In Part 2 of this episode of The IDAAHub Podcast, host Deepti Kalghatgi and Dr. John Green — trauma & acute care surgeon, Professor of Surgery at Wake Forest University School of Medicine, and Medical Director at Atrium Health — move from the problem to the possibilities: how and where AI is already advancing trauma and critical care, and where it's headed next.

From the ICU to the operating room, this half explores the places AI can do the most good — and the human moments it should never replace.

In this episode:

• Critical care monitoring as the "low-hanging fruit" — turning endless ICU data into early warnings
• Beating alarm fatigue and predicting patient deterioration hours before it shows
• Robotic surgery, economy of motion, and AI-driven performance feedback for surgeons
• The human elements AI must not replace — ethical decisions and delivering hard news to families
• The future: wearables that alert EMS, smarter regional trauma systems, and chest-wall/rib-fracture modeling
• The stat most people don't know: trauma is the #1 killer from ages 1–44 — and it's dramatically underfunded

🎧 New here? Start with Part 1 for the full story on why trauma is AI's toughest frontier.

📧 Connect with IDAAHub Follow us on: LinkedIn: https://www.linkedin.com/company/idaahub/ YouTube: https://www.youtube.com/@IDAAHUB Spotify: https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx Apple Podcasts: https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

📧 Connect with Host Host Deepti Kalghatgi: https://www.linkedin.com/in/deepti-kalghatgi/ 🌐 Visit: https://idaahub.com

In Part 1 of this episode of The IDAAHub Podcast, host Deepti Kalghatgi sits down with Dr. John Green — trauma & acute care surgeon, Professor of Surgery at Wake Forest University School of Medicine, and Medical Director at Atrium Health — to explore the last frontier AI hasn't reached: acute trauma care.

When a critically injured patient arrives, doctors often have half the information they need and no time to wait for the rest. Dr. Green calls it "putting a puzzle together without the box." In this first half, Deepti and Dr. Green dig into why the technology transforming so much of medicine still can't help in the moments that matter most.

In this episode:

• Why AI has reshaped the EMR and the clinic — but not the first critical minutes of trauma
• The "golden hour" and making life-or-death calls on incomplete data
• Human intuition vs. algorithms — and what Malcolm Gladwell's "Blink" teaches trauma surgeons
• Dr. Green's vision for bridging the data gap between EMS in the field and the trauma bay
• How robotics and real-time monitoring are beginning to open the door for AI in surgery

🎧 Listen to Part 2 to hear how and where AI is already making trauma care better.

📧 Connect with IDAAHub Follow us on: LinkedIn: https://www.linkedin.com/company/idaahub/ YouTube: https://www.youtube.com/@IDAAHUB Spotify: https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx Apple Podcasts: https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

📧 Connect with Host Host Deepti Kalghatgi: https://www.linkedin.com/in/deepti-kalghatgi/ 🌐 Visit: https://idaahub.com

📧 Connect with Host Host Deepti Kalghatgi: https://www.linkedin.com/in/deepti-kalghatgi/

 the COVID-era crisis that made their work urgent, the platform pivot that came straight from a customer complaint, the specific investor moment that unlocked their biggest funding rounds, and why they've pulled so far ahead of that same-year competitor.

What we cover:

• Why Elad Walach says there was no single "aha moment" — and what actually drove the founding
• The real, independently studied impact of AI on COVID-era imaging backlogs (a 30% cut in report turnaround time)
• aiOS: the platform decision that turned Aidoc from a vendor into infrastructure
• Why four health systems investing their own capital mattered more than any VC check
• Aidoc vs. Viz.ai — same founding year, two very different bets
• Three lessons for anyone building or buying clinical AI today

Sources:

• Authority Magazine, BackTable Industry Podcast, Alantra Podcast (Elad Walach interviews)
• Fierce Healthcare, Radiology Business, MedCity News, Healthcare Dive, Ctech/Calcalist, Tech Funding News
• Axis Imaging News (Moscow Center for Diagnostics & Telemedicine, ECR 2021)
• Philips 2025 Future Health Index
• CB Insights, Docus.ai, MytheAi

Got a startup you think belongs in this series? Reach out — I read every message.

📧 Connect with IDAAHub Follow us on: LinkedIn: https://www.linkedin.com/company/idaahub/ YouTube: https://www.youtube.com/@IDAAHUB Spotify: https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx Apple Podcasts: https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

In this episode, I dig into two developments happening right now: health systems are getting far more disciplined about how they evaluate AI (shifting from responding to vendor pitches to running data-driven projects), and healthcare investors are arguing AI is the first tech wave in the industry that's actually generating new revenue, not just cutting costs.

What we cover:

• Why Arcadia CEO Michael Meucci says health systems should find their own inefficiencies before taking a single vendor meeting
• Why replacing a physician costs roughly $1M — and why that number belongs in your ROI math
• The investor panel (Kaiser Permanente Ventures, CVS Health Ventures, Invidia Capital Management, Hippocratic AI) behind the widely-cited "100x return" claim
• Four real, sourced lessons for anyone trying to pitch AI ROI credibly right now

Sources:

• MedCity News: "How Are Health Systems Assessing ROI for AI Tools?" (Mar 2026) — medcitynews.com/2026/03/health-system-hospital-ai-roi
• MedCity News: "Healthcare Investors Are Rewriting the ROI Playbook from Cost-Cutting to Cash Flow" (Jul 2026) — medcitynews.com/2026/07/healthcare-investors-medcity-ai
• Becker's Hospital Review: "700 Lives, $100M Saved: Healthcare AI ROI in '25" (Jan 2026)
• Premier Inc.: "Redefining AI ROI in Healthcare" (May 2026)
• Healthcare IT News: "AI with human support reduces no-shows at Graybill" (Jul 2026)

Got thoughts on how your organization measures AI ROI? Reach out — I read every message.

📧 Connect with IDAAHub 

Follow us on:

LinkedIn: https://www.linkedin.com/company/idaahub/

https://www.youtube.com/@IDAAHUB

https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx

https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

📧 Connect with Host Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/

🌐 Visit: https://idaahub.com

📧 Connect with Host 

Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/

🌐 Visit: https://idaahub.com

In this episode of the Innovation & Startup Series, we unpack Tempus AI and Valar Labs — how each was built, how each grew, and three lessons any founder can borrow.

Tempus, founded by Groupon co-founder Eric Lefkofsky after his wife's cancer diagnosis, went all-in on owning the whole stack — genomics, clinical data, labs, and AI — and now does over $1.27B in annual revenue. Valar Labs went the opposite way: one sharply focused question — will this treatment work for this patient? — answered by running AI over routine pathology slides that already exist, built by seven engineers who optimized everything for one thing: speed of iteration.

We dig into why capital intensity drives the broad-vs-niche decision, why data is the real moat (and the two ways to win it), and why fast iteration toward validated evidence beats a perfect initial design.

What you'll learn:

• How Tempus and Valar chose broad vs. niche — and the role capital played
• Why proprietary data is the moat, whether you generate it or unlock it
• Why speed of iteration is the quiet engine behind both companies
• How a seven-person team out-ships rivals many times its size

Sources & further reading:

• Valar Labs — "5 Years, 7 Engineers, No Cloud": https://www.valarlabs.com/engineering/7-engineers
• Valar Labs — $22M Series A: https://www.valarlabs.com/news/series-a
• Tempus AI: https://www.tempus.com

If you enjoyed this, follow the show and share it with someone building in healthcare or AI.

📧 Connect with IDAAHub 

Follow us on:

LinkedIn: https://www.linkedin.com/company/idaahub/

https://www.youtube.com/@IDAAHUB

https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx

https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

📧 Connect with Host
 Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/
🌐 Visit: https://idaahub.com

We cover the real founding story (it started with a cardiologist, a patient, and one sentence about dignity), how Abridge grew faster than long-standing incumbents like Nuance, what just happened with Abridge's Nvidia and Eli Lilly partnerships, and why this isn't really a scribe company anymore — it's trying to become the operating system for how care gets delivered and paid for.

We close with three takeaways every startup founder can learn from Abridge's playbook: why being first to market matters less than being first to solve the real problem, why credible partners can matter more than features, and why trust has to come before product in healthcare AI.

🎙️ Topics covered:

• The real Abridge origin story
• Why Abridge grew faster than Nuance
• Abridge's Nvidia + Eli Lilly partnership news
• 3 startup lessons every founder should know

📧 Connect with IDAAHub
 Follow us on:
 LinkedIn: https://www.linkedin.com/company/idaahub/
https://www.youtube.com/@IDAAHUB
https://open.spotify.com/show/3V8Vuhqkibej5fUwtwkUMx
https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

🧠 What you'll learn in Part 2:
→ What patients can realistically do with their health data right now — and where it still falls short
→ How LLMs are turning a 5,000-page records request into an instant, queryable health profile
→ Why EMRs are now in a difficult spot: penalized if they don't open up data, disrupted when they do
→ The 25-year arc of healthcare data: from copying blood pressures row-by-row into Excel, to HL7, to data lakes, Tableau, and AI — and why the core problems of normalization and deduplication never actually went away
→ How Dr. Martens built Mark Ventures as a venture studio, evolved it into an investing syndicate, and joined Picap Fund as General Partner
→ Why having a clinician on a VC investment committee changes which bets get made — and which ones don't

The episode closes with Dr. Martens' transition from clinician to investor: founding Mark Ventures as a venture studio to build and advise healthcare technology companies, evolving it into an investing syndicate as founder demand for capital outpaced demand for advice, and ultimately joining Phycap Fund as General Partner — where his clinical domain expertise directly shapes the fund's investment decisions.

Guest: Dr. Timothy Martens — Congenital Heart Surgeon, Northwell Health | Director of Data Strategy & Innovation, Cohen Children's Medical Center | General Partner, Phycap Fund
Host: Deepti — Founder, IDAAHub | IDAAHub Podcast: AI in Finance & Healthcare
[Part 1 available in the previous episode]

On April 22, 2026, Community Health Systems reported a $58 million net loss for Q1 — on nearly $3 billion in revenue. Leadership pointed to two compounding pressures: a challenging payer mix with fewer commercial patients, and a slow, steady ramp-up in claim denials that nobody's system was surfacing until it was too late.

Three weeks earlier, Kodiak Solutions published the most comprehensive analysis of hospital revenue cycle performance ever conducted — analyzing 2,300 hospitals. The number they found: hospitals lost more than $48 billion in revenue in 2025 from claim denials and uncollected bills. A 25% increase from the prior year. And the increases were specifically for lack of prior authorization and for medical necessity.

In this episode we break down exactly why hospitals running Cerner EMR are structurally exposed to these gaps — from the split between clinical and revenue cycle data models, to the inability to track true denial and appeal overturn rates, to the lack of machine learning needed to surface payer-specific denial patterns before they become systemic revenue leaks.

We then explain Bloom Value's patented enterprise visibility system — US Patent 20230260638 — and what cooperative machine learning engines, simultaneous real-time and historical data processing, and role-specific financial dashboards actually mean for a CFO trying to see where the money is going.

As AI systems in financial services grow increasingly sophisticated—from traditional machine learning to generative AI and agentic systems—the question of guardrails becomes critical. In this episode, Kelly Cochran breaks down what responsible AI deployment really means in credit underwriting and financial services.

In This Episode:

Kelly explains two essential categories of AI guardrails: protecting data throughout its lifecycle and ensuring analytics models perform as expected. While machine learning models for credit underwriting have established transparency tools, generative AI models present entirely new challenges.

We explore the fundamental differences between ML models trained on curated financial data and large language models trained on vast swaths of the internet. Kelly discusses the transparency challenges of non-deterministic models—systems that might give slightly different outputs for the same inputs—and the serious implications of AI "hallucinations" in financial contexts where consistency is paramount.

The conversation turns to practical safeguards, particularly human-in-the-loop approaches. Kelly shares insights on the delicate balance required: keeping human reviewers engaged without becoming overly reliant on or skeptical of AI recommendations. We discuss how these safeguards must evolve as systems mature, balancing thoroughness with efficiency.

Perhaps most exciting is Kelly's vision for personal financial agents—AI assistants that could democratize access to quality financial planning. Currently, only 40% of U.S. adults work with a financial planner, dropping to just 20% among low-to-moderate income households. AI agents could provide personalized, accurate financial guidance to millions who can't afford traditional advisory services. From day-to-day expense management to long-term goal planning—even filing taxes—these tools could transform financial inclusion.

But Kelly emphasizes the critical importance of getting this right. When serving financially vulnerable populations, malfunctioning AI agents could make situations worse, not better. This requires rigorous testing, thoughtful design, and appropriate guardrails to ensure reliability.

Kelly also offers practical advice for consumers looking to improve their credit access, highlighting the growing use of cash flow data by lenders as an alternative or supplement to traditional credit scores.

Guest Bio: Kelly Cochran is Research Director at FinRegLab, a nonprofit research organization fostering data-driven dialogue about financial innovation. Her work focuses on ensuring technological advances in financial services benefit all consumers equitably and safely.

Resources Mentioned:

• FinRegLab research on cash flow underwriting
• Studies on AI transparency in credit decisions
• Consumer guidance on alternative credit data

Host: Deepti Kalghatgi, Chief Curator at IDAA Hub and VP of Sales & Partnerships at Cogniquest AI

The IDAA Hub Podcast explores the intersection of AI, finance, and healthcare, featuring conversations with industry leaders driving intelligent automation in enterprise operations.

Episode Timestamps: 00:00:24 - What guardrails do complex AI systems need? 00:00:40 - Two types of guardrails: data and analytics 00:01:18 - Difference between ML models and Generative AI 00:02:22 - Non-deterministic models explained 00:02:59 - The hallucination problem in financial services 00:03:42 - Transparency challenges with GenAI 00:04:02 - Human-in-the-loop strategies and limitations 00:15:14 - Personal financial agents: game-changing opportunity 00:15:50 - Access to financial planning statistics 00:17:25 - Financial stability and economic participation 00:18:03 -  Closing thoughts 

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In Part 2 of our conversation with Kelly, we dive deep into the world of agentic AI systems in financial services. These aren't simple chatbots or standalone AI models—they're sophisticated hybrid platforms combining large language models, machine learning algorithms, and autonomous software agents that can pull data, analyze patterns, and take action in real-time.

Kelly breaks down what makes these systems fundamentally different and why financial institutions are deploying them aggressively for internal use (particularly fraud detection and deep fake mitigation) while taking a much more cautious approach with consumer-facing applications.

EPISODE TIMESTAMPS:

[00:21] Introduction: What are agentic AI systems?

[00:28] Breaking down the technology: Why we say "agentic AI systems" not just "AI agents"

[00:40] The architecture: Multiple elements, software agents, and different tasks

[00:58] Beyond financial services: Where else agentic systems are deployed

[01:04] The role of large language models in orchestration and user interface

[01:14] Machine learning for deep quantitative analytics

[01:21] Why hybrid systems can do things individual models can't

[01:37] The autonomy difference: From reactive to dynamic real-time action

[01:52] Fraud detection use case: Fighting deep fakes and emerging threats

[02:08] Appropriate human oversight: Finding the right balance

[02:15] Personal financial assistants: The "pocket advisor" vision

[02:34] Beyond advice: AI executing transactions and managing daily finances

[02:45] The trust question: How reliable are these systems?

[02:57] Current deployment reality: Building with heavy oversight first

[03:17] The exciting potential: Greater dynamism and active support

[03:45] Where we're seeing adoption: Internal vs. consumer-facing

[03:58] Direct-to-consumer caution: Why banks are moving slower

[04:08] Shopping agents: E-commerce AI and the payment connection

[04:31] The "final yes" question: Why consumers still click to buy

[04:46] The future: Computer-to-computer interactions and changing parameters

[20:27] Credit underwriting deep dive: Data quality concerns

[20:39] Transparency and explainability challenges in AI models

[20:55] Comparing systems: Rules-based vs. machine learning vs. human judgment

[21:04] The human "black box": Subjective decisions are hard to pinpoint too

[21:18] Pros and cons: What machine learning improves (and what it doesn't)

[21:46] When things go wrong: Impact on customers, lenders, and the economy

[22:03] Human biases vs. human intuition: The complicated trade-offs

[22:18] Mission-based lending: Working with underserved borrowers

[22:37] High-touch meets high-tech: Marrying traditional relationships with AI

[22:52] The leverage effect: Data and technology expanding credit access

[23:09] Important limitations: What AI alone can't solve

[23:39] Processing benefits: Speed, efficiency, and consistency

[23:56] Machine learning nuances: Capturing patterns simple models miss

[24:10] Reliability concerns: Sensitivity to data changes

KEY TAKEAWAYS:

✅ Agentic AI systems combine LLMs, ML models, and software agents for autonomous action
 ✅ Internal fraud detection deployment is moving fast; consumer apps more cautiously
 ✅ Shopping AI exists now, but humans s

In Part 1 of this conversation, Kelly Cochran drops a truth bomb that changes everything: Millions of creditworthy people are getting rejected for loans right now—not because they won't pay them back, but because traditional credit systems can't evaluate them efficiently.

Kelly Cochran is Deputy Director and Chief Program Officer at FinRegLab, but her journey to this role gives her a perspective almost no one else has. She was at US Treasury during the 2008 financial crisis, helped draft the Dodd-Frank Act, led the effort to stand up the Consumer Financial Protection Bureau (CFPB), and now leads research on responsible AI deployment in financial services.

What You'll Discover in Part 1:

🎯 AI in Banking Is NOT New "It's been used in things like fraud detection for decades." Most people think AI in finance started with ChatGPT in 2022. Kelly explains why this misconception is causing real problems for innovation and regulation.

💳 The Real Credit Underwriting Problem "There are a lot of applicants who don't have traditional credit records. And it's not necessarily that they're really actually less likely to pay the loan back. It's that traditional underwriting systems can't really evaluate them easily."

The issue? Evaluation COST, not borrower RISK.

Traditional systems reject creditworthy people because:

• Processing alternative data is expensive
• Models are built for conventional credit histories
• Per-applicant evaluation costs are too high
• The system defaults to "can't predict = reject"

Machine learning is changing this equation—and the implications for financial inclusion are massive.

🤖 Generative AI vs. Traditional ML: Why It Matters Kelly breaks down the critical distinction between ChatGPT-style generative AI and the machine learning that's been transforming financial services for decades. They're "different and substantially more complex" from each other, requiring different approaches and risk management.

⚖️ The "Move Fast and Break Things" Problem "In financial services, there's a really high value placed on figuring out what's going to work, making sure things are reliable, not just launching something and fixing it on the fly."

When things break in banking, real people lose homes, businesses, and economic opportunity. This creates unique tension between innovation speed and responsible deployment.

Topics Covered in Part 1:

✓ Kelly's remarkable journey: UNC Chapel Hill → Treasury → CFPB → FinRegLab ✓ FinRegLab's mission: Making data and technology work for financial inclusion ✓ Why AI in financial services has a 30+ year history ✓ The credit underwriting transformation and its impact on consumers and small businesses ✓ Expanding AI applications: fraud detection, customer service, back office operations, financial advice ✓ The privacy, security, and data management challenges ✓ Is AI overhyped? "I think there's a little of both" ✓ The "muddy language" problem: How AI gets described differently to investors, regulators, and consumers ✓ ChatGPT's 2022 launch and the generative AI inflection point ✓ Why generative AI systems are fundamentally different from traditional machine learning ✓ The cultural tension in financial services between excitement and reliability

Episode Tags: AI, artificial intelligence, fintech, financial services, credit underwriting, machine learning, generative AI, ChatGPT, financial inclusion, CFPB, consumer financial protection bureau, regulatory technology, banking innovation, FinRegLab, Kelly Cochran, credit scores, loan underwriting, fraud detection, Dodd-Frank, financial regulation, responsible AI, enterprise AI, banking technology, credit access, economic development, part 1

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https://podcasts.apple.com/us/podcast/the-idaa-hub-podcast-ai-in-finance-healthcare/id1848710327

📧 Connect with Host 

Host Deepti Kalghatgi : https://www.linkedin.com/in/deepti-kalghatgi/

🌐 Visit: https://idaahub.com

Visit https://revcosolutions.com/

Subscribe and leave a review to help other healthcare professionals discover practical AI insights!

#AIinHealthcare #RevenueCycleManagement #HealthcareTechnology #RCM #HealthcareInnovation

From accountability concerns to the need for explainable AI, Dr. Neil shares his insights on why technology companies must take responsibility for their algorithms' decisions—just as traditional media has done for centuries. He reveals that 70-80% of current medical decisions are based on pre-FDA era algorithms and century-old statistics, highlighting both the urgent need for AI innovation and the importance of implementing it ethically.

This episode explores the tension between technological capability and human-centered care, the difference between generalized and personalized AI, and why the sacred physician-patient relationship must remain at the heart of healthcare—even as AI transforms the field.

Whether you're a healthcare professional, tech innovator, policymaker, or patient, this conversation will challenge your assumptions and provide valuable perspectives on the future of AI-enabled medicine.

Note: This is Part 2 of our conversation with Dr. Neil. Listen to Part 1 for the complete discussion on AI in healthcare.

Key Takeaways:

• Why AI companies must accept liability for algorithmic errors
• The importance of explainable AI in clinical decision-making
• How current medical statistics and clinical trials may not be ethically applied globally
• The need for personalized AI assistance rather than general intelligence in healthcare
• What patients should look for when choosing AI-enabled physicians
• Why humanity and ethics must come before efficiency and profit

Keywords/Tags: AI in healthcare, medical AI, healthcare technology, ethical AI, explainable AI, digital health, healthcare innovation, physician-patient relationship, medical ethics, clinical decision support, healthcare algorithms, personalized medicine, FDA approval, clinical trials, health insurance, medical technology, healthcare policy, future of medicine, AI accountability, healthcare AI

Guest Information: Dr. Neil Mukherjee

This episode contains frank discussions about medical decision-making, and healthcare system challenges.

Episode Type: Interview

Explicit Content: No