless deployment at a top 10 U.S. health system, including an 89% drop in login failures and a 79% drop in password-related help desk tickets across 17,000 users. The conversation closes with a look ahead at the next frontier of identity security: verifying not just human users, but the AI agents acting on their behalf. In this episode, they talk about:Why compliance is finally forcing health systems to ditch passwords for clinical staffWhy passwordless projects stall: what works in the back office breaks on shared clinical workstationsHow Twosense.AI makes authentication fully passive and invisible using behavioral biometricsThe real risk isn't hackers; it's accidental session sharing between clinicians"Is this really you?" A one-click fix that replaces a full compliance investigationThe case study: 17,000 users, 90 days, 89% fewer login failures, 79% fewer help desk tickets, 100% of sessions protectedHow the rollout happened passively in the background, with zero change management painBuilding an AI native security product, from research prototype to enterprise scaleThe next frontier: verifying AI agents, not just humans, on a user's sessionTwosense.AI's new SSF/CAEP support for broadcasting identity risk across the enterpriseDawud's advice: design for clinical and non-clinical together, and win over your loudest skeptics A Little About Dawud:Dawud Gordon, Ph.D., is the CEO and Co-Founder of Twosense, a cybersecurity company revolutionizing identity security through continuous, invisible authentication. An award-winning researcher with a doctorate in Computer Science from the Karlsruhe Institute of Technology, Dawud leverages deep expertise in machine learning and behavioral biometrics to solve the friction of traditional Multi-Factor Authentication (MFA). Under his leadership, Twosense has developed a platform that automates security challenges on behalf of users, successfully securing millions in venture funding and key partnerships with organizations like the U.S. Department of Defense. His work bridges the gap between rigorous academic research and scalable enterprise solutions, transforming how organizations protect their infrastructure without compromising user experience. Driven by a vision to make security effortless, Dawud is defining the future of passive identity verification for the modern workforce.  

Ransomware remains one of the biggest cybersecurity threats facing healthcare, but the threat landscape is changing rapidly. Attackers are becoming more targeted, ransomware-as-a-service is making sophisticated tools more accessible, and artificial intelligence is giving cybercriminals new ways to identify vulnerabilities and craft convincing attacks. In this episode of The Beat's Cybersecurity at ViVE series, Sandy Vance speaks with Dave Bailey, VP of Consulting Solutions & Strategy at Clearwater, about the evolving ransomware threat and what healthcare organizations can do to stay ahead of it. Dave explains why smaller healthcare organizations and specialty practices are increasingly attractive targets, how attackers are using AI to improve social engineering and phishing, and why traditional cybersecurity approaches may not be fast enough for the threats ahead. The conversation also explores why healthcare organizations need to understand their AI risk, establish guardrails, inventory their AI use cases, and prepare defenses that can respond at machine speed. Dave shares practical advice for organizations beginning their AI journey, while emphasizing that cybersecurity can no longer be something organizations assess once a year. It has to become a continuously monitored, evolving process. In this episode, they talk about: Why healthcare continues to be one of the most attractive targets for ransomware How ransomware attacks have shifted toward smaller healthcare organizations and specialty practices Why dental practices and specialty providers can be particularly appealing targets How ransomware-as-a-service has created a more scalable business model for cybercriminals Why cybercriminals increasingly operate like businesses How attackers decide which healthcare organizations to target Why post-COVID healthcare's rapid shift to telehealth changed the threat landscape How AI is making phishing and social engineering attacks more sophisticated Why AI creates new governance and risk-management challenges for healthcare organizations Why healthcare organizations need defenses that can operate at machine speed How frontier AI models could help attackers discover previously unknown software vulnerabilities Why patching needs to become faster as AI-powered attacks evolve Why healthcare organizations need to challenge vendors about their cybersecurity roadmaps How organizations can begin building an AI governance strategy Why organizations should inventory their AI use cases before trying to govern them How healthcare organizations can use a tiered approach to AI risk Why workforce training and communication are essential to responsible AI adoption Why cybersecurity risk assessment can no longer be a once-a-year exercise Why scalability and continuous monitoring will become increasingly important A Little About Dave: Dave Bailey is Vice President of Consulting Solutions & Strategy at Clearwater, where he leads the development and delivery of enterprise-level cybersecurity and risk management services for healthcare organizations nationwide. With more than 24 years of cybersecurity experience, including 14 years focused on healthcare, Dave is a trusted advisor to executive teams navigating complex regulatory, operational, and cyber risk challenges.   A recognized authority in cyber risk management and NIST Cybersecurity Framework assessment and implementation, Dave brings a strategic, business-aligned approach to security transformation. He previously served 13 years as a Communications and Information Officer in the United States Air Force, with leadership assignments spanning the Pentagon, domestic bases, and overseas operations.   Dave holds an Executive MBA from Quantic School of Business and Technology and is a CISSP, blending executive perspective with deep technical expertise.  

Healthcare doesn't need more AI pilots. It needs AI that can prove its value. Healthcare organizations are moving beyond experimenting with individual AI tools and beginning to think about AI as enterprise infrastructure. But scaling AI across an entire health system requires more than adding algorithms. It requires integrated workflows, secure technology infrastructure, clinical validation, data governance, and measurable financial and clinical impact. In this episode of The Beat's AI at ViVE series, Sandy Vance sits down with Dr. David Stoffel, Chief Business Officer at RapidAI, to discuss how healthcare AI is evolving from point solutions into enterprise platforms. David explains how RapidAI expanded from its roots in stroke care to a broader clinical AI platform, why health systems are looking to consolidate dozens of AI pilots, and what it takes to connect imaging insights to action throughout the patient journey. The conversation also explores clinical AI, healthcare IT infrastructure, interoperability, AI governance, clinical validation, reimbursement, and ROI. David shares why the next phase of healthcare AI will be defined not simply by what an algorithm can do, but by whether it can produce measurable improvements in clinical care and financial performance. In this episode, they talk about: How RapidAI evolved from a stroke detection tool into an enterprise clinical AI platform Why healthcare is shifting from asking "Why AI?" to asking "How?" The three major challenges healthcare organizations are looking to AI to solve Why clinical impact, workforce capacity, and future-proof IT infrastructure all matter The problem with managing dozens or even hundreds of individual AI point solutions Why enterprise AI platforms can help connect workflows across departments What "deep clinical AI" means and how it can support the patient journey beyond initial disease detection How AI can quantify, visualize, localize, and track disease over time Why seamless workflow integration is critical to making AI useful in clinical practice How hybrid on-premises and cloud infrastructure can improve resilience during cybersecurity incidents Why AI governance and performance tracking are essential for turning a tool into an operational solution How RapidAI is incorporating third-party algorithms into its platform Why clinical validation remains central to successful healthcare AI adoption What CIOs should look for when evaluating AI platforms How health systems can evaluate clinical ROI and financial ROI Why reimbursement can help transform AI from a cost center into a potential profit center Why the healthcare AI market may be headed toward consolidation and simplification Why measurable clinical and financial value will ultimately separate successful AI companies from the rest A Little About David: David Stoffel, M.D., has spent more than 20 years developing and commercializing innovative technologies and services in the medical device industry. David has an extensive track record of success in scaling healthcare businesses. Notably, he led marketing and corporate development at Intuitive Surgical, contributing significantly to establishing the da Vinci surgical robotic system as a new surgical standard of care. He also helped launch and lead the Mobile Cardiac Telemetry business at iRhythm Technologies, one of the fastest-growing digital health companies, and was Chief Business Officer at Ceribell, maker of an innovative point-of-care EEG solution. Earlier in his career, David was a partner at a healthcare venture capital firm, where he invested in and helped build early-stage companies. He started his career in investment banking focused on corporate finance.  At RapidAI, David leads a variety of commercial, clinical, and operational teams, including Marketing, Customer Retention and Success, Clinical Affairs, Training & Education, Corporate Development, and Finance.  David has a BA in Economics from Stanford University and received an MD and MBA from the University of Chicago.

Artificial intelligence is rapidly changing healthcare, but successful AI adoption requires much more than choosing the right technology.  In this episode of The Beat's AI at ViVE series, Sandy Vance speaks with Dr. Melinda S. Kidder, DHA, MSN, RN, CENP, Chief Nursing Officer, Office of the National Coordinator for Health IT at the U.S. Department of Health and Human Services, and Bill Slovin, Chief Technology Officer at Brandon Systems, about what healthcare organizations need to get right before AI can truly deliver value. Their conversation explores some of the biggest challenges facing healthcare AI adoption, including fragmented data, interoperability, data quality, alert fatigue, clinician buy-in, patient safety, and the importance of understanding the problem before selecting an AI solution. Dr. Kidder and Slovin also dive into how healthcare leaders can evaluate AI and analytics vendors, why frontline clinicians need to be part of technology decisions, and how interoperability can help unlock more effective AI-powered care. In this episode, they talk about: Why healthcare still has a long way to go on interoperability How fragmented data limits the effectiveness of AI in healthcare Why AI is only as good as the data behind it The importance of getting clinical, financial, HR, and operational data to work together Why healthcare organizations should solve the problem before choosing an AI solution How AI could help reduce alert fatigue for nurses and clinicians Why technology adoption is often a cultural and workflow challenge, not just a technology challenge How to get clinicians involved in AI implementation and earn their trust Why frontline staff can make or break a technology implementation How government policy can support responsible AI innovation while protecting patient safety What interoperability means for the future of AI in healthcare How TEFCA is helping expand health information exchange What healthcare leaders should ask when evaluating an AI or analytics vendor Why continuous monitoring after an AI implementation is critical Why healthcare organizations should focus on business value instead of adopting AI simply because it is the latest technology A Little About Melinda and Bill: Dr. Melinda S. Kidder, DHA, MSN, RN, CENP is a healthcare leader and nurse informatics expert focused on advancing technology, interoperability, and AI in healthcare. A former bedside nurse, Dr. Kidder transitioned into informatics after working with her organization's IT department to implement and evaluate technology in the clinical setting. In her current role as Assistant Secretary for Technology Policy at the U.S. Department of Health and Human Services, she helps advance healthcare technology and interoperability while bringing a clinician's perspective to policy, education, and innovation. Bill Slovin is Chief Technology Officer at Brandon Systems, where he leads technology initiatives focused on healthcare, analytics, software development, and AI. With more than 25 years of experience in IT and software development, Bill began his career as a developer at Brandon Systems and has built deep expertise in healthcare systems and data-driven technologies. His work focuses on using technology to improve efficiency, support better patient outcomes, and advance digital transformation in healthcare.