AI at ViVE Podcast

How Bland Is Replacing Legacy IVR Systems and Fixing Healthcare's Phone Problem

In this episode, host Sandy Vance chats with Isaiah Granet [https://www.linkedin.com/in/isaiah-n-granet/], co-founder and CEO of Bland [https://www.bland.ai/], for a sharp and eye-opening conversation about one of the most overlooked bottlenecks in healthcare: the phone call. Bland now handles 3.5 million phone calls a week, has raised over $100 million, including a $40 million Series B, and is backed by Emergence Capital, Scale, and Y Combinator. Isaiah brings a refreshingly honest take on what it actually takes to get voice AI into production in healthcare, why most vendors are just talking about it rather than doing it, and why the security risks hiding in third-party AI dependencies should be keeping every healthcare CIO up at night. In this episode, they talk about: * Most people call a call center because they are at the end of the line and cannot solve their problem any other way * The best voice AI systems conform to the caller, not the other way around * Intake is the fastest path to ROI for health systems deploying voice AI for the first time * Bland tracks emotional sentiment, call escalation rates, and a unique metric called utterances to measure patient experience quality * Bland does not use OpenAI or any third-party LLM under the hood, meaning PHI never touches an outside vendor * Health systems should demand that calls go live within 30 days and measurable automation within 60 days * A single third-party dependency, three steps removed from a vendor, recently led to a class action lawsuit * Always declare that it is an AI agent on the call; deceptive practices destroy the trust that voice AI depends on * The CIO role is becoming one of the most important in any healthcare organization, as AI decisions multiply A Little About Isaiah: Isaiah values community, family, and impact above all else. He believes that building for impact is what makes life. In addition to being the cofounder and CEO of Bland, he also sits on the board of the nonprofit he founded, the San Diego Chill.

How Limina Is Making Sensitive Health Data Safe for AI

In this episode, host Sandy Vance sits down with Patricia Thaine, [http://linkedin.com/in/patricia-thaine?originalSubdomain=ca] co-founder and chair of Limina [https://www.getlimina.ai/en] (formerly known as Private AI), for a fascinating conversation about one of the most underappreciated bottlenecks in healthcare AI adoption: the privacy of unstructured data.  With a background in natural language processing and privacy research, Patricia built the company from the ground up to solve a problem most organizations did not even know they had. Today, her platform helps health systems, research organizations, and payers de-identify everything from clinical notes to ambient listening data so they can train models, share data for research, and move their AI initiatives forward without putting patient privacy at risk.  If your AI initiative is stalled because of privacy concerns, this episode is exactly what you need to hear. In this episode, they talk about: * 80 to 90% of healthcare data is unstructured, and most organizations have no idea what sensitive information is hiding in it * Cloud providers require you to send your data outside your environment, and that alone is a dealbreaker for many health systems * De-identification is not just about removing names; quasi-identifiers like age ranges, locations, and diagnoses all factor into re-identification risk * The goal is to keep re-identification risk below 0.04%, not just strip out obvious fields * Training AI models on real PHI creates a memorization problem where the model can regurgitate patient information in production * Providence Health has used Limina since the early days to train patient and physician-facing chatbots safely * A mature privacy-to-AI operating model requires statisticians, product teams, IT, governance, and legal all at the table * LIMINA rebranded from Private AI because the old name kept attracting requests for on-premise LLMs, which is not what they do A Little About Patricia: Patricia Thaine is the Co-Founder & Chairwoman of Private AI, a Microsoft-backed startup that raised their Series A led by the BDC. Private AI won the Privacy Innovation Award at PICCASO 2024, was named a 2023 Technology Pioneer by the World Economic Forum, and was a Gartner Cool Vendor. Patricia is also the host of The Data Frontier podcast and was on Maclean’s magazine Power List 2024 for being one of the top 100 Canadians shaping the country.

Turning Healthcare AI from Vision to Verified Impact with Pegasus One

What does it really take to get a healthcare AI product past the proof-of-concept stage and into production? In this episode of the AI at ViVE series on the BEAT Podcast, host Sandy Vance sits down with Tushar Puri [https://www.linkedin.com/in/tusharpuri/], CEO of Pegasus One [https://www.pegasusone.com/], and Sebastian Ouslis [https://www.linkedin.com/in/sebastian-ouslis-3446b9148/], Co-Founder of ChartR Health [https://www.chartrhealth.com/], to find out. Together, they pull back the curtain on how ChartR is building an autonomous analytics platform that lets hospitals interrogate their own data across silos in real time, and how Pegasus One's SONG framework is the foundation making it possible. From managing agent drift to the very real stakes of lagging data in sepsis care, this conversation is a masterclass in building healthcare AI that can actually go from pie-in-the-sky to impact you can quantify.  In this episode, they talk about: * Why most healthcare AI projects never reach production * The real meaning behind the SONG framework * How data silos slow down hospital decision-making * Turning weeks of analysis into minutes with AI * The hidden cost of poor workflow integration * Why governance must be built in from day one * The importance of designing for real-world healthcare systems * How ChartR enables continuous analytics across hospital data * What agent drift is and why it matters long term * The role of partnerships in building scalable AI products A Little About Tushar and Sebastian: Tushar Puri is the Founder and CEO of Pegasus One, where he helps organizations build scalable AI and software products that actually make it to production. With more than 15 years in product engineering, he specializes in bridging the gap between technical innovation and real-world implementation, especially in healthcare. Sebastian Ouslis is the Co-Founder of ChartR, an AI-driven healthcare analytics platform focused on turning complex clinical data into real-time insights. His work centers on using AI to help hospitals continuously learn, adapt, and improve patient outcomes.

How UAMS Is Scaling AI Across the Enterprise with Luma Health

AI in healthcare is moving fast, but what does real, operational impact actually look like? In this episode, host Sandy Vance chats with Aditya Bansod [https://www.linkedin.com/in/adityabansod/], CTO and co-founder of Luma Health [https://www.lumahealth.io/], and Michelle Winfield-Hanrahan [https://www.linkedin.com/in/michelle-winfield-hanrahan-msn-mha-bsn-rn-b3b2593b/], RN BSN MHA MSN about this very topic. They share how the University of Arkansas for Medical Sciences [https://www.uams.edu/] is scaling AI beyond the call center and into complex, high-friction workflows like fax processing and patient access. From backfilling missed appointments to transforming referral intake, this conversation dives into what it takes to build trust, move quickly, and turn AI into measurable results across an entire health system. In this episode, they talk about: * How UAMS identified a high-impact, low-risk entry point for AI in call center workflows * Turning after-hours cancellations into filled appointments and improved patient access * Why trust in the partner matters just as much as trust in the technology * Expanding from AI call handling to backend fax automation * How AI is reducing referral lag time and improving data visibility * The power of an EHR-first strategy and working inside existing workflows * Avoiding “AI sprawl” and the challenge of managing too many point solutions * Real talk on ROI: operational efficiency, revenue lift, and happier patients * Why success builds momentum for scaling AI across departments * Practical advice for health systems navigating the explosion of AI vendors A Little About Aditya and Michelle: Aditya Bansod is CTO and co-founder of Luma Health. With a lifelong passion for building software, Bansod leads Luma Health’s technical vision and strategic direction for building a Patient Success Platform that empowers healthcare providers to serve their patients better and improve healthcare outcomes. With over 15 years of experience as a product management leader developing mobile solutions at Adobe and Microsoft, and at venture-backed start-ups, Bansod made the transition from B2B software solutions to healthcare in 2015 to have a meaningful and measurable impact on how providers use mobile technologies to engage with and communicate with their patients. Michelle Winfield-Hanrahan, RN BSN MHA MSN, is a seasoned healthcare clinical operations executive with over 20 years of experience in optimizing patient access, improving patient engagement, and streamlining clinical operations. Throughout her career, Michelle has demonstrated a strong ability to lead cross-functional teams and implement strategic initiatives that enhance the patient experience, increase operational efficiency, and drive sustainable growth. In her current role as Chief Clinical Access Officer and Associate Vice Chancellor of Access at the University of Arkansas for Medical Sciences, Michelle oversees patient access and flow from both the outpatient and inpatient setting, ensuring seamless patient flow and appointment scheduling, reducing wait times, and improving overall patient satisfaction. She has spearheaded innovative solutions that leverage technology to enhance patient communication and engagement, ensuring that patients remain informed, involved, and empowered throughout their healthcare journey. Michelle is dedicated to driving innovation in healthcare operations and is committed to making quality healthcare more accessible and efficient for all.

How Optura Is Helping Healthcare Organizations Actually Transform

In this episode, host Sandy Vance and Andy Fanning, [https://www.linkedin.com/in/andyfanning/] the CEO & Co-founder of Optura.ai [http://optura.ai], sit down to talk about why so many healthcare organizations are making AI headlines without actually transforming anything. Andy breaks down how Optura helps payers, providers, and life sciences companies move from a scattered list of AI ideas to a prioritized, production-ready roadmap with measurable return on investment. From crowdsourcing use cases across an entire organization to aligning AI investments with executive strategy, this episode is packed with practical insight for any healthcare leader who wants AI to actually move the needle. In this episode, they talk about: * Most healthcare leaders feel they are not moving fast enough with AI, despite the headlines * Shadow AI is just an unmet need, and governance is the answer * Crowdsourcing AI ideas from the bottom up reveals hotspots that leadership often cannot see * Aligning AI use cases to existing strategic initiatives makes adoption dramatically easier * Point solutions do not share context, and that missing context is where the real value lives * Return on AI investment requires defining what value actually means for your specific organization * Agentic AI is the next big wave, and organizations need to decide where they sit on the risk spectrum * Trust at the frontline is built by showing workers how AI follows their own standard operating procedures * If finance cannot see the ROI, they will conclude that AI does not work A Little About Andy: Andy is the Co-Founder and CEO of Optura.ai, where he's on a mission to help healthcare organizations stop dabbling in AI and start seeing real returns from it. His team built an AI Orchestration Platform designed from the ground up for healthcare, giving organizations the infrastructure, trust, and clarity to turn AI ambition into measurable ROI. The platform does it all in one place: spotting high-value AI opportunities, building and deploying custom agents, unlocking data without the ETL headache, auto-generating workflows from existing SOPs, and tracking return on AI investment in real time. No black boxes, no guesswork. Just AI that actually proves its worth.