"AI has the potential to re-ontologize healthcare—to completely redesign what we consider to be a disease, what we consider to be a disability, and how we organise care. But we need to decide what good healthcare actually means before we AI-ify everything."

— Dr Jessica Morley

In this episode of Clinical Changemakers, Dr Jessica Morley, an AI ethics expert and researcher from Yale Digital Ethics Centre, challenges our understanding of what large language models actually are and what they mean for healthcare. Drawing from her work on data ethics and her involvement with OpenSafely during the pandemic, Dr Morley explains why viewing AI as "stochastic parrots" is crucial for healthcare implementation, explores the concept of re-ontologizing medicine, and argues why we need ethical frameworks before technological deployment rather than after.

Key Takeaways

Stochastic Parrots in Medicine: Large language models don't understand medical concepts—they predict the most likely next word based on probability from their training data. This means they can give you different answers to the same medical question and lack the contextual understanding crucial for patient care. Understanding this fundamental limitation is essential for safe healthcare implementation.

The Re-ontologizing Power of AI: AI doesn't just replace existing tools like upgrading from a physical to digital stethoscope. It has the power to completely redesign healthcare by redefining what constitutes disease, changing how we organize care, and separating diagnosis from physical patient interaction. This transformation can be powerful and positive, but only if we're intentional about our goals.

The Inverse Data Quality Law: Just as the inverse care law states that those who need healthcare most get it least, the inverse data quality law means those who need AI healthcare tools most will have the poorest quality data about themselves. This creates a two-tier system where marginalized populations get inferior AI-driven care.

Social License Trumps Legal Permission: Technical feasibility and legal compliance aren't enough for successful AI implementation in healthcare. Projects like the UK's Care.data failed despite being legal because they lacked social acceptance. OpenSafely succeeded by building in privacy protections, transparency, and meaningful public engagement from the start.

Where to Find Our Guest

Dr Jessica Morley (LinkedIn, Google Scholar)

In This Episode

01:27 - Stochastic parrots: Understanding what LLMs actually do

04:27 - Emergent properties: Why LLMs remain sophisticated probability machines

11:57 - Moral worth and parasocial relationships: When humans attach meaning to AI

14:33 - Re-ontologizing healthcare: How AI redesigns medicine itself

19:28 - The ethical traps: What happens when we AI-ify without thinking about outcomes

25:33 - Two-tier systems: How AI could worsen healthcare inequalities

28:44 - Ethics vs. law: Why we need both rules and values

32:31 - Learning from NHS failures: The importance of not making assumptions

47:43 - Global policy tensions: EU regulation vs. US "let it rip" approaches

54:33 - False dichotomies: Moving beyond "some care vs. no care" thinking

59:02 - Current global sentiment: From tech optimism to healthcare caution

Referenced

* Dr Morley’s Paper - The ethics of AI in health care: A mapping review (link)

* Defining a Stochastic Parrot (link)

* OpenSafely platform and approach to health data research (link)

* Lessons from Care.data project and its failure in the UK (link)

Contact

If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Hey there 🙌

As a small independent podcast, every rating and share makes a real difference in helping us reach more healthcare leaders. If you found value here, please rate us and pass this along to a colleague who needs to hear it.

Clinical Changemakers is a podcast that explores inspiring stories of leadership and innovation in healthcare. To learn more and join the conversation, visit: www.clinicalchangemakers.com

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.clinicalchangemakers.com

"We want a two-way flow of communication so that we have a better understanding through the levels of an organisation up through those levels of what's actually happening and we can make decisions closer to the ground."

— Dr. Sharen Paine

In this episode of Clinical Changemakers, Dr. Sharen Paine, a systems thinking expert with a doctorate in business administration focusing on cybernetics, explores why healthcare organisations repeatedly struggle with the same problems despite constant restructures. Drawing from her hands-on work with ophthalmology services, Dr. Paine introduces the Viable System Model (VSM) as a framework for creating truly functional healthcare organisations that can adapt and thrive rather than merely survive.

Key Takeaways

Cybernetics vs. Command-and-Control: Healthcare systems fail because they rely on one-way, top-down commands rather than cybernetic feedback loops. True organisational health requires continuous information flow between all levels, like an aircraft's autopilot system that constantly adjusts based on environmental feedback, not rigid directives from a distant control tower.

The Black Hole of Management Chaos: Service managers and clinical directors get consumed by day-to-day firefighting because healthcare systems lack proper coordination functions. Without clear coordination mechanisms, everything collapses into the management layer, creating a "tumble dryer" of urgent tasks that prevents strategic thinking and long-term planning.

The Five Functions of Viable Systems: Every healthcare organisation needs five continuously operating subsystems: (1) Operations that deliver patient care, (2) Coordination that orchestrates daily activities, (3) Management that sets conditions for success, (4) Intelligence/Planning that looks ahead strategically, and (5) Governance that defines purpose and values. Most healthcare systems conflate these functions, leading to dysfunction.

Recursive Autonomy Creates Efficiency: The VSM applies at every level—from national health systems down to individual departments. Each level requires all five functions to operate with appropriate autonomy. A $150 equipment purchase shouldn't require Wellington approval, just as an ophthalmology clinic shouldn't decide national hospital placement. Right-sized autonomy eliminates bureaucratic bottlenecks.

Coordination is the Missing Function: Healthcare systems fail to recognise coordination as a distinct, critical function requiring dedicated resources and IT systems. Coordination includes prioritised wait lists, rosters, schedules, skills matrices, and training—currently done manually or through spreadsheets, creating errors and inefficiency. Proper coordination would prevent most day-to-day chaos.

Where to Find Our Guest

Dr. Sharen Paine (LinkedIn)

In This Episode

00:00 - Introduction: Dr. Sharen Paine's background in cybernetics and systems thinking

07:37 - Sharen's journey: From mental health services to ophthalmology system challenges

15:05 - Production planning struggles: Why system changes fail without organizational support

18:16 - The governance-management-operations disconnect in healthcare hierarchies

25:04 - Introduction to the Viable System Model: Five essential organizational functions

26:59 - Defining viability: How organizations persist and adapt over time

28:05 - The five subsystems explained: From governance to operations

36:05 - Applying VSM to ophthalmology: What it looks like in practice

46:33 - Functions vs. roles: VSM describes what needs to happen, not organization charts

55:29 - Building collaborative understanding: Clinicians learning constraints, managers understanding frontlines

59:48 - Bottom-up implementation: Practical steps toward VSM adoption

1:05:12 - The VSM perspective: Seeing cybernetic patterns everywhere

Referenced

* Utilising insights from cybernetics and the Viable System Model to address health system disconnects (Doctoral Thesis)

* Principles for embedding learning and adaptation into the New Zealand health system functioning: the example of the Viable System Model (paper)

* Utilising VSM insights to address health system disconnects: introducing three novel organisational pathologies (paper)

* Mapping clinical governance to practitioner roles and responsibilities (paper)

Contact

If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Music Attribution: Music by AudioCoffee from Pixabay.

Before you go! 🙌

As a small independent podcast, every rating and share makes a real difference in helping us reach more healthcare leaders. If you found value here, please rate us and pass this along to a colleague who needs to hear it.

Clinical Changemakers is a podcast that explores inspiring stories of leadership and innovation in healthcare. To learn more and join the conversation, visit: www.clinicalchangemakers.com

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.clinicalchangemakers.com

"I think judgment, I've been honing in on that word more frequently recently because I feel like the judgment piece is the piece that feels particularly like human in this decision."

— Dr Graham Walker

In this episode of Clinical Changemakers, Dr Graham Walker, an ER doctor and AI healthcare leader, discusses his role at Kaiser Permanente and the challenges and successes of integrating AI into healthcare. He emphasizes the importance of communication, leadership, and the need for alignment in large healthcare systems. Dr. Walker shares insights on predictive AI tools, the operationalisation of AI in patient care, and the ethical considerations surrounding AI's explainability. He also highlights the rapid adoption of AI scribes across 4.6 million members and the balance between AI benefits and risks in clinical practice.

Key Takeaways

Leadership through servant mentality: Successful healthcare innovation requires leaders who ask "How can I help you?" rather than directing from above. Clear communication, removing blockers, and building relationships are essential for large-scale change.

The 85% capacity rule: Healthcare systems running at 100% capacity fail due to variance in patient complexity and unpredictable demand. Queuing theory suggests keeping 10-15% flexibility to handle the inherent unpredictability of medical care.

Predictive AI has staying power: Kaiser's Advanced Alert Monitoring system, running live since 2018, successfully predicts ICU transfers and rapid responses across all inpatients. The operational challenge isn't building the model—it's fine-tuning sensitivity/specificity and creating sustainable workflows.

AI scribes: Healthcare's fastest adoption: The rapid rollout of AI scribes represents perhaps the fastest technology adoption in healthcare history, driven by ease of use (just install an app and hit record) and immediate physician benefit in reducing "pyjama time" documentation.

Judgment remains human: While AI excels at pattern recognition and data synthesis, clinical judgment—weighing risks, understanding context, and making treatment decisions—remains distinctly human. The scraped knee doesn't need septic workup; that's judgment, not diagnosis.

Where to Find Our Guest

* Dr. Graham Walker (LinkedIn)

In This Episode

00:00 - Introduction: Graham Walker's role at Kaiser Permanente Northern California

04:36 - Innovation framework: Value-based care vs. fee-for-service and finding ideas with potential

07:08 - Commercialisation strategy: Building internal tools for external deployment

11:00 - Leadership lessons: Coalition building and relationship management in large institutions

16:34 - Queuing theory in healthcare: Why running at 100% capacity breaks systems

19:48 - Predictive AI success story: Advanced Alert Monitoring system in production

26:00 - The human element: Why unexplainable AI might force better clinical thinking

32:33 - AI scribes rollout: Fastest healthcare technology adoption in history

39:30 - Information fidelity challenges: Note bloat, omissions, and human nature

45:02 - The judgment frontier: What remains uniquely human in clinical decision-making

50:20 - Advice for healthcare leaders: Use AI tools daily to understand capabilities and limits

53:57 - Off-call introduction: Building transparency in physician work and workload data

Referenced

* Dr Walker’s company website Offcall

* Dr Walker’s Medical Calculation Website MedCalc

* Paper: Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration (Link)

* Paper: Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation (Link)

* Queueing Theory (Wikipedia)

* Servant Leadership (Link)

Contact

If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Music Attribution: Music by AudioCoffee from Pixabay.

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“You know, what if they were to actually put it’s [AI] mind to a science of practical compassion for everybody?… if the right machines were to come along and help us do it, that's going to be a fabulous thing.”

Dr Richard Lehman is a retired GP from Oxfordshire who had a "ringside seat" to the birth of evidence-based medicine, previously held academic positions at Oxford and Yale, later becoming Professor of Shared Understanding of Medicine at the University of Birmingham. Dr Raj Mehta is a physician and evidence-based medicine educator who views EBM as essential heuristics for discerning truth in clinical practice. Together, they bring decades of experience wrestling with how we know what works in medicine, from the historical foundations laid by James Lind's scurvy trials to the AI revolution that promises to transform how we synthesise and apply medical evidence.

Key Takeaways

Truth-seeking requires method, not just conviction: Before EBM, medicine operated largely on "conviction-based" approaches collected in massive textbooks. The shift to systematic evidence evaluation transformed how we separate opinion from fact in clinical practice.

Numbers Needed to Treat illuminate magnitude: Tools like NNT help clinicians and patients understand effect sizes. Context and timeframe matter enormously.

AI could democratise and personalise evidence: Rather than replacing doctors, AI might enable real-time synthesis of evidence matched to individual patients, creating feedback loops between treatments and outcomes at an unprecedented scale.

The evidence map has gaps and mountains: Current evidence is like an 18th-century road atlas - some areas well-mapped, others blank. AI could be the "sat nav system" for medicine that acknowledges uncertainty while guiding decisions.

Social determinants still trump beta blockers: While we refine molecular treatments, the biggest health impacts remain at the policy level - safe neighbourhoods, warm homes, and social conditions. Medicine must embrace "practical compassion" beyond prescriptions.

Where to Find Our Guests

* Dr. Richard Lehman (X/Twitter)

* Dr. Raj Mehta (X/Twitter)

In This Episode

00:00 - Introduction: Why evidence-based medicine matters now more than ever

02:54 - The scientific method in medicine: Discerning truth from fiction in clinical practice

06:06 - James Lind and scurvy: The 200-year gap between discovery and adoption

12:01 - Bradford Hill and the RCT revolution: Moving from mechanism to measurement

14:29 - Richard's ringside view: When "evidence-based medicine" arrived in Oxford

19:10 - The limits of population evidence: Why Numbers Needed to Treat aren't enough

22:39 - Shared decision-making complexity: The overwhelming challenge of multimorbidity

26:00 - The AI revolution: From medical scribes to comprehensive evidence synthesis

29:39 - Patient empowerment in the age of monetised medicine

36:43 - The pre-Copernican challenge: Are we just getting better at measuring the wrong thing?

39:01 - China as a sandbox: Where innovation might overtake Silicon Valley

44:54 - Beyond beta blockers: Why social determinants still matter most

54:10 - COVID-19's mixed report card: EBM's triumphs and failures in crisis

59:04 - Communicating uncertainty: The topographical map of medical evidence

1:01:39 - Looking forward: Why this is a "fabulous time in medicine"

Referenced

* Richard Lehman’s weekly review of medical journals (Link)

* James Lind Library (Link)

* Richard Lehman on Evidence-Based Medicine (Podcast)

* The RECOVERY Trial (COVID-19 treatment comparison) (Link)

Contact

If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Music Attribution: Music by AudioCoffee from Pixabay.

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"When the frontline feels we're actually offering, 'what do you need? What are the resources we can help?' We'll co-create. Of course, they don't have the control of the resources or some decisions, but that's what executives can do."

Dr. Raj Srivastava is a pediatrician, health system leader, and implementation science researcher, serving as Chief Clinical Programs Officer at Intermountain Health. With an MPH from Harvard and over two decades of experience transforming healthcare delivery, Dr. Srivastava has pioneered approaches to scaling evidence-based practices across one of America's largest health systems, which spans 630,000 square miles (roughly the size of France, Germany, Spain, and the UK combined) and employs 64,000 people. From his early work mapping 150 million lab results across six children's hospitals to his current role orchestrating clinical transformation across 33 emergency departments, he brings unique insights into why healthcare struggles to implement best practices and how to overcome these challenges through learning health systems.

Key Takeaways

Co-creation beats imposition: Successful implementation requires working with frontline staff to understand their needs rather than imposing solutions. When clinicians feel heard and involved in designing solutions, resistance melts away.

Learning health systems require discipline: It's not just about having data or technology - it's about creating systematic approaches to identify what works, scale it appropriately, and sustain improvements over time using scientific methods.

Start with the problem, not the solution: Too often, healthcare gets excited about new technologies (EMRs, AI, big data) without first understanding the specific problems frontline staff face in delivering evidence-based care.

Systematic vs. one-off barriers: Distinguishing between system-wide challenges (like data infrastructure) and location-specific issues is crucial for effective problem-solving and resource allocation.

Trust enables speed: Building strong interpersonal relationships and psychological safety among leadership teams dramatically accelerates implementation. The "meeting before the meeting" matters.

Where to Find Dr. Raj Srivastava

* LinkedIn

In This Episode

00:00 - Introduction and why evidence-based medicine is so hard to implement in daily practice

04:47 - The gap between medical training aspirations and healthcare system reality

07:31 - Moving from descriptive research to application-based implementation science

09:05 - Transforming patient handoffs to reduce harm across multiple sites

19:32 - Systematic barriers and facilitators: Moving beyond "AI will solve everything"

23:53 - Addressing health equity through targeted subpopulation analysis

33:38 - "Build it and they won't come": The importance of co-creation and marketing

35:24 - Creating repeatable, scalable pathways for clinical improvement at enterprise scale

39:07 - Governance structures for managing clinical transformation across 630,000 square miles

41:23 - The power of pre-meeting communication and building executive trust

Referenced

* Increasing Adherence to Evidence-Based Clinical Practice (Paper)

* Intermountain Health, Health Delivery Institute (Link)

* Intermountain Health, Leadership Institute (Link)

Contact

If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Music Attribution: Music by AudioCoffee from Pixabay.

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"At the end of the day, we don't really invest as much into products as we do into people. It's the people behind the products that are going to make the product successful"

Dr. Amandeep Hansra is a general practitioner turned health tech entrepreneur, advisor, and investor, founder of the Creative Careers in Medicine community (with over 25,000 members), co-founder of Australian Medical Angels and is the Chief Clinical Adviser for the Australian Digital Health Agency. From accidentally falling into telehealth to building virtual care services across Australia and Asia, Dr. Hansra has spent her career at the intersection of clinical practice and technology innovation. Her work with Medical Angels provides crucial funding and expertise to health tech startups, while her community platform showcases the diverse career paths available to medical professionals beyond traditional clinical roles.

Key Takeaways

Say yes to unexpected opportunities: Career pivots often happen by accident - being open to opportunities outside your planned path can lead to transformative experiences and skills you never knew you needed.

Networking is essential but different for doctors: Medical professionals aren't taught to network, but being visible in health tech communities and conferences is crucial for discovering non-traditional opportunities.

Regulation vs. innovation balance: Health tech faces unique challenges in evidence generation and regulation that differ from those of pharmaceuticals, requiring new approaches to safety while not stifling innovation.

People over products in investing: When investing in health tech startups, the founding team matters more than the initial product. Mission-driven founders with clinical awareness are more likely to succeed.

In This Episode

* 00:35 - Accidental entry into digital health: from GP to telehealth pioneer

* 04:13 - The power of saying yes: building a portfolio career through opportunities

* 06:45 - Creating community: building Creative Careers in Medicine from scratch

* 10:25 - Global burnout crisis: why 70-80% of doctors wouldn't recommend medicine to their children

* 15:26 - Investment challenges: why health tech struggles to attract traditional VCs

* 19:40 - Evidence standards: adapting pharmaceutical rigour to rapidly evolving technology

* 25:23 - Regulatory sweet spot: balancing safety with innovation accessibility

* 29:32 - What investors look for: mission-driven teams over hockey-stick projections

* 32:42 - Corporate vs startup innovation: lessons from building inside Telstra

* 39:31 - Global expansion: how the same product succeeds differently across health systems

* 47:07 - Exit strategies: why health tech doesn't follow traditional pathways

Referenced

* Creative Careers in Medicine

* Australian Medical Angels

Contact

If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Music Attribution: Music by AudioCoffee from Pixabay

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"The enterprise of medicine has both scientific and moral dimensions, and they're inextricably balanced"

Dr Vikas Saini is a Cardiologist and President of the Lown Institute, where he leads a non-partisan think tank advocating bold ideas for a just and caring system for health. With a unique background combining philosophy and medicine, Dr. Saini has spent decades examining the intersection of ethics, evidence, and economics in healthcare. His work on the Social Responsibility Index challenges traditional hospital rankings by measuring what truly matters: equity, value, and patient outcomes rather than reputation and revenue.

Key Takeaways

Philosophy enables systemic healthcare thinking: A philosophical background provides tools to step back and examine healthcare's "water" - the invisible assumptions and frameworks that shape medical practice and policy decisions.

Healthcare is fundamentally a moral enterprise: While science guides medical decisions, the uncertainty inherent in complex human systems requires ethical frameworks. The profession exists to serve others, making moral dimensions inseparable from scientific ones.

20-30% of healthcare may be unnecessary: From procedures lacking evidence to system inefficiencies driving unnecessary hospitalisations, overuse represents a massive opportunity for improvement - but requires paradigm shifts, not just cutting services.

Hospital consolidation hasn't improved care: Despite promises of efficiency, consolidation has primarily raised prices while hospitals increasingly operate like businesses, with some running billion-dollar hedge funds rather than focusing on community health.

Where to Find Dr. Vikas Saini

* LinkedIn

In This Episode

00:08 - Philosophy's influence: learning to think about thinking in healthcare

02:45 - Healthcare as a moral and scientific enterprise: beyond the science

11:13 - The penicillin paradigm trap: why silver bullets fail for chronic disease

15:13 - Individual patients vs population means: the clinician's dilemma

19:15 - The culture of "doing something": when waiting is the hardest medicine

23:24 - Longitudinal care and efficiency: why more time means better outcomes

25:33 - Measuring overuse: from clear waste to uncertain benefits

30:36 - Why Choosing Wisely hasn't moved the needle significantly

34:28 - Policy opportunities: professional self-regulation over bureaucratic control

38:49 - Origins of the Social Responsibility Index: beyond reputation rankings

43:29 - Nonprofit hospitals acting like businesses: when mission meets margins

46:00 - Hospital consolidation: King Kong vs Godzilla, with patients trampled

50:58 - Advice for clinicians: taking up the cause of patients in systemic change

Referenced

* Abraham Flexner and the 1910 Flexner Report (Original Report)

* Dr. Bernard Lown (Profile)

* Continuity in general practice as a predictor of mortality, acute hospitalisation, and use of out-of-hours care: a registry-based observational study in Norway (Paper)

* Choosing Wisely (Website)

* Lown Institute's Social Responsibility Index (Website)

Contact

Contact Information: If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Music Attribution: Music by AudioCoffee from Pixabay

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"The way to prevent cascades is to keep your eye on the goal, to understand what you're trying to accomplish and not to go down rabbit holes chasing abnormalities, but rather to be focused on the outcome that you're looking for."

Dr James W. Mold is a family medicine physician, geriatrician, researcher and academic with a Master of Public Health degree and is Professor Emeritus at the University of Oklahoma Health Sciences Centre. His pioneering research focuses on transforming healthcare from a problem-oriented to a goal-oriented paradigm, addressing the limitations of traditional problem-focused approaches. For over 20 years, in collaboration with the Oklahoma Physicians Resource/Research Network (OKPRN), Dr Mold has completed more than 75 major projects, resulting in more than 100 publications.

Key Takeaways

Clinical cascades are preventable through goal focus: Unnecessary medical interventions often cascade from initial anxiety-driven decisions. Maintaining focus on patient outcomes rather than chasing every abnormality prevents harmful escalation.

The problem-oriented paradigm has reached its limits: While effective for acute, single problems, traditional disease-focused medicine struggles with the reality that all patients have multiple risk factors and complex needs.

Four universal healthcare goals exist naturally: Every patient wants to survive as long as possible, maintain quality of life, have a good death, and continue personal growth—goals that require individual understanding rather than standardised approaches.

Practice-based research networks bridge the research-practice gap: Successful implementation requires trusting practitioners, addressing their real-world questions, and providing ongoing facilitation support rather than just education.

Goal-oriented care is more satisfying and effective: This approach makes medicine more enjoyable for providers while resonating strongly with patients, though system constraints make adoption challenging.

Where to Find Dr James Mold

* LinkedIn

In This Episode

01:07 - Early experiences in family medicine and the humanising revolution

05:40 - Transition from family practice to academic geriatrics

10:31 - The cascade effect: when one decision spirals into unnecessary care

15:32 - Building practice-based research networks and learning from practitioners

22:00 - Implementation research and the "aeroplane repair in mid-flight" challenge

26:45 - The problem-oriented paradigm: how we got here and why it's failing

33:57 - Goal-oriented healthcare: the four universal goals every patient has

40:47 - Implementation challenges and global adoption of goal-oriented care

Referenced

* The Cascade Effect in the Clinical Care of Patients (Paper)

* Goal-Oriented Prevention: How to Fit a Square Peg into a Round Hole (Paper)

* The Law of Diminishing Returns in Clinical Medicine: How Much Risk Reduction is Enough? (Paper)

* Thomas Kuhn's "The Structure of Scientific Revolutions" (Book)

* Failure of the Problem-Oriented Medical Paradigm and a Person-Centred Alternative (Paper)

* Oklahoma Physicians Resource Research Network (Link)

* Practice-based research network methodology (Link)

Contact

Contact Information: If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

👍 Hey there - if you're enjoying this episode, make sure you share it with a friend! Thank you for your support.

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"We need to pay more attention to the networks that operate between people and the networks that operate between organisations.”

Professor Ingrid Nembhard is an Organisational Behaviour expert in healthcare systems, based at the Wharton School of the University of Pennsylvania. Her research focuses on how characteristics of health care organisations, their leaders, and staff contribute to their ability to implement new practices, engage in continuous organisational learning, and ultimately improve quality of care.

Key Takeaways

Relationships and networks drive outcomes: The connections between people and organisations significantly impact healthcare delivery. Traditional structural changes often fail because they ignore these relational dimensions.

Psychological safety is essential: Creating environments where people can take interpersonal risks—raising concerns, offering suggestions, sharing half-formed ideas—enables both innovation and problem-solving. This requires relationships built on trust and respect.

Standardisation and creativity complement each other: Rather than being opposed, standardisation helps identify where creativity is needed, and creative solutions can become new standards. Healthcare requires both standardised processes and customised approaches.

COVID revealed organisational potential: The pandemic demonstrated healthcare's capacity for creative problem-solving and adaptability. Organisations should maintain the communication channels and cross-functional collaboration that emerged during this crisis.

Where to Find Professor Ingrid Nembhard

* LinkedIn

In This Episode

00:01 - Introduction

00:34 - Is healthcare truly different from other industries?

03:16 - The growing recognition of organisational factors in healthcare

05:31 - How organisational skills should be incorporated into clinical education

10:20 - Learning the broader healthcare context vs. operational skills

14:25 - Understanding networks in healthcare organisations

18:47 - How to map and leverage relationships within organisations

22:48 - Building networks prospectively rather than retrospectively

26:26 - Creating psychological safety in healthcare teams

31:24 - Practical steps for leaders to implement psychological safety

36:40 - How standardisation and creativity can complement each other

42:51 - Lessons from COVID-19 for healthcare management

Referenced

* Integrating network theory into the study of integrated healthcare (Paper)

* NEJM: Responding to Covid-19: Lessons from Management Research (Paper)

* COVID-19 Inspired Creativity In Health Care: Lessons For Management And Policy (Article)

Contact

Contact Information: If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

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"Epic charges researchers to access the data that the same Institute has spent thousands of hours storing. They're not a good custodian of data... they're literally locking your data away and holding it for ransom." — Dr. Sidharth Ramesh

Listen now on Apple, Spotify, YouTube or wherever you get your podcasts.

Dr Sidharth Ramesh is a medical doctor, clinical informatics expert, YouTube educator, and co-founder of MedBlocks, a company dedicated to creating patient-centric digital health platforms and providing tools, products, educational materials, and services. His work spans healthcare data models, API standardization, and breaking down data silos to enable innovation across healthcare systems globally.

Key Takeaways

Legacy Healthcare IT stifles innovation: Current systems either lock you into vendor ecosystems or create unstable bespoke solutions that can't evolve with changing requirements.

Health data platforms offer a solution: By standardising data models and APIs, these platforms allow for consistent data storage while enabling innovation through applications built on top.

Global standards matter: Projects like OpenEHR with over 1,000 standardised clinical data models prevent reinventing the wheel for common healthcare concepts.

Headless EHRs are emerging: The concept of slimmed-down EHRs focused on data storage with standard APIs rather than locked-in workflows is gaining traction.

Policy can drive change: Regulations like information blocking rules in the US have started to crack open data monopolies, though progress is still needed.

Where to Find Dr. Sidharth Ramesh

* LinkedIn

* YouTube

In This Episode

00:01 - Introduction

04:13 - How the healthcare industry stifles innovation

07:47 - Problems with bespoke IT solutions in healthcare

11:59 - Integration challenges of best-of-breed approaches

14:30 - Examples of inconsistent data representation

17:05 - The healthcare data platform concept

19:22 - OpenEHR and global data models

22:09 - Consistent APIs and standards

23:15 - The benefits of platform approaches

27:46 - AI's impact on healthcare data access and patient empowerment

33:49 - Challenges with AI in healthcare

36:38 - Why big tech companies struggle in healthcare

39:52 - The role of policy in unlocking healthcare data

44:52 - The mission of MedBlocks

51:28 - The future of MedBlocks

Referenced:

* Medblocks Company (link)

* The Innovator's Prescription by Clayton Christensen (Book)

* Clinical Knowledge Manager (CKM) for OpenEHR (link)

* FHIR standard for healthcare interoperability (link)

Contact Information:

If you have any feedback, questions or if you'd like to get in touch, reach out at [email protected]

Music Attribution: Music by AudioCoffee from Pixabay

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