In this AMDIS Roundtable, Dr. Eve Cunningham (Cadence), Dr. Howard Landa (Adventist Health), and Dr. Deepti Pandita (UCI Health) explore what it really means to build an AI-ready EHR. Moving beyond features and vendor roadmaps, they examine how data governance, workflow design, and clinical validation shape the physician's day — from inbox overload to exam-room disconnect. Together, they discuss shadow AI, cognitive burden, and the hard tradeoffs between optimizing legacy workflows and redesigning care. Their message is clear: the next generation EHR isn't about adding more technology — it's about restoring clinical cognition and rebuilding trust in how care gets delivered.

• Bad data leads to bad AI — governance must come first.
• AI is math, not magic — noisy and inconsistent data amplify risk.
• Clinical validation is as important as data hygiene for meaningful AI use.
• The EHR disrupts connection when clinicians must focus on screens instead of patients.
• Ambient documentation restores presence but does not yet create a true intelligent clinical partner.
• Most AI tools solve isolated tasks, not the full end-to-end care workflow.
• Health systems need internal prompt and AI literacy — clinicians are not trained engineers.
• Third-party innovation fills gaps, but long contracts slow adaptation.
• Shadow AI often signals unmet needs, not noncompliance.
• Governance should enable safe experimentation rather than block innovation.
• Clinicians remain accountable for AI-driven decisions under current regulations.
• Patients are rapidly adopting AI tools, often without reliable guardrails.
• AI's highest near-term value is reducing cognitive and administrative burden.
• True progress requires redesigning workflows, not endlessly optimizing legacy processes.
• Leadership prioritization, not technology limits, often slows transformation.
• The next generation EHR must restore clinical cognition and trust.

00:00 | The Key to a Successful EHR: Restoring Clinical Cognition
02:36 | When the EHR Slows Care Instead of Supporting It
01:08 | Why This Isn't About Features or Vendor Roadmaps
02:36 | When the EHR Slows Care Instead of Supporting It
03:46 | AI Is Math, Not Magic
04:05 | Data Governance as the Prerequisite for AI
05:23 | Governance + Clinical Validation = Actionable Intelligence
06:44 | The Keyboard Breaks the Sacred Patient Encounter
08:25 | Ambient Tools and the Return of Joy in Medicine
09:19 | The "Intelligence-Ready" EHR Vision
11:10 | Why Clinicians Aren't Prompt Engineers
12:39 | Is Epic Cosmos Ready for Prime Time?
14:01 | Contextual, Specialty-Specific Views at the Point of Care
15:08 | Third-Party Innovation vs EHR Vendor Lag
16:49 | When to Replace vs Layer New AI Tools
18:09 | Shadow AI as a Signal of Unmet Need
19:08 | Enabling Safe Experimentation Through Governance
20:55 | If AI Makes a Mistake, Who Is Liable?
23:35 | Where AI Should Help First: The Patient Journey
25:19 | Patients Are Already Using AI for Medical Advice
27:33 | Cognitive Scaffolding: Removing Clinical Noise
28:31 | Intelligent Synthesis vs Data Mining
29:13 | The Peer Challenge: How Do We Move 2–3x Faster?
30:02 | Leadership Indecision Slows Transformation
30:47 | Stop Optimizing Legacy Workflows
31:46 | Final Message: Technology Isn't the Barrier Anymore

Guests: Dr. Eve Cunningham, Dr. Howard Landa, Dr. Deepti Pandita
Host: Gregg Malkary - Lighthouse Healthtech
Cohost: Keith Washington - Companions in Courage Foundation
Sponsored by: Simplifi Medical & Storage Systems Unlimited
Audio/Video: Tim Jones - Health Nuts Media
Marketing: Josh Troop - Troop-Creative
Learn More at: www.beyond-blueprint.com

In this AMDIS collaboration, Dr. Mark Pierce (former Parkview Health), Dr. Howard Landa (Adventist Health), and Dr. John Lee (HIT Peak Advisors) examine why data governance is ultimately a patient-care issue, not just an IT function. Drawing on real bedside decisions, EHR transitions, and conflicting metrics, they show how unclear definitions and hidden data create hesitation, workarounds, and loss of clinician trust. Together, they discuss the leadership commitment and investment required to improve data quality, and why clean data is the prerequisite for safe and meaningful AI in healthcare.

• Unclear data definitions slow care — clinicians pause, double-check, or guess at the bedside.
• Data governance is a patient-safety function, not an IT committee exercise.
• Most data problems are silent — staff create workarounds instead of reporting issues.
• Different departments define the same metric differently, eroding organizational trust.
• Leadership wants fast ROI, but governance requires long-term investment and persistence.
• AI magnifies bad data — clean inputs matter more than advanced algorithms.
• Use AI first to clean and structure data before deploying clinical AI tools.
• Start governance with visible problems (e.g., length-of-stay definitions, data sharing).
• Tie governance efforts to active initiatives to gain momentum and participation.
• Organizations rarely advertise good governance — you see it in outcomes, not org charts.
• Clinician trust is fragile; one bad data-driven decision can last for years.
• Accessible, synthesized data enables faster decisions and real improvement cycles.

00:00 | Why Data Governance Gets Ignored
02:01 | Data Governance as a Patient-Safety Issue
04:25 | When Data Helps — and When It Slows Care
06:29 | Too Much Information, Not Enough Clarity
09:14 | The "Raiders of the Lost Ark" Data Warehouse Problem
11:04 | Why Leaders Delay Governance Work
13:06 | The Real Investment Required
14:28 | The PD-Not-PDSA Cycle in Healthcare
15:16 | AI Fails Without Clean Data
16:55 | What Breaks When Definitions Differ
18:05 | Bad Data at the Bedside: A Stroke Decision
20:19 | How Trust in Data Is Lost
23:13 | Silent Workarounds Across Hospitals
25:18 | Where Organizations Should Start
27:15 | Making Data Actionable for Clinicians
29:26 | Governance as Organizational DNA
31:24 | Aligning AI Governance With Data Governance
32:13 | Use AI to Clean Data First
34:15 | Bias, Training Data, and Clinical Risk
36:29 | Final Message: When Rules Are Unclear, Care Suffers

Guests: Dr. Mark Pierce, Dr. Howard Landa, Dr. John Lee
Host: Gregg Malkary - Lighthouse Healthtech
Cohost: Keith Washington - Companions in Courage Foundation
Sponsored by: Simplifi Medical & Storage Systems Unlimited
Audio/Video: Tim Jones - Health Nuts Media
Marketing: Josh Troop - Troop-Creative
Learn More at: www.beyond-blueprint.com

In this episode, we're joined by two leaders from Sutter Health who are scaling ambient listening across their enterprise: Dr. Veena (Goel) Jones, Chief Medical Informatics Officer, and Stephanie Driscoll, Vice President of Digital Implementation.

In 2025 alone, Sutter onboarded more than 3,100 clinicians to ambient listening—moving well beyond pilot programs to true system-wide adoption. Together, they share what it takes to make that scale possible, from EHR integration and workflow redesign to governance, training, and change management.

• Ambient listening only scales when the EHR foundation is ready. Workflow standardization and template cleanup made enterprise adoption possible.
• In 2025, Sutter onboarded more than 3,100 clinicians—moving ambient listening beyond pilot and into system-wide infrastructure.
• AI adoption is operational, not just technical. Governance, enrollment processes, and structured onboarding determined success.
• Clinicians pulled the technology. Ambient listening spread because it reduced cognitive burden—not because it was mandated.
• Full Epic integration eliminated friction and accelerated both ambulatory and inpatient adoption.
• Ambient listening is a gateway. Documentation is just the beginning—queued orders and future AI workflows depend on foundational readiness.

00:00 | Enterprise Change Management and Ambient Listening
01:30 | Welcome to Beyond the Blueprint
02:30 | Why Ambient Listening Is More Than Hype
03:25 | Meet the Leaders: Sutter Health's AI Scale Strategy
05:00 | Early Physician Reactions: "This Changed My Life"
06:05 | From Pilot to Enterprise Enrollment
07:40 | Standardizing Epic to Prepare for AI
09:20 | Governance, Safety, and Clinical Oversight
11:00 | Integrating Ambient Listening Directly into Epic
13:30 | The Role of the Digital Academy in Scaling Adoption
15:45 | Reducing Cognitive Burden at the Bedside
18:00 | Scaling Across Ambulatory and Inpatient Settings
20:30 | Structured Intake and Monthly Onboarding Waves
23:00 | Protecting Coding Integrity and Revenue Cycle
27:15 | Beyond Documentation: Orders and Workflow Automation
29:00 | Measuring Impact: Charting Time and Pajama Time
31:45 | Why Clinicians Pulled the Technology
34:00 | Preparing the EHR Foundation for Future AI
36:30 | What It Really Takes to Move Beyond the Pilot
38:50 | Closing Thoughts: Ambient Listening as Infrastructure