Artificial intelligence in healthcare is moving beyond simply summarizing things that have already happened. In behavioral health, AI is beginning to help clinicians make better-informed decisions before care happens; things like bringing clinical protocols, payer requirements, documentation, and a patient's real-time condition together at the point of care.

In this episode, host Sandy Vance talks with Mehmet Kazgan, CEO and Founder of Cliexa, and Susan Jerlow, VP Enterprise Applications at ERC Pathlight, about their collaboration to use AI to reduce friction across behavioral health workflows. They discuss how AI can help clinicians evaluate medical necessity, improve documentation quality, support authorization processes, and make transitions between levels of care smoother for patients and their families.

The conversation also digs into one of the biggest questions facing healthcare organizations today: How do you actually measure the ROI of AI? 

In this episode, they talk about:

• How AI can bring clinical protocols, payer requirements, and real-time patient information together at the point of care
• Using AI to reduce documentation burden and improve consistency in behavioral health
• How ERC Pathlight uses AI to support admissions and level-of-care decisions
• Why keeping clinicians in the loop is essential for quality, compliance, and trust
• How ERC Pathlight reduced admission declines by 20% through its Enhanced Admission Review process
• Why AI adoption and measurable outcomes are more meaningful than simply promising time savings
• How healthcare organizations can build trust and successfully integrate AI into clinical workflows

A Little About Susan and Mehmet:

Susan Jerlow is a healthcare technology executive and AI strategist leading one of the most advanced real-world AI transformations in behavioral health today. As Vice President of Enterprise Applications, Analytics, and AI at Eating Recovery Center | Pathlight Mood & Anxiety Center, she architects and executes an end-to-end enterprise AI ecosystem across clinical, operational, and analytical domains.

Her work moves beyond pilots and proofs of concept - deploying AI directly into live care environments to reduce clinician burden, improve documentation quality, and enable more consistent, data-driven decisions across the patient journey. Her portfolio includes ambient AI documentation, automated clinical audits, AI-guided assessments, admissions intelligence, and a governed semantic data layer that powers enterprise-wide insights.

She is particularly focused on how AI can separate signal from noise in complex clinical environments - turning fragmented data into clear, actionable insight that improves both patient outcomes and operational performance.

Mehmet Kazgan is the Founder and CEO of cliexa, the Denver-based healthcare AI company he launched in 2016. His path to founding cliexa runs through some of the most influential names in technology and healthcare — Adobe, Google, Microsoft, PayPal, Visa, and Aetna — where he learned to build at consumer scale while mastering the exacting standards of financial services and the realities of how care actually gets delivered.

It was his time in executive leadership at Aetna that proved formative. Working inside one of the nation's largest health insurers, Mehmet saw firsthand where healthcare breaks down: the gap between what clinicians know is right for a patient and what the system will recognize, approve, and pay for. That vantage point — sitting where medicine, coverage, and technology collide — became the founding insight behind cliexa. He built the company to close that gap, giving clinicians the clarity to know what's medically necessary and appropriate at the moment care happens, rather than after the fact. 

Under his leadership, cliexa earned selection into the Mayo Clinic Platform_Accelerate program, forged a co-development partnership with the American College of Cardiology, and delivered its flagship enterprise deployment with ERC Pathlight. A frequent speaker, mentor, and advisor across digital health, Mehmet has built cliexa into a company whose partners include the American College of Cardiology, ERC Pathlight, Columbia University, Astellas, and more.

As healthcare AI evolves from tools that generate recommendations to agents capable of accessing data and taking action across systems, governance has to evolve with it.In this episode of The Beat's AI at ViVE series, host Sandy chats with Corinne Pellows, the Director of product management at Rhapsody about the growing need for real-time AI governance.

They explore why written policies alone aren't enough, how health systems can manage AI agent permissions and data access, and where human oversight belongs in increasingly autonomous workflows. Corinne also discusses Rhapsody's Guardian platform and how organizations can build flexible safeguards that evolve alongside AI.

Remember, responsible AI requires more than setting policies. It requires putting them into action.

In this episode, they talk about:

• Why AI agents introduce new governance risks when they move from advice to action
• How to manage AI agent permissions like employee access privileges
• Why written AI policies need real-time technical enforcement
• How Rhapsody's Guardian platform supports AI and API governance
• Why healthcare organizations need visibility into what data AI agents access
• When human oversight is essential and when it can create unnecessary friction
• How risk-based policies can evolve alongside AI technology
• Why effective AI governance must work with existing healthcare infrastructure 

A Little About Corinne:

Corinne Pellows is Director of Product Management at Rhapsody, bringing more than a decade of experience in healthcare interoperability and care coordination. She focuses on helping healthcare organizations securely connect data, embrace emerging AI technologies, and put practical governance safeguards in place. Her work bridges healthcare's interoperability foundations with the next generation of AI-powered workflows.

Healthcare and life sciences have a unique challenge when it comes to using AI for marketing, sales, and communications: accuracy isn't a nice-to-have. It's a must-have.

In this episode of The Beat's AI at Health series, host Sandy Vance talks with Divya Srungaram, VP of Healthcare Solutions at WRITER, about how AI agents are helping healthcare organizations accelerate highly regulated workflows without sacrificing compliance, accuracy, or human oversight.

Divya shares why healthcare marketing campaigns can take nine to 12 months to move from brief to launch, and how AI agents can embed legal and compliance requirements directly into the content creation process, reducing the back-and-forth that traditionally slows teams down.

Some of the key takeaways include: organizations shouldn't start by trying to automate every step of an existing process. Instead, they should start with the outcome they need to achieve and then determine how AI can orchestrate the work.

As healthcare organizations move from experimenting with AI to deploying it at scale, Divya sees an inflection point ahead, one where domain expertise combined with AI fluency could become a significant competitive advantage.

In this episode, they talk about:

• Why healthcare content creation is so slow and how AI agents can change the equation
• Building compliance and legal requirements directly into AI workflows
• Using checker agents and traceability to reduce AI risk
• How AI can accelerate complex Medicaid RFPs
• Why organizations should start with outcomes rather than existing processes
• The importance of data discipline and a clear source of truth
• Preparing employees for roles that will change as AI adoption grows
• Measuring AI ROI through speed, revenue, sales effectiveness, and efficiency
• How AI can make healthcare communications more empathetic and easier to understand
• Why healthcare organizations may be approaching an AI inflection point

A Little About Divya:

Divya Srungaram is Vice President of Healthcare Solutions at WRITER, where she leads the healthcare vertical, helping health plans and providers deploy generative AI to drive measurable impact across commercial and operational workflows.

She is a healthcare AI executive with nearly two decades of experience transforming payer and provider operations through generative AI, machine learning, and advanced analytics. She brings deep expertise across claims, utilization management, care management, and risk adjustment, and has served as a trusted advisor to multi-billion-dollar health plans on enterprise AI strategy, responsible AI governance, and large-scale implementation. Her work focuses on translating data and AI into actionable insights and solutions that help health plans better understand employer populations, manage cost trends, and drive improved engagement and outcomes across Commercial and Medicare lines of business.

Divya was a leader in the Healthcare Strategy & Analytics practice at Deloitte, where she advised large healthcare organizations on transformation and led the development of AI-driven products and data platforms. She later served as VP of Product at Blue Health Intelligence, where she led the Employer Analytics platform supporting multiple Blue Cross Blue Shield plans and millions of members. Most recently, as SVP of Provider Growth and Product at Allymar Health, she led product strategy and growth for Medicare solutions, developing AI-enabled capabilities to improve documentation quality, risk adjustment accuracy, and clinical workflows.

She holds a Bachelor’s degree in Computer Systems Engineering from Massey University (New Zealand) and an MBA from Harvard Business School.

Millions of people struggle with behavioral health conditions that remain undiagnosed or undertreated. AI could help find those patients sooner and connect them to more effective care.

In this episode of the AI at ViVE series on The Beat, host Sandy Vance talks with Jeremy Kreyling, the SVP of Healthcare Informatics at NeuroFlow, about where artificial intelligence genuinely belongs in behavioral health and where clinical judgment and human connection remain irreplaceable.

Jeremy explains how NeuroFlow uses machine learning across claims, EHR, assessment, and engagement data to identify hidden behavioral health risk, and why models built around an organization's own population can provide greater confidence than a one-size-fits-all approach. They also discuss how AI can help reduce the number of patients lost between identification, referral, and effective treatment.

In this episode, they talk about:

• How AI can identify behavioral health needs that haven't yet been diagnosed
• Why healthcare AI models should reflect the populations they're designed to serve
• How machine learning can make behavioral health outreach more targeted
• Why finding a provider isn't enough if the patient doesn't stay in care
• How AI agents can take administrative work off highly trained care managers
• Why behavioral health still lags physical health in analytics and integration
• How measurement-based care can identify when treatment isn't working
• Why AI should create more meaningful patient care, not simply more patient volume

A Little About Jeremy:

Jeremy Kreyling is the SVP of Healthcare Informatics at NeuroFlow, the market leader in behavioral healthcare technology infrastructure, where he leads analytics strategy to integrate behavioral health into value-based care models. He brings more than 20 years of leadership in healthcare data and analytics, with prior roles at NOCD, ProgenyHealth, and Change Healthcare. Jeremy holds an MBA from the University of Arizona and a B.S. in Information Systems from Arizona State University. 

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 staff
• Why passwordless projects stall: what works in the back office breaks on shared clinical workstations
• How Twosense.AI makes authentication fully passive and invisible using behavioral biometrics
• The 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 investigation
• The case study: 17,000 users, 90 days, 89% fewer login failures, 79% fewer help desk tickets, 100% of sessions protected
• How the rollout happened passively in the background, with zero change management pain
• Building an AI native security product, from research prototype to enterprise scale
• The next frontier: verifying AI agents, not just humans, on a user's session
• Twosense.AI's new SSF/CAEP support for broadcasting identity risk across the enterprise
• Dawud'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.

On today’s episode, host Sandy Vance chats with Guillaume de Zwirek, CEO & Co-Founder of Artera. Artera is pioneering a new category in healthcare AI by pairing its agentic platform with dedicated AI Service Squads that build custom solutions directly alongside healthcare providers, solving urgent workflows from day one. With over a decade of experience, 2 billion annual patient interactions, and 1,000+ healthcare organizations served, Artera is proving that the future of healthcare AI isn't just about the technology. It's about having the right builders working directly with providers to strengthen how they communicate and care for patients.

Healthcare organizations are under pressure to improve patient experience while reducing administrative burden and staffing challenges. In this episode of The Beat AI Series, Sandy Vance sits down with Guillaume de Zwirek, CEO and Co-Founder of Artera, to discuss how agentic AI is transforming patient engagement across the healthcare journey.

From scheduling appointments and managing referrals to automating complex patient communications, Guillaume explains why AI is about far more than chatbots. He shares how Artera has evolved over the past decade, why healthcare organizations need a security-first approach to AI, and how intelligent automation is helping providers deliver better patient experiences while improving operational efficiency.

Whether you're a healthcare executive, CIO, digital health leader, or AI innovator, this episode offers practical insights into what it really takes to deploy AI successfully in healthcare.

In this episode, they talk about:

• Why patient experience remains one of healthcare's biggest unsolved problems
• How agentic AI is changing patient communication beyond simple chatbots
• Why healthcare organizations must rethink their entire operating model for AI
• The importance of security-first AI in highly regulated industries
• How Artera automates scheduling, referrals, billing, and patient engagement
• Why AI should augment human expertise instead of replacing it
• The role of foundation models in healthcare AI
• How customization at scale is becoming possible with AI
• Why healthcare leaders should focus on solving domain-specific problems rather than building their own large language models
• What separates true AI-native companies from organizations simply adding AI features
• How intelligent automation improves both patient satisfaction and operational efficiency

A Little About Guillaume:

Guillaume de Zwirek, CEO and co-founder of Artera, is at the forefront of the AI-first revolution in healthcare communications. With a decade of expertise and two billion annual patient communications, Artera is the proven leader in agentic healthcare. 

Artera bridges the gap between human staff and AI Agents to fix patient communications across channels – text, phone, and web. The platform is designed to unify the entire patient journey, with the power to streamline everything from initial scheduling to post-visit billing. Trusted by over 1,000 healthcare organizations and federal agencies, Artera drives staff efficiency and improves provider outcomes, ensuring patients get the care they need with simplicity and speed. 

Prior to founding Artera in 2015, Guillaume worked at Google and Graphiq.com (acquired by Amazon). He is a graduate of the University of Southern California with a B.A. in Classical Guitar Performance and a B.S. in Business Administration. A six-time Ironman athlete, Guillaume applies the same discipline and dedication to building a future where healthcare communication is intuitive, efficient, and AI-driven.

Healthcare organizations have more data than ever before, but turning that data into actionable insights remains one of the industry's biggest challenges. In this episode of The Beat AI in Healthcare Podcast, host Sandy Vance sits down with Nabil Jallouli, CEO and co-founder of Rollstack, to discuss how AI-powered reporting is helping healthcare organizations simplify value-based care reporting, improve decision-making, and scale personalized reporting without overwhelming their teams.

Nabil shares why dashboards alone are no longer enough, why executives need trusted narratives instead of raw data, and how healthcare organizations can automate reporting while maintaining governance, security, and accuracy. Whether you work in healthcare leadership, data analytics, customer success, value-based care, or digital transformation, this conversation offers practical insights into using AI responsibly to improve reporting and business outcomes. 

In this episode, they talk about:

• Why value-based care requires an entirely different approach to healthcare reporting
• The hidden "last mile" problem between business intelligence dashboards and executive decision-making
• Why healthcare organizations struggle with manual reporting despite major investments in analytics platforms
• How AI can automate repetitive reporting while keeping humans focused on strategy and relationships
• The importance of governance and trusted data in healthcare AI
• Why executives rarely spend more than a few seconds reviewing dashboards
• How personalized reporting improves payer, provider, and customer relationships
• The role of automation in scaling reporting across hundreds of healthcare partners
• Measuring ROI from AI reporting solutions and why reporting automation often delivers significant business value
• Why healthy skepticism around AI is actually helping healthcare adopt better long-term solutions
• How Rollstack protects sensitive healthcare data with enterprise-grade security and zero data retention policies
• What healthcare leaders should prioritize as AI adoption continues to accelerate

A Little About Nabil:

Nabil Jallouli is the co-founder and CEO of Rollstack, an enterprise platform helping companies like SoFi, Zillow, and Whirlpool automate their data-driven presentations and documents. Rollstack leverages deep integrations with leading business intelligence tools (Tableau, Power BI, Looker, and others) and AI to streamline reporting processes, including business reviews, EHR and HCP reporting, financial reporting, and client presentations. By eliminating manual copy-paste, reducing errors, and ensuring accuracy, Rollstack enables organizations to operate more efficiently and focus on decision-making rather than document preparation. The company is backed by Y Combinator and Insight Partners and is fully SOC 2 Type II and HIPAA compliant, meeting the highest standards of security and data protection for enterprise and healthcare customers.

Before founding Rollstack, Nabil built his career leading data analytics and revenue strategy teams at Pinterest, Deel, and Groupon. Across these experiences, he observed a common problem: highly skilled professionals spending countless hours preparing slides and reports instead of focusing on high-value work and strategy. This recurring pain point ultimately inspired the creation of Rollstack.

Nabil holds a Master of Engineering and dual Bachelor’s degrees in Mathematics, Physics, and Computer Science from École Centrale Paris.