Community health centers see one in seven Americans — and one in three in rural communities. Yet they operate with tight budgets, fragmented data, a growing shortage of providers, and the same cybersecurity threats facing every health system in the country

In this episode, Jay Modh, Founder and CEO of Intuitive.ai, sits down with Isaiah Nathaniel, CIO and SVP at Delaware Valley Community Health System and CIO Executive Advisor for the National Association of Community Health Systems. Isaiah shares the unfiltered principles driving his transformation strategy — and why trust, not technology, is always his starting point

From early adopter strategies and AI governance to a bold prediction about what the exam room looks like in three years, Isaiah brings the kind of candor and clarity that is rare in healthcare leadership conversations

🏥 Trust first — technology strategy must start with provider trust patient trust and system trust before a single acronym is introduced or adopted

📊 AI is not the data problem — it exposes the data problem — community health centers are data rich but presenting it correctly and governing it responsibly is where the real work happens

🔒 If it does not pass security requirements it does not get to Isaiah's desk — SOC 2 HITRUST bias review and hallucination records are mandatory gates before any AI conversation begins

🧠 No technology is solving governance — governance is a people problem — putting someone in a governance committee does not work if the organization is not ready for the change that governance demands

⏱️ Timeline

0:00 — Introduction — Jay Modh of Intuitive.ai meets Isaiah Nathaniel CIO at Delaware Valley Community Health

0:41 — Why Isaiah is excited about companies like Intuitive helping community health

1:00 — The core principles driving digital transformation at Delaware Valley

1:34 — Trust collaboration and security — the three non-negotiables before any technology

2:27 — Co-design and co-development — building AI with providers not against them

3:06 — Removing access barriers through technology not introducing them

3:47 — The data and interoperability strategy from EHR to population health to AI

5:05 — How Isaiah brings nurses clinicians and operations staff into the vision first

5:33 — Early adopters alpha groups and friendly competition — making AI adoption stick

7:39 — AI is not the problem — data is the problem and here is how Isaiah is solving it

8:55 — How consumer AI like ChatGPT is arriving at the clinic before the system is ready

13:23 — Security is job zero — if it does not pass the gate it does not get reviewed

14:47 — Governance is not a technology problem it is a people and change management problem

16:06 — Isaiah's message to aspiring CIOs — a slow no beats a fast yes

18:36 — Together we achieve apart from that we selfishly perceive — a message to nurses and clinical staff

20:19 — Three-year prediction — no computers in exam rooms wearables ambient AI and saliva-based labs

22:08 — Would Isaiah let a robot perform surgery on him — yes if the error rate is under 5%

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Rural health systems are underfunded, understaffed, and buried in fax machines. Meanwhile, large insurance payers are using automation and AI to deny claims faster than ever — and rural providers often don't have the time or staff to appeal before the deadline passes. The result is money quietly bleeding out of systems that can least afford to lose it.

In this episode of Health Reimagined, host Jon Myer (Myer Media, powered by Ingram Micro) sits down with Dan from InfoCap to talk about intelligent document processing, human-centric automation, and why the very fact that rural hospitals are behind on technology might actually put them in the best position to leapfrog the legacy mess that is slowing everyone else down.

🔗 Learn more at infocap.ai⏱️ Timeline0:00 — Introduction — Why being behind on health IT might be rural health's biggest advantage

0:36 — What InfoCap does — intelligent document processing and human-centric automation

1:46 — How the prior authorization process actually works across healthcare systems

2:34 — Fax machines denial queues and the manual burden still crushing rural providers

4:01 — The appeal deadline trap — rural hospitals bleeding budget because they run out of time

5:44 — A real case — a patient in West Virginia who died while his cancer treatment was denied

7:21 — Payers using AI to deny faster — David vs Goliath and the uneven playing field

10:42 — Is being behind on technology actually an advantage for rural health

11:42 — Slingshotting — how rural hospitals can skip legacy tech and land ahead

13:45 — Can we eliminate denials before they happen — the gold card program explained

16:15 — What good enough looks like for rural facilities that cannot afford a million dollar project

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Everyone is asking "can AI do this?" But that's the wrong question. The right questions are: does it have the right knowledge, the right tools, and clear enough instructions to do the job? And is your enterprise actually ready for what comes after?In this episode, Jon Myer is joined by Christopher and Diego from Epi-use — two experts in AI architecture, governance, and enterprise implementation — for a frank conversation about why so many agentic AI projects fail, what the successful ones have in common, and why the real bottleneck is almost never the AI itself.Topics covered:Why most companies are implementing AI for the wrong reasons and asking the wrong questionsThe difference between generative AI and agentic AI — vending machine vs. sous chefPilot purgatory — why AI projects get stuck and never make it to productionWhy the AI part is usually the easiest part and the foundation is the heavy liftHow governance becomes the enabler not the obstacle when done rightThe difference between human in the loop and human on the loop — and why it matters for designA real-world case study — a bank that ran 350 agentic AI use cases and what actually workedDo we want true autonomy or accountability? The answer is both⏱️ Timeline0:00 — Introduction — Reframing agentic AI as an evolution that demands first principles thinking0:50 — What companies are getting wrong at the very start — the fascination phase2:13 — Is agentic AI truly novel or just a rebrand of what we already had3:03 — The right questions to ask — knowledge context tooling and instruction4:51 — Pilot purgatory — AI projects stuck in QA that never make it to production5:46 — First principles have not changed — what problem are we solving and what outcome do we want8:50 — Are we implementing true agentic AI or just rebranding existing workflows9:59 — Generative AI as a vending machine vs agentic AI as a sous chef11:51 — Model in the loop — when you are really just calling a more sophisticated API13:33 — Where agentic AI adds the most value — exception handling in enterprise workflows16:40 — Governance as an enabler — why dirty data is a governance failure not an AI failure18:19 — What feels familiar and what feels genuinely new about AI adoption today20:29 — The importance of foundation before implementation — and the rush that bypasses it23:29 — 40% of AI projects will fail by 2027 — and the bank that ran 350 use cases25:18 — What actually determines AI success vs quiet failure26:28 — Human in the loop vs human on the loop — why design matters from day one29:41 — Agentic AI is a business project not a technology project31:05 — Do we want true autonomy or accountability? The answer is both🔔 Don't forget to Like, Subscribe, and hit the notification 🔔 ✔ Subscribe: https://www.youtube.com/jonmyer/?sub_confirmation=1📱 Social Media Twitter: https://twitter.com/_JonMyerWebsite: https://jonmyer.comLinkedIN: https://www.linkedin.com/in/jon-myer/Spotify: https://open.spotify.com/show/0wjJzdIwctq4o4pTXM2KSFLike my sounds? Here's my audio source: https://www.epidemicsound.com/referral/fj9o9k#aws #awscloud #podcast #podcasting #costoptimization #finops #cloudcost #cloudoptimization