Sign up to save your podcasts
Or
Share The Signal Room | AI in Healthcare: Strategy & Governance
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
The Signal Room | AI in Healthcare: Strategy & Governance
By Chris Hutchins | Healthcare AI Executive & Governance ConsultantBusinessMedicineHealth & FitnessTechnologyManagement
The Signal Room | AI in Healthcare: Strategy & Governance episodes
- AI and Language Access in Healthcare: Communication Equity and Patient Safety | Carol Velandia
Send us Fan Mail
AI in healthcare cannot replace language access — Carol Velandia on communication equity, patient safety, and the limits of AI strategy for healthcare.
AI in healthcare cannot replace language access, and treating it like it can puts patients at risk. Carol Velandia, Founder of Equal Access Language Services, has built her career around eliminating language-based discrimination in clinical settings. Her core argument is direct: you cannot outsource ethics to AI, and the companies that win will combine AI speed with human-in-the-loop precision.
What We Cover- Why language access is patient safety infrastructure, not an administrative convenience, for the 26 million Limited English Proficient patients in the US.
- Research from the Agency for Healthcare Research and Quality showing LEP patients experience 4 times more severe temporary harm than English speakers.
- What the Affordable Care Act Section 1557 requires and where AI-only translation falls short of compliance.
- Why cultural context around health, illness, and family dynamics cannot be replicated by current machine translation.
- How AI with human post-editing can expand access without eroding the clinical precision that patient safety demands.
Key TakeawaysLanguage access is infrastructure, not a service line item. Declaring English the standard in a clinical setting is, in Carol's metaphor, like declaring staircases the standard. There is nothing wrong with staircases unless you use a wheelchair.
Machine translation accelerates speed, not accuracy. A word with specific clinical meaning in one language may not translate precisely into another. Human post-editing is what keeps translation safe in diagnosis and treatment planning.
Ethics cannot be outsourced to AI. Reducing language access to a cost-reduction problem rations care based on budget rather than patient need, which is the opposite of the equity healthcare leaders claim to protect.
Frameworks & Tools Mentioned- Affordable Care Act Section 1557
- Agency for Healthcare Research and Quality (AHRQ) research on LEP outcomes
- Effective Inclusion Through Language Access training course
- Human-in-the-loop translation models
Timestamps00:00 Language access as ramps vs staircases 00:45 Why this matters in an AI era 03:10 Trust, readiness, and frontline communication 05:10 Communication is the most powerful diagnostic tool 08:20 Civil rights, policy risk, and meaningful access 12:10 AI won’t replace ethics, empathy, or rapport 15:00 Human-in-the-loop translation and post-editing 17:40 Artificial intelligence is not moral intelligence 21:45 Inclusion as infrastructure, not an afterthought 24:10 Making language access visible as patient safety 26:20 “Language access is the bridge between compliance and compassion” 27:55 How to reach Carol and learn more
About Carol VelandiaCarol Velandia is the Founder of Equal Access Language Services. She trains healthcare providers on communication equity, Section 1557 compliance, and the cultural frameworks that determine whether a patient can safely describe their symptoms. Her work sits at the intersection of patient safety, equity, and responsible AI deployment.
Related ResourcesRelated episodes:
- AI in the ER: Clinical Judgment at the Bedside
- Balancing Human Judgment and Clinical Trust
- Healthcare Leadership and Operational Reality
Related topic: 📘 Beneath the Signal, Chris's book on the human work behind trusted data and responsible AI in healthcare: Get it on Amazon
Support the show
About The Signal Room: The Signal Room is a podcast and communications platform exploring leadership, ethics, and innovation in healthcare and artificial intelligence. Hosted by Christopher Hutchins, Founder and CEO of Hutchins Data Strategy Consultants. Leadership, ethics, and innovation, amplified.
Website: https://www.hutchinsdatastrategy.com
LinkedIn: https://www.linkedin.com/in/chutchins-healthcare/
YouTube: https://www.youtube.com/@ChrisHutchinsAi
Book Chris to speak: https://www.chrisjhutchins.com
31 min - Data Quality and AI Strategy: The Rising Cost of Ignoring Data Governance | Danette McGilvray
Send us Fan Mail
AI strategy for healthcare is a data quality strategy first — Danette McGilvray on the rising cost of ignoring data governance in healthcare AI.
AI strategy for healthcare is a data quality strategy first. Danette McGilvray, President of Granite Falls Consulting and author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, has spent more than 30 years explaining why the data powering AI is the reason the technology exists at all. Her framing is direct: organizations will spend tens of millions on technology and refuse to spend hundreds of thousands on the data that makes it work.
What We Cover- Why generative AI amplifies bad data at scale and with confidence rather than fixing it.
- Data Quality Debt, the parallel Danette draws to technical debt, and why it compounds at the rule of tens (a $1 fix at requirements becomes $10 at the next stage and thousands in production).
- A real global ERP migration case where every site that invested in data quality closed the books on time and the one that refused could not ship product.
- How AI bias gets baked in by historical data, including research showing AI recommended $400,000 for a male medical specialist versus $280,000 for a woman in the same role.
- The 10 steps methodology for creating, managing, and sustaining trusted data, now in a second edition and translated into multiple languages.
Key TakeawaysGenerative AI does not fix bad data; it amplifies it. Outputs appear sophisticated and authoritative while remaining fundamentally unreliable. This creates a false sense of trust that hides underlying quality problems.
Data quality is a strategic investment, not a cleanup project. Organizations that succeed invest in data governance long before they invest in machine learning, treating data quality as foundational rather than preparatory.
Ethics starts in the metadata. Governance is about enabling people to use data responsibly, not controlling access. Doing things right the first time only feels like slowing down while actually saving money and accelerating results.
Frameworks & Tools Mentioned- Ten Steps to Quality Data Methodology (Granite Falls Consulting)
- Data Quality Debt framework
- Rule of Tens for cost escalation
- Metadata-driven governance
About Danette McGilvrayDanette McGilvray is President of Granite Falls Consulting and author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, now in its second edition with translations in Chinese, Japanese, and Spanish underway. She has spent more than 30 years helping organizations treat data as a strategic asset rather than a technical afterthought.
Related ResourcesRelated episodes:
- Rethinking Healthcare Data Strategy
- The Enterprise AI Journey: Data Foundations
- Data Readiness for AI Adoption
Related topic: Healthcare AI Strategy
Related articles:
- Healthcare Data Strategy
- Data Governance in Healthcare
Stay connected: The AI Health Pulse newsletter
Enjoying the show?📘 Beneath the Signal, Chris's book on the human work behind trusted data and responsible AI in healthcare: Get it on Amazon
Support the show
About The Signal Room: The Signal Room is a podcast and communications platform exploring leadership, ethics, and innovation in healthcare and artificial intelligence. Hosted by Christopher Hutchins, Founder and CEO of Hutchins Data Strategy Consultants. Leadership, ethics, and innovation, amplified.
Website: https://www.hutchinsdatastrategy.com
LinkedIn: https://www.linkedin.com/in/chutchins-healthcare/
YouTube: https://www.youtube.com/@ChrisHutchinsAi
Book Chris to speak: https://www.chrisjhutchins.com
46 min - Tailoring AI Strategy for Healthcare Leaders: Product Adoption and AI Literacy | Ritu Chakrawarty
Send us Fan Mail
AI strategy for healthcare cannot be copy-pasted from other industries — Ritu Chakrawarty on AI readiness, product adoption, and AI literacy for healthcare leaders.
AI strategy for healthcare cannot be copy-pasted from another industry. Ritu Chakrawarty, an analytics and AI product leader with nearly 2 decades across analytics, consulting, startups, and enterprise AI strategy, argues that the reason most healthcare AI projects stall is not model quality but adoption design. Recorded at the Put Data First Conference in Las Vegas, this conversation lays out a practical adoption-first framework for healthcare leaders who are tired of pilots that never scale.
What We Cover- The "stop, easy, and flow" framework for designing AI that actually gets adopted: identify what people will stop doing, make the solution easy to use, and embed it in the workflow where people already operate.
- The "should or could" test that separates problems that need AI from problems a simpler solution would handle faster and cheaper.
- Why AI literacy has to be sponsored from the top of the organization, and why fear of AI is fundamentally a fear of not knowing what is coming and why.
- Why every CEO needs an AI advisor reporting directly to them, and why the right advisor blends technology depth with business acumen rather than being purely technical.
- A litmus test for AI viability: start from the value, verify the information is digital and verifiable, then test for adoption before scaling.
Key TakeawaysAI adoption is a product problem, not a model problem. Most healthcare AI initiatives fail at the adoption layer. The technology works; the humans around it are not ready to change their workflows.
"Should or could" is the most underused question in AI strategy. Leaders regularly greenlight AI for problems that could be solved with a form, a rule, or a spreadsheet. The real cost of AI is not the model; it is the organizational debt of maintaining it.
A purely technical AI advisor creates a bias toward technical solutions. The best AI strategists are generalists who have delivered business outcomes across multiple parts of the organization and bring strong technology understanding to the conversation.
Frameworks & Tools Mentioned- Stop, Easy, Flow adoption framework
- Should or Could AI viability test
- AI literacy sponsorship model (top-down)
- CEO AI advisor role
About Ritu ChakrawartyRitu Chakrawarty is a data and analytics executive with nearly 2 decades of experience spanning analytics, consulting, startups, and enterprise AI product strategy. She advises leaders on how to move AI from pilot to adoption and frames AI literacy as an organizational capability rather than a training program.
Related ResourcesRelated episodes:
- From AI Strategy to Execution: Trust, Leadership, and Operational Reality
- Data Readiness for AI Adoption
- From AI Hype to Real Value
Related topic: Healthcare AI Strategy
Related article: Healthcare Data Strategy
Stay connected: The AI Health Pulse newsletter
Enjoying the show?
Honest ratings on Apple Podcasts are the single best way to help more healthcare A
📘 Beneath the Signal, Chris's book on the human work behind trusted data and responsible AI in healthcare: Get it on Amazon
Support the show
About The Signal Room: The Signal Room is a podcast and communications platform exploring leadership, ethics, and innovation in healthcare and artificial intelligence. Hosted by Christopher Hutchins, Founder and CEO of Hutchins Data Strategy Consultants. Leadership, ethics, and innovation, amplified.
Website: https://www.hutchinsdatastrategy.com
LinkedIn: https://www.linkedin.com/in/chutchins-healthcare/
YouTube: https://www.youtube.com/@ChrisHutchinsAi
Book Chris to speak: https://www.chrisjhutchins.com
24 min - Healthcare AI Leadership: Trust, Psychological Safety & Credible Authenticity | Dr. Larry Kuhn
Send us Fan Mail
Ethical leadership, AI strategy, and the human-AI trust deficit — Dr. Larry Kuhn on psychological safety and why healthcare AI adoption stalls without credible leadership.
Leadership in AI starts with trust, and the data says leaders are starting from a deficit. Only 25% of employees say they trust their CEO, and just 21% strongly trust their organization. Dr. Larry Kuhn, clinical psychologist, Founder of AspirVue, and President of Prepare to Change, has spent more than 3 decades helping healthcare organizations, Fortune 500 companies, and government agencies rebuild the trust that AI transformation demands.
What We Cover- Why trust is the single greatest barrier to healthcare AI adoption, and what the 5-decade drop from roughly 70% institutional trust in the 1970s to 25% today actually means for leaders driving change.
- Dr. Kuhn's Seven Dimensions of Credible Authenticity framework and how to translate each dimension into behaviors employees can feel in daily interactions.
- The 3 predictable fear responses employees exhibit during transformation: moving against (aggression and control), moving away (disengagement and avoidance), and moving toward (minimizing problems behind a false calm).
- Why 50 to 70 percent of adults cannot recall a primary caregiver who was consistently available, and how those attachment patterns shape workplace responses to AI disruption.
- The leadership practices that create psychological safety for AI adoption, and why no amount of technology can substitute for genuine human connection during periods of disruption.
Key TakeawaysTrust is relational, not informational. No amount of data, slide decks, or ChatGPT output can replace the lived experience of a leader who shows up consistently, follows through on commitments, and grounds decisions in evidence rather than sales pitches.
Fear is a predictable response to uncertainty, and it has a shape. Leaders who recognize the 3 fear patterns can meet employees where they are rather than misreading disengagement as resistance or false calm as alignment.
Credible authenticity is a practice, not a personality trait. Being reliable, real, relatable, ready, and rational shows up in small repeated behaviors over time, not in a single all-hands speech.
Frameworks & Tools Mentioned- Seven Dimensions of Credible Authenticity (Kuhn, AspirVue)
- Trust at Work research paper
- Three fear responses: moving against, moving away, moving toward
- Psychological safety frameworks for transformation
Timestamps00:00 Leading Through AI With Trust 02:10 Trust Tax vs. Trust Dividend 05:45 Why Trust Is Declining at Work 10:20 Psychological Safety and Fear 14:30 AI Transformation and Job Anxiety 19:00 Reliability and Authentic Leadership 25:40 Full Disclosure and Trust 30:10 Credible Authenticity Framework 32:20 How to Connect with Dr. Larry Kuhn 33:00 Closing
About Dr. Larry KuhnDr. Larry Kuhn is a clinical psychologist, Founder of AspirVue, and President of Prepare to Change. He has more than 3 decades of experience helping healthcare organizations, Fortune 500 companies, nonprofit executives, and government agencies navigate transformation. His paper "Trust at Work: The Seven Dimensions of Credible Authenticity" is gaining traction in leadership circles nationwide.
Related ResourcesRelated episodes:
- Healthcare Leadership, Operational Reality, and System Signals
- Why Human-Centered AI Governance is Non-Negotiable
- Get it on Amazon
Support the show
About The Signal Room: The Signal Room is a podcast and communications platform exploring leadership, ethics, and innovation in healthcare and artificial intelligence. Hosted by Christopher Hutchins, Founder and CEO of Hutchins Data Strategy Consultants. Leadership, ethics, and innovation, amplified.
Website: https://www.hutchinsdatastrategy.com
LinkedIn: https://www.linkedin.com/in/chutchins-healthcare/
YouTube: https://www.youtube.com/@ChrisHutchinsAi
Book Chris to speak: https://www.chrisjhutchins.com
31 min - Healthcare Data Readiness and AI Adoption: Why 85% of Organizations Aren't Prepared | Ratnadeep Bhattacharjee
Send us Fan Mail
AI readiness in healthcare is a data readiness problem — Ratnadeep Bhattacharjee on why 80–85% of organizations are not prepared for AI strategy at scale.
AI readiness in healthcare is not a marketing slogan; it is a data readiness problem, and 80 to 85 percent of organizations are not prepared. Ratnadeep Bhattacharjee, co-founder and Managing Director of Tech Variable, has spent his career bridging non-technical leaders and deeply technical engineering teams so that data challenges become competitive advantages. His argument is direct: before an organization trains a model, it should audit whether its data can be trusted to train anything at all.
What We Cover
- The 4-lens data audit framework every healthcare organization should run before deploying AI: completeness, consistency, connectivity, and compliance.
- A real Medicaid health plan case study where fixing foundational data issues improved quality measures reporting without deploying a single AI model.
- How Tech Variable's SyncMesh platform harmonizes fragmented data from EHRs, claims feeds, pharmacies, and devices into a unified FHIR-compliant patient 360 view.
- Why data maturity must be treated as an ongoing service rather than a one-time project, and what that looks like operationally.
- The 4 trends shaping healthcare AI through 2030: ongoing data maturity, context-aware AI, ambient AI designed to reduce burnout rather than add clicks, and ethics embedded by design.
Key Takeaways
Data readiness is the bottleneck, not model selection. Organizations that skip the audit and move straight to AI procurement end up with pilots that demonstrate nothing because the underlying data cannot support a defensible conclusion.
Interoperability is a governance outcome, not a technical feature. Healthcare data is fragmented across EHRs, claims, pharmacies, and devices. A unified patient 360 view requires data contracts and stewardship, not just a new integration platform.
Ethics belongs at every stage, not at the end. AI adoption that waits until deployment to ask ethics questions has already baked in the decisions that matter most. Ethics by design is cheaper and safer than ethics by retrofit.
Frameworks & Tools Mentioned
- Four-lens data audit: completeness, consistency, connectivity, compliance
- SyncMesh integration platform (Tech Variable)
- FHIR-compliant patient 360 architecture
- Context-aware and ambient AI patterns
- Ethics-by-design adoption model
About Ratnadeep Bhattacharjee
Ratnadeep Bhattacharjee is co-founder and Managing Director of Tech Variable, a healthcare IT firm specializing in AI-driven solutions, data integration, and digital transformation. His perspective is distinctive: a non-technical founder in a deeply technical world, leading with curiosity and servant leadership, bridging engineering teams and healthcare stakeholders who care about outcomes, not algorithms.
Related Resources
Related episodes:
- The Enterprise AI Journey: From Data Foundations to Generative and Agentic AI
- From AI Strategy to Execution: Trust, Leadership, and Operational Reality
- Data Quality and AI Strategy: Garbage In, Gen AI Out
Related topic: Healthcare AI Strategy
Related articles:
- Healthcare Data Str
📘 Beneath the Signal, Chris's book on the human work behind trusted data and responsible AI in healthcare: Get it on Amazon
Support the show
About The Signal Room: The Signal Room is a podcast and communications platform exploring leadership, ethics, and innovation in healthcare and artificial intelligence. Hosted by Christopher Hutchins, Founder and CEO of Hutchins Data Strategy Consultants. Leadership, ethics, and innovation, amplified.
Website: https://www.hutchinsdatastrategy.com
LinkedIn: https://www.linkedin.com/in/chutchins-healthcare/
YouTube: https://www.youtube.com/@ChrisHutchinsAi
Book Chris to speak: https://www.chrisjhutchins.com
33 min - Redefining the Patient-Physician Journey with AI | Trust & Interoperability | Dr. Barry Chaiken
Send us Fan Mail
AI governance, interoperability, and healthcare AI strategy for 2050 — Dr. Barry Chaiken on why powerful AI tools require governance before deployment.
AI in healthcare is powerful, and powerful tools require governance before they require deployment. Dr. Barry Chaiken, a physician leader with more than 25 years in clinical transformation, health IT, and public health, continues his conversation with host Christopher Hutchins on what responsible AI adoption actually looks like at the point of care. Part 2 moves from theory to the questions leaders need to answer before any AI touches a patient.
What We Cover
- Why AI deployment should start with a clearly defined goal and iterate in small cycles of execute, learn, and adjust rather than launching at scale from day one.
- Algorithmic bias and the diverging regulatory approaches between the United States and the European Union, including why deep fakes influencing roughly 160 elections worldwide is a governance problem healthcare cannot ignore.
- Healthcare interoperability through the lens of global ATM networks, and why EMR vendors have a responsibility to make patient data portable.
- A 2050 vision for AI-accelerated drug development through AlphaFold-style protein folding research, clinical trials conducted in silico using digital twins, and personalized patient engagement that accounts for real-world constraints.
- How to read vendor claims skeptically, including Dr. Chaiken's observation that even Sam Altman does not fully understand how AI works.
Key Takeaways
Humans should control AI; AI should never control humans. Without human knowledge, experience, ethics, and values directing AI, models hallucinate and cause harm. With those qualities guiding them, AI can do meaningful work.
Interoperability is a patient-rights issue, not an IT issue. The data belongs to the patient. EMR companies that make portability difficult are defending a business model at the expense of the patient relationship.
Digital twins and in silico trials will redefine drug development. By 2050, personalized simulation will compress timelines that currently take more than a decade. That shift is already beginning, and healthcare leaders should be building the data infrastructure that supports it.
Frameworks & Tools Mentioned
- DocsNetwork (consultancy for clinical innovation and patient safety)
- HIMSS governance frameworks
- AlphaFold protein folding research
- Digital twin and in silico trial architectures
- US vs EU algorithmic bias regulatory comparison
About Dr. Barry Chaiken
Dr. Barry Chaiken is a physician leader with more than 25 years of experience in clinical transformation, health IT, and public health. He has served as chief medical officer for multiple health tech companies, advised the federal government on pandemic preparedness, and is a past board chair of HIMSS. He is the founder of DocsNetwork, a consultancy focused on clinical innovation and patient safety, and author of Future Healthcare 2050.
Related Resources
Related episodes:
- How AI Redefines the Patient-Physician Journey (Part 1)
- Balancing Human Judgment and Clinical Trust in Healthcare
- The Scary Truth About AI in the ER and Why Clinical Judgment Still Wins
Related topic: Clinical AI and Patient Care
Related article:
📘 Beneath the Signal, Chris's book on the human work behind trusted data and responsible AI in healthcare: Get it on Amazon
Support the show
About The Signal Room: The Signal Room is a podcast and communications platform exploring leadership, ethics, and innovation in healthcare and artificial intelligence. Hosted by Christopher Hutchins, Founder and CEO of Hutchins Data Strategy Consultants. Leadership, ethics, and innovation, amplified.
Website: https://www.hutchinsdatastrategy.com
LinkedIn: https://www.linkedin.com/in/chutchins-healthcare/
YouTube: https://www.youtube.com/@ChrisHutchinsAi
Book Chris to speak: https://www.chrisjhutchins.com
30 min - Redefining the Patient-Physician Journey with AI | Ambient Listening & Automation Bias | Dr. Barry Chaiken
Send us Fan Mail
Healthcare AI, ambient listening, and the future of the patient-physician journey — Dr. Barry Chaiken on why AI in healthcare has to start with trust, not tools.
AI in healthcare gets its meaning from the patient-physician relationship, not the other way around. Dr. Barry Chaiken, a physician leader with more than 25 years in clinical transformation, health IT, and public health, opens the inaugural episode of The Signal Room with a challenge to every healthcare leader: scale AI without losing the human touch, or accept that the technology will erode the trust that makes care possible.
What We Cover
- The origin of Dr. Chaiken's interest in health IT, traced back to an early IBM PC at the CDC during his training and the through-line to his book Future Healthcare 2050.
- The Revolutionary Health IT (RHIT) framework and why technology without workflow redesign tied to specific outcomes makes things worse, not better.
- How ambient listening technology can free physicians from documentation burden while producing personalized patient summaries, specialty-prioritized referral notes, and communications adapted to a patient's language and education level.
- Why the electronic health record was built for reimbursement documentation, not patient care, and what that design debt means for AI layered on top.
- Automation bias and the 3 safeguards every healthcare AI deployment should include: workflow design that keeps physicians in the loop, AI-monitoring-AI systems, and ongoing surveillance to identify patterns.
Key Takeaways
AI should humanize healthcare, not replace human connection. The greatest near-term value of clinical AI is reclaiming the physician's attention for the patient in the room, not replacing the conversation that happens there.
Automation bias is the quiet failure mode of clinical AI. Clinicians who defer to a confident-looking output without questioning it are the mechanism that turns a well-calibrated model into a dangerous one. Aviation solved this with crew resource management; healthcare has not.
Nurses are the load-bearing test for any AI deployment. If you want to fail, implement something without asking the nurses. If you want adoption, design the workflow with them first.
Frameworks & Tools Mentioned
- Revolutionary Health IT (RHIT) framework
- Ambient listening / ambient AI for clinical documentation
- Future Healthcare 2050 (book, Chaiken)
- DocsNetwork consultancy
- Aviation crew resource management (applied as a safety analog)
About Dr. Barry Chaiken
Dr. Barry Chaiken is a physician leader with more than 25 years of experience in clinical transformation, health IT, and public health. He has served as chief medical officer for multiple health tech companies, advised the federal government on pandemic preparedness, and is a past board chair of HIMSS. He is the founder of DocsNetwork and author of Future Healthcare 2050.
Related Resources
Related episodes:
- How AI Redefines the Patient-Physician Journey, Part 2
- Balancing Human Judgment and Clinical Trust in Healthcare
- Healthcare Leadership, Operational Reality, and System Signals
Related topic: Clinical AI and Patient Care
Related article: Healthcare Data Strategy
S
📘 Beneath the Signal, Chris's book on the human work behind trusted data and responsible AI in healthcare: Get it on Amazon
Support the show
About The Signal Room: The Signal Room is a podcast and communications platform exploring leadership, ethics, and innovation in healthcare and artificial intelligence. Hosted by Christopher Hutchins, Founder and CEO of Hutchins Data Strategy Consultants. Leadership, ethics, and innovation, amplified.
Website: https://www.hutchinsdatastrategy.com
LinkedIn: https://www.linkedin.com/in/chutchins-healthcare/
YouTube: https://www.youtube.com/@ChrisHutchinsAi
Book Chris to speak: https://www.chrisjhutchins.com
24 min
About The Signal Room | AI in Healthcare: Strategy & Governance
From the publisher's feed
The Signal Room is a healthcare AI podcast hosted by Chris Hutchins, founder of Hutchins Data Strategy Consultants, a healthcare AI consulting practice built for leaders implementing AI with…
strategy, governance, and ethical leadership. Drawing on real AI consulting work with health systems, the show goes deep on AI strategy for healthcare, ethical governance, data strategy, data readiness, and responsible adoption in real health systems. Each episode brings healthcare executives, clinicians, and technology leaders into candid conversations about the decisions, operating conditions, and accountability behind healthcare AI implementation. Listen at https://signalroompodcast.com. Subscribe to AI Health Pulse for weekly healthcare AI analysis.