Healthcare organizations are constantly pulled toward whatever feels most urgent.

But when everything demands attention, the work that matters most can easily get crowded out.

The problem is not simply that healthcare has too much to do. It is that many organizations have not made their priorities clear enough to help people distinguish signal from noise.

In this piece, I’ll explore how leaders can make strategy useful in the decisions that shape everyday work:

• Define a small number of priorities clearly enough to guide choices.

• Use those priorities to decide what deserves attention.

• Distinguish strategic work from necessary but lower-value work.

• Make it acceptable to leave some problems alone.

• Use impact and effort as a second filter for deciding how much to invest.

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More than a decade into my medical career, I keep coming back to one painful observation:

In healthcare, we are constantly mistaking the urgent for the important.

There is always another fire.

An accreditation deadline. A complaint. A policy that needs updating. A case review. A new reporting requirement. A meeting someone thinks we absolutely need. An email with URGENT in the subject line.

And we respond.

Lights flashing. Sirens blaring. Everyone mobilizes.

Eventually, the sirens become so loud that they are almost the only thing we can hear.

The problem is not that none of these things matter. Many do. Some really are urgent.

The problem is that urgency has become one of healthcare’s main mechanisms for allocating attention.

And urgency is a terrible strategy.

We have too much work, but that’s only part of the problem

There is an obvious explanation for why this happens: healthcare organizations are overloaded.

There really is more work than there is capacity to do it.

Clinicians are stretched. Managers are stretched. Quality teams are stretched. Leaders are stretched. Basic daily operations consume an enormous amount of organizational energy before anyone gets around to improving anything.

But I think there is a deeper problem.

We often haven’t defined what is important clearly enough to distinguish it from what is merely demanding our attention.

If everything matters, then the loudest thing wins.

That means the remedy has to start before the next fire appears.

It starts with a conversation about what actually matters.

Strategy should tell us what not to do

Most healthcare organizations have a strategic plan. They have mission statements, values, annual priorities and glossy diagrams showing where they want to go.

But here is a harder test:

Could someone use your strategy to decide what they should stop doing tomorrow?

That is where strategy becomes real.

Imagine an organization had only three major priorities:

• Make preventable harm exceptionally rare.

• Ensure patients move through care without unnecessary waiting or friction.

• Build clinical systems that make the right thing easier to do, every time.

The precise wording doesn’t matter. What matters is that there are only a few priorities, that people genuinely believe in them, and that they are specific enough to guide choices.

Now take the next supposedly urgent task and put it through that filter.

Does this move us meaningfully toward one of those priorities?

If it does, how much? If it doesn’t, is this something we truly need to do, and if so, what is the minimum responsible response?

Consider accreditation.

Healthcare organizations can spend hundreds or thousands of hours preparing for an accreditation visit. Some of that work is unquestionably useful. Accreditation can identify important gaps and create leverage for improvement.

But some of the preparation can also become theater: polishing documents, rehearsing answers, chasing signatures and making sure the organization looks right for the visit.

If our priorities are to make preventable harm exceptionally rare, eliminate unnecessary waiting and friction, and build systems that make reliable care easier to deliver, then the relevant question isn’t simply:

Are we ready for accreditation?

It is:

How much will the next hundred hours of accreditation preparation actually advance those priorities?

Those are very different questions.

Some accreditation work may have enormous value. Other tasks may be necessary but deserve only the minimum effort required. And some may contribute almost nothing at all.

A strategy should help us tell the difference.

Priorities have to live somewhere other than PowerPoint

Of course, defining priorities isn’t enough. You have to use them. Every single day.

If an organization says these are its most important priorities, they should become part of its operating system.

Start major staff meetings with them. Put them into committee terms of reference.

Use them during leadership rounds.

Build them into quality and safety discussions.

Ask improvement teams how their work connects to them.

Teach them during recruitment and onboarding.

Use them when evaluating leaders.

Use them when deciding who gets promoted.

Use them when deciding which projects receive staff, money and executive attention.

If a value disappears the moment an inconvenient decision arrives, it isn’t really a value.

And if a strategic priority has no effect on where an organization spends its time, it isn’t really a priority.

The priorities shouldn’t be invented in a boardroom

There is another trap here.

It would be easy for a small group of leaders to disappear into a room, emerge with five beautifully worded priorities, print them on banners and announce that these are now everyone’s values.

That is not the same thing as alignment.

One of the most powerful questions in healthcare is also one of the simplest:

What matters to you?

Ask patients. Ask families. Ask nurses, physicians, therapists, housekeepers, clerks and porters. Ask the people working nights. Ask the people who have been there for twenty years.

Ask the people who arrived three months ago and can still see things everyone else has stopped noticing.

People are usually very willing to tell you what matters to them.

And if the same ideas keep appearing across hundreds of conversations, that is far more meaningful than anything a leadership team could manufacture on a retreat.

The job of leadership is not simply to announce values.

It is to discover what people care about, translate that into a coherent direction and then keep showing, through decisions, that those things really matter.

Then comes the hard part: letting some fires burn

Even with clear priorities, the urgent will keep arriving. That part doesn’t stop.

The challenge is developing the organizational courage not to respond to all of it.

There is something psychologically uncomfortable about deliberately not fixing a problem.

An issue lands in your inbox. Somebody is unhappy. A metric turned red. A case review found a weakness. A committee has made a recommendation.

The instinct is to act. Doing something feels responsible and doing nothing can feel deeply uncomfortable.

But in a system with finite capacity, saying yes to one problem is always an implicit decision to say no to something else. The cost is simply harder to see.

Maybe organizations should keep track not only of the problems they solved, but also of the work they deliberately chose not to do.

Call it a stop-doing list.

Or a list of fires we chose not to fight.

Whatever the name, there is something powerful about making those decisions visible.

Imagine a leadership meeting where someone said:

We identified seven issues this month that could have generated new projects. We deliberately declined five because they were not connected closely enough to our priorities.

That should not be embarrassing. It should be evidence of discipline.

We are very good at celebrating people who take on more work.

Perhaps we should get better at celebrating people who protect the organization from low-value work.

Some important problems still won’t fit the strategy

There is a complication.

Sometimes we discover a legitimate problem that does not align neatly with our strategic priorities.

A reported event reveals a process weakness. A chart review identifies an inconsistency. A patient complaint exposes something that should be fixed.

The problem is real. But it may not justify turning the organization toward it.

This is where improvement systems can accidentally undermine strategy.

If our rule is that every identified problem deserves a project, then the portfolio of improvement work will simply reflect whichever problems happened to surface.

Over time, the organization accumulates hundreds of initiatives scattered across countless topics.

Everything is being improved. Nothing is being transformed.

The answer cannot be to ignore every problem outside the strategic plan. But neither can the answer be to treat every problem as equally deserving of resources.

We need different levels of response. Some problems deserve major improvement programs. Some deserve a small local intervention. Some need a reminder, a checklist change or a simple process fix. Some should be monitored.

And some should simply be acknowledged without launching anything at all.

This is where a second filter becomes useful.

Once we have decided that something deserves attention, ask:

How much impact might this have, and how much effort will it require?

A simple effort-impact matrix can prevent a two-hour problem from turning into a six-month project.

Strategy should determine where we are trying to go.

Impact and effort should help determine how aggressively we pursue each opportunity along the way.

Attention may be healthcare’s scarcest resource

Money is scarce. Staff are scarce. Time is scarce.

But underneath all three is another resource that may be even more constrained:

organizational attention.

There are only so many problems an organization can think deeply about at once.

Only so many improvement projects leaders can meaningfully support.

Only so many changes frontline teams can absorb.

Every additional priority dilutes the others.

That means one of the most important jobs of leadership is not generating more activity.

It is protecting attention.

Clearly define what matters.

Repeat it until everyone knows it.

Use it to decide where to put people, money and time.

Create permission to leave some problems alone.

And reward people who are disciplined enough to distinguish a true priority from the latest thing screaming for attention.

Healthcare will never run out of fires.

The goal isn’t to extinguish all of them.

It is to make sure we still have enough water left for the things that matter most.

Continue the conversation

What is one thing your organization spends too much time on because it feels urgent, even though it is not especially important?

I’d especially like to hear from people who have found good ways to protect attention, say no to low-value work, or keep strategic priorities from getting buried under day-to-day demands.

If this was useful

Subscribe to Leading Quality for future essays on healthcare quality, safety, improvement, leadership and organizational learning.

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Share this article with someone who is trying to help a healthcare organization focus on what matters most.

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Respond to the question above. I read the comments and often use the discussion to shape what I explore next.

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High reliability is often treated as a collection of visible practices: safety huddles, rounding, event review, reporting systems.  But Nicholas Testa argues that the practices matter only if they create an organization that can reliably surface risk, learn, and respond. The harder leadership problem is durability: can those behaviors continue when the CMO changes, the CEO turns over, or the original champion leaves? This conversation examines the organizational scaffolding that makes safety less dependent on personality. It also surfaces a critical tension: psychological safety cannot mean the absence of accountability. Leaders need people to report mistakes, near misses, and uncertainty without fear, while still creating clear expectations that problems will be examined and acted upon. The goal is not simply a safer culture, but a system capable of seeing and correcting its own weaknesses.

Key Ideas Explored

• Psychological safety and accountability have to rise together. Testa describes high accountability without psychological safety as an environment of anxiety, while psychological safety without accountability can drift toward apathy. The leadership task is to make it safe to expose problems without lowering expectations for responding to them.

• People learn whether it is safe to speak up by watching what leaders repeatedly do with bad news. Testa recalls a CEO whose consistent response to serious problems was essentially: thank you for telling me; now what are we going to do about it? Predictability matters because culture is built from repeated responses, not declarations about openness.

• Improving safety reporting can initially make an organization look less safe. When Testa pushed hospitals to conduct regular event reviews and encourage reporting, his region began accounting for a disproportionate share of reported events. That increase was not necessarily deterioration; it exposed previously hidden problems and created the information needed to learn, track patterns, and eventually reduce harm.

• Near misses may contain more learning than the harm events leaders naturally prioritize. Serious events demand attention, but precursor events can show where defenses nearly failed before a patient was harmed. Testa’s work on lost and mislabeled specimens illustrates how something routinely categorized as a near miss can reveal substantial patient burden and recurring system weakness once leaders examine it closely.

• High reliability requires an operating structure, not a collection of rituals. Testa’s core scaffolding includes visible senior leadership commitment, structured rounding, a daily safety huddle, and multidisciplinary event review. A huddle is not “high reliability” merely because it exists; it earns that value only when information moves through it, problems are acted upon, and feedback loops close.

• Metrics can become dangerous when protecting the metric replaces seeing reality. Testa describes a hospital nearing 350 days without a harm event that resisted classifying a medication error as serious because doing so would reset the count. The episode exposes a recurring safety problem: once an organization becomes attached to the appearance of success, accurate classification can begin to feel like failure.

Takeaways for Quality Leaders

• When reporting suddenly increases after a safety initiative, ask whether performance worsened or whether the organization has finally become better at seeing what was already happening.

• Examine your own response to bad news. Would staff who watched you handle the last serious event predict curiosity and action?  Or would they expect anger, blame, defensiveness, or surprise?

• Test the durability of your safety system by imagining the current senior leaders disappearing tomorrow. Which practices would continue because they are embedded in operations, and which survive only because a particular person keeps pushing them?

• Look beyond the events that reached the patient. Ask whether your review processes have enough bandwidth to identify recurring near misses and precursor events before they become the next serious harm.

• Audit cherished performance streaks and “days since” metrics for unintended consequences. If acknowledging an event feels organizationally costly, the measure itself may be creating pressure not to see the system clearly.

Continue the Conversation

Dr. Testa 

...on LinkedIn

...via Email: ntesta@sentact.com

Resources & Frameworks Referenced

• High reliability organizations (HROs) — the broader framework Testa uses to distinguish dependable outcomes from merely adopting safety practices.

• Amy Edmondson’s work on psychological safety and accountability — referenced in framing the relationship between safety, anxiety, apathy, and high performance.

• Deming — referenced in discussing the importance of defining what good healthcare outcomes actually mean before attempting to improve them.

• Safety huddles, leadership rounding, and safety event roundtables — the core operational scaffolding Testa describes for making high reliability durable.

• Good catch programs — a mechanism for recognizing and reinforcing the identification of near misses before they reach patients.

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Healthcare will not improve dramatically if quality improvement remains the domain of a relatively small group of specialists.

We need many more people across healthcare to understand how to improve the systems around them.

But I’m not sure we always teach beginners the most important things first.

In this piece, I’ll explore five habits I would want someone to learn before worrying too much about the tools:

• Get unusually precise about the problem.

• Assume you understand less of the system than you think you do.

• Treat resistance as information.

• Make the problem small enough to learn from.

• Measure to learn, not merely to prove that you succeeded.

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Healthcare has no shortage of problems worth improving.

Patients wait too long. Important information gets lost. Discharges are delayed. Clinicians spend time on work that adds little value. Processes that seem straightforward on paper become frustratingly difficult in practice.

And yet the number of people who are actually trained to improve these systems remains relatively small.

If we want healthcare to get dramatically better, improvement cannot remain the work of a small group of quality professionals. We need many more physicians, nurses, pharmacists, therapists, administrators, patients, and others to see improving the systems around them as part of their work.

But I’m not sure we always introduce people to quality improvement in the right way.

We often begin with the machinery: PDSA cycles, process maps, driver diagrams, run charts, Lean tools, project charters.

Those things can all be useful.

But before someone learns the tools, there are a few ways of thinking that matter even more.

If I were introducing someone to healthcare improvement for the first time, these are the ideas I would want them to understand.

1. Don’t start with the solution

Healthcare professionals are trained to solve problems.

A patient has a symptom. We gather information, make a diagnosis, and decide what to do. And that instinct is useful clinically but it can also get us into trouble when we try to improve systems.

We notice that discharge takes too long and decide we need a discharge checklist.

We see medication errors and decide staff need more education.

We notice delays getting patients to imaging and decide we need another porter.

The solution can arrive almost simultaneously with the recognition that a problem exists. And while that can sometimes be the right solution, more often it falls short. In complex systems like healthcare, heuristics and knee-jerk solutionizing seldom works.

One of the most useful habits in improvement is learning to separate the problem from your first idea for fixing it. Start by forcing yourself to describe the problem precisely.

Who is experiencing it?

Where does it occur?

How often?

Under what circumstances?

What is happening now that should be happening differently?

Write the problem down. Show it to other people who work in the system. Ask whether they recognize the same problem.

This sounds almost absurdly basic. I promise it isn’t.

A vague problem such as “discharge is inefficient” can support almost any proposed solution. A much more specific problem might be: patients medically ready for discharge frequently wait several hours for a particular step in the process. This framing dramatically changes what you need to understand next.

Problem definition is not administrative housekeeping before the real improvement begins. It is part of the improvement.

2. Assume you understand less of the system than you think you do

When I was a resident learning to place central lines, one lesson was drilled into me repeatedly: prepare the environment before you start.

Position the patient properly. Make sure the ultrasound is working. Have the equipment you need within reach. The procedure starts before the needle ever touches the skin.

Improvement works the same way.

One of the most important things you can do before changing a system is understand who actually knows how that system works.

That sounds obvious. It often isn’t.

Healthcare creates expertise in narrow slices of enormously complicated processes. A physician may understand one part extremely well while knowing very little about what happens before or after it. A hospital leader may understand the intended process without seeing dozens of workarounds that frontline staff use every day.

I once watched a physician pursue what seemed like a straightforward improvement: introducing a better patient gown.

The new gown appeared superior for patients and clinicians. But the project quickly ran into problems because some of the people responsible for purchasing, infection control, laundering, and managing the gowns had not been involved early enough.

They knew things about the system that the project leader did not.

This is one of the easiest mistakes to make in improvement: confusing expertise in your own work with expertise in the whole system.

Before changing something, ask:

Who actually performs this work?

Who depends on it?

Who works immediately upstream and downstream?

Who will have to do something differently if the change succeeds?

Who sees failure modes that I never encounter?

This is often called stakeholder engagement, which makes it sound like a communications exercise. I believe it is more important than that.

You are trying to assemble a more accurate picture of reality.

3. Treat resistance as information

Once people start proposing changes, another predictable phenomenon appears: someone objects.

The new workflow will take too long. The technology won’t work in a particular setting. The proposed process creates extra work. A department says it cannot support the change.

It is tempting to categorize these people as “resistant to change.” Sometimes people really are resistant to change. But that explanation is often too convenient and deserves scrutiny.

The person objecting may understand something about the system that you don’t. Perhaps your proposed workflow works beautifully on weekdays but falls apart overnight. Perhaps it solves a problem for physicians while inadvertently creating more work for nurses. Perhaps an apparently redundant step exists because of a failure that happened years ago.

Perhaps the person who seems difficult has watched three previous improvement projects arrive with enthusiasm and disappear six months later.

An objection is not automatically correct. But it is data.

Instead of asking, “How do we overcome the resistance?” a better first question is:

What might this person know that we don’t?

That question changes the conversation.

It also prevents stakeholder engagement from becoming a euphemism for persuading people to accept something that has already been decided.

Good improvement work does not merely seek buy-in. It seeks understanding.

4. Make the problem smaller

Ambitious people tend to choose ambitious improvement problems. We want to fix patient flow, improve safety, reduce burnout, and redesign disharge.

These are worthy goals. They are also enormous systems.

One of the most valuable things a beginner can learn is that narrowing the scope of a project is not the same as lowering its ambition. It is often what makes learning possible.

Suppose your hospital has a discharge problem. You could attempt to redesign discharge across every medical and surgical unit simultaneously.

Or you could ask a much narrower question:

Can we improve one part of the discharge process, for one patient population, on one unit?

While the second version can feel less impressive, it also gives you something the first version often does not: the ability to see what happens when you change the system.

You can learn quickly, discover unintended consequences, and revise the intervention.

And, if it works, you now have something concrete to build on.

Trying to change too much at once makes it harder to know why things improved, or why they didn’t.

Small scope is not small thinking. It is often disciplined experimentation.

5. Measure to learn, not just to prove that you succeeded

Eventually, improvement requires data.

But beginners are often introduced to measurement as if it were the scoreboard at the end of a project.

Did the intervention work?

Did the metric improve?

Can we demonstrate success?

Those questions matter. But measurement can do something more valuable: it can tell you whether your understanding of the system was correct.

Suppose you believe a new process will shorten a delay.

How much?

For which patients?

How quickly?

What else might change as a result?

Before making the change, make the prediction explicit.

Then look at what actually happens.

If the result is different from what you expected, that is not merely a disappointing outcome.

You have learned something.

Perhaps the bottleneck was somewhere else. Maybe the staff used the new process differently than anticipated. The intervention may have worked only under certain conditions. Or, perhaps you improved one measure while worsening another.

This is why measurement in improvement should not be thought of primarily as proof. It is feedback.

And feedback is what allows a team to move from “we think this will work” to “we are beginning to understand how this system behaves.”

Improvement starts before the tools

None of this means that improvement methods are unimportant. Quite the opposite, they are essential.

People must ultimately learn how to map processes, develop measures, test changes, understand variation, and use structured methods if they want to pursue improvement seriously.

But those tools work much better when they sit on top of a few foundational habits:

Get unusually precise about the problem.

Assume that other people understand parts of the system that you do not.

Treat disagreement as a potential source of information.

Make the problem small enough that you can actually learn from changing it.

Use measurement to test your understanding of the system, not merely to validate your solution.

Healthcare needs more people who know how to improve it. That does not mean everyone needs to become a quality improvement expert. But imagine what would happen if many more people working in healthcare developed these habits.

They might be slower to jump to solutions and more curious about how work actually happens. The might be more willing to test their assumptions and pay closer attention to the ideas of the people around them.

And better equipped to turn the frustrations they encounter every day into opportunities to make care better.

That seems like a very good place to start.

Continue the conversation

If you were teaching quality improvement to someone for the first time, what is the single most important idea you would want them to understand?

I’d especially like to hear from people who teach improvement, lead QI programs, or remember what they found confusing when they first entered the field.

If this was useful

Subscribe to Leading Quality for future essays on healthcare quality, safety, improvement and organizational learning.

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Share this article with someone who is learning quality improvement—or someone who teaches it.

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Respond to the question above. I read the comments and often use the discussion to shape what I explore next.

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Interested in partnering with Leading Quality?

If your company is aligned with our goal to improve healthcare, click here to discuss sponsorship opportunities.

Healthcare organizations cannot learn simply because they have dashboards, safety reports, improvement methods, or formal escalation processes. Learning depends on whether people can surface what leaders do not know, challenge assumptions, admit uncertainty, and act on problems without making truth-telling personally dangerous. This second year-in-review episode examines the human conditions that make a learning system work: leadership humility, trust, psychological safety, accountability, high expectations, and agency. The central tension is that these ideas are often treated as opposites. Leaders are expected to be confident, yet must remain open to being wrong; organizations need psychological safety, yet cannot abandon accountability; and improvement should support people without lowering the standard of care. The challenge is to build systems that are demanding about the work while remaining curious about why reality falls short.

Key Ideas Explored

• Leadership increasingly means leading knowledge you do not personally possess. As problems become more complex, the leader’s advantage cannot be knowing more than everyone else. The work shifts from providing answers toward connecting expertise, asking better questions, and creating conditions in which the organization can use what its people collectively know.

• An open door is not an information system. Leaders inevitably operate with an incomplete picture of their organizations, and simply inviting people to raise concerns places too much responsibility on those with less authority. Rounding, specific questions, escalation mechanisms, safety huddles, and deference to expertise can reduce the personal courage required to make important information visible.

• The goal is to lead a less imaginary organization. Every leader carries a mental model of how the organization works, but certainty can cause contradictory information to be filtered out until the picture reaching leadership becomes cleaner and less true. A learning posture treats unexpected observations as evidence that the model may need revision rather than as resistance to be overcome.

• Psychological safety and high expectations are not competing choices. Taking a stand on zero harm can express what an organization believes patients deserve without pretending that perfect performance will be achieved immediately. The gap between aspiration and reality should become a source of disciplined learning rather than an automatic trigger for blame.

• Accountability and systems thinking require separate questions. Asking what the system needs to learn is different from asking what accountability is appropriate. Mature organizations must distinguish human error, risky behavior, and system failure rather than defaulting either to punishment or to the idea that individual choices never matter.

• Improvement can create agency rather than additional burden. Repeated workarounds and poorly designed processes can produce helplessness. Improvement becomes a “science of hope” when people have a method for understanding problems, testing changes, redesigning their work, and removing unnecessary friction—including through subtraction rather than continually adding new requirements.

Takeaways for Quality Leaders

• Ask what information currently requires unusual courage to reach you. If a serious problem depends on one brave individual speaking up, the organization may have a weak information system rather than a strong safety culture.

• When a room goes silent after “Any questions?”, do not interpret silence as agreement. Reconsider the design: ask what you may be missing, where the plan could fail, and what people closest to the work are seeing that leadership cannot.

• Examine how your organization responds to performance data. The same number can open a conversation about what is happening in the system or signal that judgment has already been made.

• When performance falls short, separate aspiration from response. Maintain a demanding standard while asking what the miss can teach you before deciding what accountability is warranted.

• Before adding another checklist, alert, meeting, field, or training requirement, ask what could be removed. Improvement should not automatically mean asking already burdened clinicians to absorb more work.

Continue the Conversation

Follow Dr. Meadows on LinkedIn

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Quality and patient safety are so often paired together that it is easy to assume they are essentially the same discipline.

They aren’t.

In this piece, I want to explore three questions:

1. How did healthcare arrive at the way we think about quality and safety today?

2. What does Terry Fairbanks mean when he argues that they require different bodies of knowledge?

3. What should that distinction change about how we prepare people to lead this work?

My hope is that, by the end, you will have a clearer way to think about where quality improvement and patient safety overlap, perhaps more importantly, where they do not.

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A few years ago, Terry Fairbanks wrote on LinkedIn that healthcare routinely conflates quality and safety.

We put them in the same departments. We combine them in job titles. We talk about “quality and safety” as if the phrase describes a single discipline. But his point was that they draw on different bodies of knowledge and require different skills.

It’s a distinction I hadn’t spent enough time absorbing until my recent conversation with Terry on the Leading Quality podcast. He shared a patient safety case involving a suspended nurse and the difference started to become much more concrete for me.

I wrote a short LinkedIn post afterward about what I had learned. The response was much larger than I expected.

That made me wonder whether the confusion wasn’t mine alone.

If this distinction already feels useful, consider sharing this article with someone who works in quality or patient safety. I suspect many of us have used the phrase “quality and safety” for years without fully unpacking what it contains.

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We are still figuring out what “quality” means

Healthcare has been concerned with quality for a long time. But what we mean by quality work has changed substantially.

An earlier model was largely retrospective. We looked in the rear view mirror to see if we met standard or followed the policy. We reviewed charts, investigated cases, audited performance, checked compliance and tried to assure ourselves that care was acceptable.

That work still occupies an important foundational role.

But over time, healthcare learned the limitation of relying on defects to tell us what needed improvement. By definition, that meant our starting point was always something that had already happened.

Improvement science, Lean, PDSA, statistical process control and related approaches taught us to examine the processes, reduce variation, and consistently improve our most important outcomes.

That represented an important shift: from inspecting quality to designing for it.

The modern patient safety movement added another layer.

Safety science pushed healthcare to think more deeply about how people behave in complex systems, how technology and workflow interact, why reasonable people make errors, how hazards develop, and how systems perform when conditions differ from what their designers expected.

Human factors. Systems thinking. Just Culture. Event analysis. Resilience. Risk mitigation. Teamwork. Psychological safety.

These were not simply additional QI tools.

They came, in important ways, from different intellectual traditions.

And now our understanding of quality is expanding again.

We are increasingly talking about quality management systems, organizational learning, Whole System Quality and Quality as an Organizational Strategy, not something produced by a Quality Department, but something embedded in how an organization is managed.

None of these stages completely replaces what came before.

Together, though, they suggest something important:

Healthcare is still constructing its understanding of what quality and safety work actually consists of.

Quality and safety overlap. They are not identical.

One of Terry’s simplest distinctions is also one of the most useful.

Quality improvement often involves trying to move a process toward a known desired state.

Suppose we know the evidence-based bundle that reduces central-line infections. The challenge becomes creating a system in which the right care happens reliably.

That calls for capabilities like process design, measurement, understanding variation, standard work and testing changes.

Safety often presents a different kind of problem.

A serious event may emerge from an unexpected interaction among a clinician, a device, a workflow, an interface, environmental conditions and organizational pressures.

The questions change:

Why did this action make sense to the person at the time?

How did the design of the system shape what happened?

What hazards were present before the event?

What happens when the system is under stress?

How do we prevent an individual error from becoming patient harm?

Those questions require capabilities that many people with excellent improvement training have never formally learned.

Being skilled at Lean, Six Sigma or PDSA does not necessarily mean someone understands human factors, safety engineering, cognitive bias, event investigation or resilience.

And the reverse is also true.

There is substantial overlap.

Improvement methods can absolutely make care safer. Safety work often requires improvement methods to implement and test changes.

But overlap is not identity. Quality and safety may belong together as aims. That does not mean they come from the same science.

Sometimes the difference changes what we see

The case Terry discussed with me helped make this real.

A nurse was suspended after a serious patient safety event. A superficial interpretation could focus on the individual: someone made an error, someone failed to follow the expected process, someone therefore needed correction. But a deeper safety analysis asks different questions:

What was happening in the system around the nurse?

How did equipment design contribute?

What conditions made the error possible or even understandable?

What changes would reduce risk for the next clinician and the next patient?

That is one reason safety science matters.

If our model of safety is simply “find the bad outcome and improve the process,” we may misunderstand the nature of the problem. And if we misunderstand the problem, an excellent improvement method can help us implement the wrong solution more reliably.

What should a safety leader actually know?

Terry has described seeing candidates for patient safety leadership positions emphasize Lean or Six Sigma credentials while having little formal training in safety itself.

That should make us uncomfortable. Not because every quality leader needs another credential or because the answer is to build bigger professional silos around quality and safety.

The question is more basic. If someone has “patient safety” in their title, what should we reasonably expect them to know?

Can they analyze human error without defaulting to “the policy wasn’t followed”?

Can they recognize when poor design is shaping behavior?

Can they distinguish the way work is supposed to happen from the way it actually happens?

Can they identify hazards before enough people are harmed to create a measurable trend?

Can they design stronger mitigations than another reminder, training module or policy?

These seem like reasonable expectations. But I’m not sure healthcare has consistently defined them. Perhaps our confusion about quality and safety is not simply a failure to distinguish two established disciplines.

It may reflect something more interesting:

Healthcare is still defining what these disciplines are, what knowledge they require, and how they should fit into the way we run healthcare organizations.

The answer is not necessarily to separate quality from safety organizationally. It is to stop assuming that putting them together makes their underlying competencies interchangeable.

Because we cannot build safer systems if we have not first become clear about what it actually takes to make them safe.

Continue the conversation

What is one capability you think every patient safety leader should have that is still too rarely taught?

I’d genuinely like to hear how people working in different parts of healthcare answer this.

Listen: Leading Quality

This article was partly inspired by my conversation with Terry Fairbanks on Leading Quality, where we discussed the patient safety case that made this distinction much more concrete for me.

Leading Quality explores how healthcare systems improve, why meaningful change is difficult, and how leaders can build organizations capable of learning and improving consistently at scale.

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Healthcare organizations can run hundreds of improvement projects without becoming fundamentally better at improvement. After 26 conversations in the first year of Leading Quality, a larger question emerged: what makes an organization capable of learning repeatedly, across problems, teams, and time? This episode examines the difference between having people who know improvement methods and having a system that can recognize problems, understand the work producing them, test its assumptions against reality, and carry what it learns forward. The challenge is not to move beyond projects because projects do not matter. It is to ensure that successful projects leave behind more than better results; they increase the organization’s capacity to solve the next problem.

Key Ideas Explored

• A successful improvement project is not the same as organizational learning. The deeper test is what happens after the project ends: whether new knowledge becomes part of standard work, travels elsewhere, survives the departure of its champions, and leaves the organization more capable of solving future problems.

• The people with the greatest authority often have the least direct visibility into the work. As information moves upward, frontline experience becomes metrics, categories, and dashboards. Leaders therefore need mechanisms that connect the knowledge held by people doing the work with the authority required to change the systems around them.

• Seeing a bad outcome is not the same as understanding the system that produced it. Safety events can be recognized and investigated while still being fundamentally misinterpreted. Direct observation, frontline knowledge, human factors, and measurement reveal different parts of reality; none is an adequate substitute for the others.

• Standards can be treated as hypotheses rather than permanently correct rules. A standard represents our current prediction about how a process should behave. When reality differs from that prediction, the discrepancy can become an opportunity to investigate what we misunderstood rather than simply evidence that someone failed to comply.

• Quality cannot remain in the organizational “sidecar.” Quality, safety, patient experience, and operations are produced by the same underlying work. Improvement expertise remains essential, but responsibility for producing quality ultimately has to be integrated with the people who operate the system.

• Learning requires infrastructure, not heroics. Training people in improvement is insufficient if they return to environments without meaningful problems to work on, protected time, coaching, data, governance, or mechanisms for spreading what they discover. Capability becomes organizational only when the system knows how to use it.

Takeaways for Quality Leaders

• Ask what happens to knowledge after a successful improvement project. Does it alter how work is done and make the next improvement easier, or does it remain primarily with the team that generated it?

• When an important metric changes, resist moving immediately from the number to a solution. Ask what the measurement reveals, what you would need to observe directly to understand the work producing it, and what the people doing that work know that the metric cannot tell you.

• Before acting under uncertainty, make the prediction explicit: What do we expect to happen, and why? Then compare that expectation with reality and investigate meaningful discrepancies, including results that are better than expected.

• Examine your organization’s theory for how improvement actually happens. If someone identifies a recurring problem tomorrow, where does it go, who responds, who can act, how can frontline knowledge shape the investigation, and how does anything learned travel?

• Consider whether your improvement capability exists mostly in individuals or in the organization itself. The critical question is not simply how many people have been trained, but whether their knowledge has somewhere to go.

Resources & Frameworks Referenced

• Flanders Quality Model — an integrated approach to quality management

• Physician Quality Improvement program in British Columbia

• East London NHS Foundation Trust improvement journey

• The High Velocity Edge

• SeamlessMD

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Healthcare has a complicated relationship with standards.

We need them. We resist them. We create them. We ignore them. We audit them. We turn them into policies, protocols, checklists, accreditation requirements, and laminated cards stuck beside computer monitors.

And clinicians often hear the word standardization as something vaguely threatening: someone who does not understand my patient, or perhaps does not understand my work, is going to tell me exactly how to do it.

But a conversation with Dr. Steve Spear on the Leading Quality podcast gave me a different way to think about standards: as hypotheses.

As I see it, a standard can be at least three different things.

It can be a rule: Do it this way.

It can be a benchmark: This is what good looks like.

Or it can be a hypothesis: Based on what we currently know, if we do these things under these conditions, we expect these outcomes.

That third interpretation changes almost everything. A hypothesis is not asking for obedience because authority has spoken. It is making a claim that can be tested.

And perhaps that is exactly how we should think about standard work in healthcare.

A team of scientists

The hypothesis framing makes me imagine a hospital, clinic, surgical ICU, management team, quality department, or any other collection of people in healthcare as a team of scientists engaged in a continuous series of experiments.

In some ways, that is already what we are. We observe, form theories, intervene, look at what happens, and revise our understanding.

But there is an important constraint. Once we have discovered something better, our next experiments should be conducted on top of that new knowledge.

We cannot perpetually allow ourselves to do whatever we like when we already have a current best hypothesis. I emphasize ourselves deliberately because standardization is often framed as something leaders impose upon frontline workers: we decide how they should work. But if a healthcare organization is genuinely functioning as a scientific community, the standard belongs to all of us.

Someone may initially design it from the top. It may emerge from frontline experience. More likely, it should be created through some combination of both. But once we agree that this is our best current understanding of how the work should be done, everyone accepts two responsibilities.

First, we should generally work from that shared standard rather than continuously reinventing the process independently.

Second, we should continuously test the standard against reality.

The default assumption should be that the standard is never final. That preserves rigor while making the standard intellectually humble.

The opposite of standard work

I remember working at a hospital where small cards labelled “standard work” appeared beside computers. One described how patient discharge rounds were supposed to be conducted. Leadership had decided on the process, printed the cards, and placed them around the hospital.

And that was essentially the end of the experiment.

There was no meaningful feedback about whether people were actually able to conduct rounds that way. There was no test of whether the proposed process made sense in the real conditions of the units. There was no systematic mechanism for discovering why (or even knowing if) people deviated from it. There was no feedback about whether the standard itself should change. And there was little connection between adherence to the process and the outcomes it was presumably intended to produce.

It was called standard work. But in many ways it was the antithesis of what standard work should be. If we describe something as a hypothesis, printing it on a card is not the end of the work. It is the beginning.

Who is accountable when the standard fails?

The hypothesis framing changes the relationship between leadership and the frontline. Most often in healthcare today, when a leader introduces a standard as a rule, it primarily serves to increase frontline accountability. The dominant question becomes:

Why didn’t you follow the standard?

But if the standard is a hypothesis, there are at least two equally important questions:

Did we do what we said we would do?

and

When we did, did it actually produce what we expected?

That second question changes the power dynamic. It makes the standard itself accountable. A clinician who repeatedly discovers that following a standard does not produce the predicted result is not necessarily being resistant. They may be generating evidence that the organization’s theory is wrong.

If I tell hundreds of people that this is the best way to perform an important task, I should have an unusually strong interest in discovering evidence that I am wrong. Unfortunately, organizations can sometimes behave in exactly the opposite way.

Once a process has become a policy, protocol, approved workflow, or accreditation requirement, considerable institutional energy may go into defending compliance with it.

The hypothesis framing asks us instead to actively look for disconfirming evidence. That does not mean every clinician gets to ignore a standard whenever they disagree with it. Quite the opposite. It means that we must agree to disciplined adherence to our current hypotheses until evidence shows they should be revised. By doing this, deviation, failure, and unexpected outcomes become critical information.

Not all variation means the same thing

Imagine that a patient does not improve after a standardized process is followed.

There are several possibilities.

Perhaps the standard was never actually followed.

Perhaps it was followed, but the patient was meaningfully different from the population or circumstances for which the standard works.

Perhaps the environment made reliable execution impossible.

Or perhaps the standard itself is wrong.

Those are four very different learning opportunities. Yet traditional compliance systems can flatten all of them into a single category: variance.

That wastes information.

If we genuinely believe our standards are hypotheses, then every meaningful deviation or unexpected outcome becomes an opportunity to understand what kind of failure occurred. Did the process fail? Did the environment fail to support the process? Did the prediction fail? Or did we encounter a condition our current theory does not adequately explain?

That is much closer to science.

We already know how to do this with patients

There is a striking contradiction here.

Clinicians already think this way constantly. Suppose I prescribe a medication. I do not normally think: I prescribed the evidence-based medication, therefore the job is finished.

I have made an intervention based on a prediction. After that intervention I expect the blood pressure to fall, the pain to improve, the infection to respond, or the laboratory value to change. And then I follow up.

If reality disagrees with my expectation, I rethink the diagnosis, treatment, dose, adherence, physiology, or perhaps the entire theory of what is going on. That is normal clinical reasoning.

But our approach to organizational interventions can be considerably less disciplined. Sometimes it is not even: I prescribed the evidence-based medication, therefore the job is finished. It is closer to: I prescribed a medication that seems pretty good according to my intuition, therefore the job is finished.

We introduce a new committee, change the workflow, redesign rounds, create a policy, add a form or EHR field. We train everybody and then we move on.

This connects to another point Steve made in our conversation. Clinicians already know how to examine, diagnose, treat, and follow up. The missed opportunity is that we often fail to apply that same discipline “a step or two or three away from the bedside”—to the systems that shape the care our patients ultimately receive.

What would happen if we treated the system itself with the same clinical discipline we bring to the patient?

A standard should generate evidence about itself

That leads to another important question:

How do we design standards that generate evidence about themselves?

An effective standard should ideally help us answer whether the work happened as we expected and whether it produced the result we expected. Consider the medication analogy again.

A weak standard might say:

When condition Z is present, give medication X at dose Y.

A stronger standard would implicitly contain more:

When condition Z is present, give medication X at dose Y. Confirm that it was administered correctly. Look for response A within time B. If response A does not occur, reassess.

Now the standard contains not merely an action but a test of the theory.

Healthcare already contains examples of this. Barcode medication administration can detect some mismatches at the moment work is performed rather than discovering them later through audit. A surgical count reconciles what should be present with what is actually present. Teach-back gives us an immediate test of whether a patient actually understood what we intended to communicate. Closed-loop systems for diagnostic tests can detect when an expected acknowledgement or follow-up has not occurred.

Clinical pathways can specify both an intervention and the expected response, but there are enormous opportunities to go further. Admittedly, there are also many situations where this is extremely difficult to design.

What makes this hard in healthcare is that so much of the important work is not directly observable. We can’t routinely observe, and certainly not in real time, whether the clinician recognized that the patient’s condition was changing. We often learn this only by speaking with the clinician long after the fact, when memories may have faded and their recollection may be shaped by the circumstances in which it is elicited.

Similarly, we can’t see if a handoff communicated the most important uncertainty, the receiving clinician understood the contingency plan, or the nurse knew which change should trigger escalation.

Patients discharged from hospital may not know how to take their medications or under what circumstances to return to hospital, and our records alone won’t capture this.

In these scenarios, an EHR checkbox telling us that something was “done” may be a remarkably weak test of whether the underlying work actually happened.

So what might standards that generate evidence about themselves look like?

Perhaps a discharge process does not merely require that education be documented. It includes a lightweight method for confirming what the patient or caregiver actually understood and whether the next step occurred. Drs. Amy Billett and Chris Wong (Leading Quality Episode #8) provide strong examples of such education in their work in pediatric central-line care.

Perhaps a handoff tool should do more than record that a handoff occurred. In fact, use of I-PASS as a structured handoff tool already points in this direction and has been associated with substantial reductions in medical errors and preventable adverse events.1

Perhaps an escalation pathway can identify when an expected response did not happen within the anticipated time and offer help before the delay becomes harm.

Perhaps a new rounding process contains its own measures of whether the people involved were actually able to accomplish the intended work, rather than waiting six months for a retrospective audit.

Perhaps we could design digital systems that recognize recurring workarounds. If clinicians repeatedly bypass the same step, the first organizational question should not automatically be, How do we force compliance?

It might be:

What are these clinicians discovering about our standard that we don’t yet understand?

AI could change what we can observe

Artificial intelligence makes this increasingly interesting.

Computer vision is beginning to make some previously invisible clinical processes observable. Ambient systems can increasingly understand elements of conversation and workflow.2 AI video analysis is helping to recognize when patients are at risk of falls.3 EHR data can identify sequences, omissions, delays, and recurring patterns at scales that humans could never manually review.4

In principle, these technologies could help us know whether a standard was followed, where the work departed from expectation, and whether the expected result followed.

But there is a major danger here:

A learning system and a surveillance system can use exactly the same technology.

A camera, microphone, AI model, or event log can be used to help people succeed or it can be used to catch people doing something wrong. To deploy these technologies for the benefit of healthcare workers and patients alike, leaders will need to appreciate that their people really are one of the greatest resources available to the organization.

The goal should be to give them what they need to thrive, not to build increasingly sophisticated ways of constraining them.

A good system might notice that I have forgotten an important lab test required before the antibiotic I’m prescribing and give me a nudge. It might recognize that the conditions around me are making the standard difficult to follow and offer help. It might identify that a step is repeatedly failing across hundreds of clinicians and signal that the process itself needs redesign. It might make expertise available at exactly the moment it is needed.

That is very different from creating a system whose primary purpose is to accumulate evidence against the people doing the work.

But even if we can observe more, we still have to design these systems in a way that helps clinicians rather than burdening them.

There is another constraint.

If we want people to explain meaningful deviations from standards, the mechanism cannot itself make clinical work worse. During a resuscitation, for example, an AI system might appropriately flag an amiodarone dose that appears inconsistent with the expected sequence because the discrepancy could matter immediately. What would not make sense is interrupting the team to demand that a physician document, in real time, why they departed from a protocol. Likewise, requiring contemporaneous justification during an urgent surgical procedure could increase risk rather than reduce it. The standard should create accountability for meaningful deviation without turning every deviation into an interruption.

On balance, the design of these standards would not aim to increase documentation and would be mindful of the real-world value of that documentation. Instead it would provide a way to capture meaningful deviations from expected practice in a way that supports learning, quality of care, and the well-being of clinicians and patients. The design challenge is to create visibility without creating friction.

Standards don’t prevent experimentation. They make improvement possible.

There is another reason the hypothesis framing matters. Standards are sometimes portrayed as the opposite of creativity, autonomy, or experimentation.

I think the reverse is often true.

Without a standard, we may already have enormous amounts of experimentation. But it is experimentation in all directions, at all times, conducted independently by hundreds or thousands of people.

One clinician does it this way. Another does it slightly differently. A third developed a workaround years ago. A fourth learned another process during residency.

Nobody necessarily knows that these experiments are occurring. Their results aren’t analyzed and others never get to learn from those that succeed. That is not a learning system. It is uncontrolled experimentation without observation.

This is also occurring in a setting where creative energy itself is a limited resource. Since, under the proposed hypothesis-as-standard framework, we are still asking clinicians to deploy their creativity, we owe it to them to create conditions where they can do that only when it is most useful. Creativity to deploy endless workarounds is not creativity.

A standard gives us a current shared baseline. Now, when someone finds something better, there is something against which it can be compared. If it works, the standard can change and the next round of experimentation begins from a more advanced starting point.

In that sense, the standard is not what prevents experimentation.

Follow the best current standard when appropriate, make meaningful deviations visible, observe the results, and investigate anomalies. Revise the standard when reality tells us our hypothesis can be improved. Repeat.

Rigorous and provisional

I increasingly think this may be a much more useful way to talk about standardization with clinicians. Professional judgment matters more than ever and should be the fuel for our improvement.

We can acknowledge that many great ideas emerge from the frontline while leaders continue to exercise their responsibility to design systems and set institutional priorities.

Leaders can set standards. Frontline clinicians can create standards. Both can challenge and improve them.

But everyone, including the people with the most organizational authority, has to accept the same bargain:

This is our best current hypothesis. We will take it seriously enough to follow it, and we will remain humble enough to try to prove it wrong.

Perhaps the most scientific healthcare organizations will be those that understand standards not as fixed truths, but as our best current hypotheses. They may be the ones that hold their standards most rigorously and most provisionally.

And perhaps that is the real opportunity.

Not fewer standards. Better hypotheses.

Listen: My full conversation with Dr. → Spotify | Apple Podcasts | Other Platforms

A note on Leading Quality

This is the first edition of the Leading Quality newsletter. Every other week, I’ll explore ideas about how healthcare systems improve, why meaningful change is difficult, and what leaders can do to build organizations capable of learning. These essays will draw on research, my own experience, and conversations from the Leading Quality podcast.

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References

Healthcare organizations often tolerate ambiguity, workarounds, and recurring operational problems until they produce serious harm. Dr. Steve Spear explains how leaders can apply the same disciplined thinking used in clinical diagnosis to the systems in which care is delivered—making problems visible early, investigating them rigorously, and building problem-solving capability throughout the organization.

Key Ideas Explored

• High-performing organizations compete through distributed problem-solving capability.

• Standards work best as testable hypotheses, not rigid commands.

• Ambiguity and workarounds allow small problems to become serious failures.

• Amplification, slowification, and simplification create the conditions for learning.

• Leadership means developing others’ ability to see, solve, and share problems.

Takeaways for Quality Leaders

• Go directly to the point of work and observe where staff experience friction or suffering.

• Create clear expectations that make deviations and emerging problems visible.

• Respond to reported problems with curiosity, presence, and practical support.

• Pause when work departs from expectations rather than pushing teams to work around it.

• Build capability in frontline leaders so improvement spreads without depending on a large central quality team.

Continue the Conversation

Steve Spear on LinkedIn or through the See To Solve website

Resources & Frameworks Referenced

• Decoding the DNA of the Toyota Production System

• Fixing Health Care from the Inside, Today

• The High-Velocity Edge

• Wiring the Winning Organization

• See to Solve

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Patients spend most of their lives outside the hospital, yet healthcare teams often have limited visibility into what happens after discharge, between visits, or during long-term recovery. In this episode, Dr. Joshua Liu discusses how SeamlessMD was built to close that gap by helping patients navigate clinical journeys, giving care teams better insight into patient progress, and showing why successful health technology depends as much on workflow, incentives, and leadership alignment as it does on the product itself.

Key Ideas Explored

• The “black hole” after discharge and why patients need more support outside the hospital

• SeamlessMD as a healthcare GPS for surgery, chronic disease, cancer care, pregnancy, and recovery

• Why surgical pathways were easier to scale than complex chronic disease management

• How workflow fit determines whether health technology helps or burdens care teams

• Why strong outcome data may still fail to drive adoption without strategic and financial alignment

Takeaways for Quality Leaders

• Look beyond the hospital walls when designing quality and safety interventions.

• Do not assume better data or better outcomes will automatically create executive buy-in.

• Evaluate technology by how well it fits real clinical workflows, not just by its features.

• Engage both frontline teams and senior leaders early if pilots are expected to scale.

• Use patient questions and after-hours concerns as signals for improving education, navigation, and care design.

Continue the Conversation

Joshua Liu - LinkedIn

SeamlessMD

Resources & Frameworks Referenced

• Retrieval augmented generation

• Ontario Health at Home

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Healthcare has made real gains in quality and safety, but Ken Segel argues that too much improvement work still lives as projects, dashboards, or specialist-led initiatives rather than as part of how organizations are run every day. This episode examines what it takes to move from episodic improvement to habitual excellence: a clinical operating system where safety, flow, problem solving, leadership, and accountability are built into daily work.

Key Ideas Explored

•  The Pittsburgh Regional Healthcare Initiative and the early proof that zero harm could be pursued across competing hospitals 

•  Why safety, quality, patient experience, access, and cost are all connected through the flow of care 

•  The clinical operating system: the work system, the problem-solving system, and the leadership system 

•  Why quality and safety experts should advise operating leaders rather than own the work from the sidecar 

•  The shift from rear-view mirror problem solving to real-time learning while information is still fresh 

Takeaways for Quality Leaders

•  Look at whether improvement work is central to operations or still peripheral to how care is actually managed. 

•  Treat quality and safety as operating responsibilities, not just specialist functions or compliance activities. 

•  Go to where the work happens and observe how care flows, how problems surface, and how leaders respond. 

•  Build problem solving into daily management rather than relying only on retrospective reviews and dashboards. 

•  Use discipline to free clinical expertise, not constrain it. 

Continue the Conversation

Ken Segel on LinkedIn

Ken Segel at Value Capture

The Habitual Excellence Podcast

Resources & Frameworks Referenced

•  Pittsburgh Regional Healthcare Initiative 

•  Value Capture 

•  Value Capture Canada 

•  Toyota Production System 

•  Decoding the DNA of the Toyota Production System (The Four Rules in Use) 

•  Shingo Institute 

•  Prisma Health Pulse

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Patient safety has often been built around what happens after harm occurs: incident reports, investigations, accountability, and corrective action. In this episode, Dr. Peter Lachman argues for a more proactive and moral view of safety: one where teams talk about risk every day, anticipate who may be harmed next, and make safety part of the “genome” of healthcare education, leadership, governance, and frontline work.

Key Ideas Explored

•  The early safety event that became Dr. Lachman’s “big why” for patient safety work. 

•  Why professionalism and good intentions are not enough to make care safe. 

•  The shift from retrospective harm review to proactive risk prediction. 

•  The SAFE program as a practical way to help frontline teams talk about safety every day. 

•  Why safety and quality need to become social movements, not just programs or products. 

Takeaways for Quality Leaders

•  Treat adverse events as signals of system design, not simply individual failure. 

•  Build daily routines that help teams ask who is at risk before harm occurs. 

•  Make safety part of training, clinical reasoning, and leadership language from the start. 

•  Pay attention to culture: what people talk about, what they notice, and what they are willing to learn from. 

•  In low-resource settings, do not underestimate the power of people, relationships, and practical methods. 

Continue the Conversation

Dr. Peter Lachman on LinkedIn

Resources & Frameworks Referenced

•  SAFE program / Situation Awareness for Everyone 

•  Patient Safety Movement Foundation Kiani Fellowship 

•  The Institute for Healthcare Improvement (IHI) 

•  To Err Is Human (Institute of Medicine Report, 1999)

•  Crossing the Quality Chasm (Institute of Medicine Report, 2001)

•  Donabedian’s structure-process-outcome model 

•  SEIPS model / Systems Engineering Initiative for Patient Safety 

•  Safety-I and Safety-II 

•  Great Ormond Street patient and family safety reporting work 

• FlaQuM / House of Trust model 

•  Quality 1.0, 2.0, and 3.0 

•  Patient safety as a social movement

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Healthcare organizations often know they have problems with flow, safety, delays, frustration, and waste, but they may not actually see the work clearly enough to solve them. In this conversation, Maria Mentzer explains how See to Solve helps organizations surface hidden problems, involve the people closest to the work, and build practical problem-solving capability through simple, repeatable behaviors rather than heavy improvement infrastructure or abstract training.

Key Ideas Explored

• Why leaders often see only the tip of the iceberg of organizational problems 

• How See to Solve helps teams make work visible before jumping to solutions 

• The role of process and relationship mapping in creating shared understanding 

• Why leadership support is essential for sustaining improvement behavior 

• How small, rapid experiments help teams generate learning instead of just implementing fixes 

Takeaways for Quality Leaders

• Start with a narrow slice of real work rather than trying to solve the whole system at once. 

• Involve the people who actually do the work; they see barriers leaders often cannot. 

• Treat mapping as a way to create insight, connection, and energy for change. 

• Make leadership participation visible, practical, and sustained beyond the first workshop. 

• Build internal coaching capacity so improvement becomes part of daily work, not a consultant-dependent event. 

Continue the Conversation

Maria Mentzer on LinkedIn

Resources & Frameworks Referenced

• See to Solve

• Decoding the DNA of the Toyota Production System

• The High-Velocity Edge

•  Iceberg of Ignorance (Sidney Yoshida)

Disclosure: This episode is not sponsored. Jason has no financial or commercial relationship with See to Solve. The conversation reflects his editorial interest in the work and in Steven Spear’s contributions to improvement science.

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Healthcare organizations often treat improvement as a set of projects, tools, or training programs. Amar Shah’s work at East London NHS Foundation Trust (ELFT) and NHS England points to something larger: the long-term work of building belief, capability, infrastructure, and leadership routines so improvement becomes part of how a health system thinks and operates. This conversation explores what it takes to move from local improvement activity to organization-wide and national-scale improvement strategy.

Key Ideas Explored

• Building belief as a core design challenge in improvement 

• Moving from centralized QI support to distributed improvement capability 

• Why storytelling is essential improvement infrastructure

• Co-design as both an ethical commitment and a driver of better results 

• Scaling improvement from ELFT to NHS England’s national improvement work 

Takeaways for Quality Leaders

• Treat belief in improvement as something you must deliberately build, not something you can mandate. 

• Invest in stories that make improvement visible, credible, and emotionally meaningful. 

• Build distributed coaching capability so improvement support lives closer to the work. 

• Help boards learn improvement through better questions, better data, and better routines. 

• Use co-design early and seriously, especially when tackling complex system problems. 

Continue the Conversation with Dr. Amar Shah

LinkedIn

Resources & Frameworks Referenced

• NHS IMPACT

• NHS Frailty improvement collaborative

• Kostal & Shah, Putting improvement in everyone’s hands

• IHI/ELFT, Fostering an Improvement Culture

• Shah, Applying improvement to the co-creation of quality 

• Shah, How to move beyond quality improvement projects

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Goals-of-care conversations can profoundly shape serious illness care, but in many health systems they remain difficult to find, inconsistently documented, and hard to measure. In this episode, Matthew Gonzales and Deborah Unger describe how Providence treated serious illness communication as a systemwide quality problem, combining leadership commitment, clinician training, nursing engagement, informatics, and AI to make “what matters” conversations more visible and actionable across 51 hospitals.

Key Ideas Explored

• Why goals-of-care documentation became a “conversation in the haystack” problem 

• How Providence made serious illness communication a system priority, not a palliative care side project 

• Why training physicians alone did not move the needle, and how nurses became critical to implementation 

• The tension between standardized documentation and preserving the humanity of the conversation 

• How AI helped identify meaningful goals-of-care conversations without relying on checkboxes or dot phrases 

Takeaways for Quality Leaders

• Treat important clinical conversations as part of system design, not just individual clinician skill. 

• Build measurement only after defining what meaningful quality looks like in practice. 

• Engage the disciplines closest to the workflow; nursing involvement may reveal implementation paths leaders miss. 

• Avoid designing metrics that reward documentation behavior while missing the underlying clinical purpose. 

• Look for AI use cases where language, workflow burden, and quality measurement intersect. 

Continue the Conversation

Dr. Gonzalez -  

Email: Matthew.Gonzales@providence.org

Dr. Unger - 

Email: Deborah.Unger@providence.org
Bluesky: @qoflmd.bsky.social

Resources & Frameworks Referenced

• Providence Institute for Human Caring

• Ariadne Labs Serious Illness Conversation Guide

• Guide Successful Strategies for Operationalizing Goals-Of-Care Documentation - NEJM Catalyst

• Finding the Conversation in a Haystack: Leveraging AI to Detect Goals-Of-Care Documentation - Journal of Pain and Symptom Management

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

For years, many Belgian hospitals invested heavily in accreditation. It brought structure, standards, and visible progress. But Kris Vanhaecht and other healthcare leaders began to notice a deeper problem: when accreditation became the goal, quality could become episodic. Energy rose before the survey, then faded after the label was achieved.

The question became how to keep the useful discipline of accreditation while building something more durable. In this episode, Kris discusses the Flanders Quality Model, or FlaQuM, and the shift toward a co-created quality management system that connects bedside care, leadership, governance, culture, and shared learning.

Key Ideas Explored

• Why accreditation can help, but still fall short of sustainable quality

• The FlaQuM pillars of Think, Do, Learn

• How Juran’s trilogy informs modern quality management 

• Why leadership, culture, and context matter alongside technical quality methods 

• Co-design with clinicians, patients, executives, nurses, engineers, and other stakeholders 

• Why quality models require local translation, not simple implementation 

Takeaways for Quality Leaders

• Clarify your quality vision before beginning with indicators, audits, or standards. 

• Treat quality management as an operating system, not a quality department project. 

• Involve the people closest to the work early.

• Preserve the discipline of accreditation, but do not let the label become the aim.

• Build regular structures for shared learning across teams and organizations.

• Adapt leadership, culture, and context locally.

• Aim for quality that is sustained every day, not revived before external review.

Continue the Conversation

Connect with Professor Kris Vanhaecht on LinkedIn or through his website.

Resources & Frameworks Referenced

• Flanders Quality Model (FlaQuM)

• The Juran Trilogy: quality planning/design, quality control, and quality improvement

• Accreditation Canada

• Joint Commission International

• Safety-II

• Institute for Healthcare Improvement

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Too many healthcare organizations still respond to safety events as if the main question is who made the mistake. This conversation offers a better lens: what in the system made the event possible, and how can leaders learn early enough to prevent the next one?

Using Annie’s story, Dr. Terry Fairbanks explains why strong event review matters, why timely response matters, and why healthcare falls short when it treats quality improvement and safety management as though they require the same skills. This episode gets beneath the language of safety and into the logic of safer systems.

Key Ideas Explored

• Annie’s story as a case study in how system failures get mistaken for individual failure 

• Why event reviews should begin immediately, even before every fact is known 

• The difference between product design, implementation, and real-world use 

• Why safety requires distinct competencies from traditional quality improvement 

• A practical model of primary, secondary, and tertiary prevention in safety 

• How hospitals could use existing data streams to identify hazards before harm occurs 

Takeaways for Quality Leaders

• Do not rush to discipline before a full systems-based review is complete 

• Treat early family communication and caregiver support as core parts of the safety response 

• Ask what design or implementation factors shaped the event 

• Make sure safety expertise is in the room during technology and device implementation 

• Stop assuming quality improvement training alone is enough for patient safety leadership 

• Invest in ways to detect weak signals and emerging hazards before they become events 

• Judge mitigation strategies by two standards: effectiveness and sustainability

Connect with Dr. Terry Fairbanks

LinkedIn

Twitter / X

Resources & Frameworks Referenced

• Annie’s Story 

• The MedStar Health National Center for Human Factors in Healthcare

• Systems-based event review

• AHRQ's CANDOR Framework 

• IHI's RCA2 Framework 

• Trigger tools 

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Healthcare organizations are investing heavily in new technologies, yet many implementations unintentionally add complexity to clinicians’ daily work. This episode explores a different question: what if we deliberately evaluate tools for their ability to reduce friction and support clinician well-being?

Dr. Chris Dale and Dr. Ryan Dix discuss the development and evaluation of MedPearl, a clinical decision support tool built to streamline referrals and support frontline clinicians. Their conversation highlights why system design, not individual resilience, is often the most powerful lever for improving workforce well-being.

Key Ideas Explored

• Micro-frictions in clinical workflows accumulate into meaningful cognitive and emotional burden

• Organizational interventions often outperform individual resilience strategies

• MedPearl was designed to capture and operationalize “tribal knowledge” in referral workflows

• Technology adoption spreads socially through trusted peer networks

• Measuring well-being impact requires using existing data thoughtfully

• The future of innovation must include workforce impact, not just efficiency metrics

Takeaways for Quality Leaders

• Treat clinician well-being as a system property, not an individual responsibility

• Look for “sticky note problems” that signal hidden workflow friction

• Use existing organizational data sources before launching new surveys

• Expect heterogeneous impact. Not every tool benefits every group equally

• Pair product design thinking with traditional improvement methods

• Monitor indirect indicators of well-being, not just annual survey scores

• Recognize that meaningful improvement will come from many small changes, not one solution

Continue the Conversation

Connect with Dr. Ryan Dix through the Wellbeing Trust website to learn more about Providence’s workforce well-being initiatives.
Follow Dr. Chris Dale on X (Twitter) or LinkedIn or visit Arborgenie.com to explore his work in AI and clinical data.

 This episode is especially useful for quality leaders, CMOs, CMIOs, operational leaders evaluating new clinical technologies, and anyone interested in the intersection between AI, data, quality improvement, and clinician wellbeing.

If you found this conversation valuable, consider rating, commenting, or sharing with a colleague.

Resources & Frameworks Referenced

• MedPearl clinical decision support tool

• Quadruple Aim framework

• PDSA cycles

• Epic EHR integration concepts

• Physician well-being survey methodologies

• Physicians Foundation research funding

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Many healthcare organizations say quality matters. Far fewer are built so improvement is part of daily operations. Too often, quality is treated as a department, a committee agenda, or a set of projects at the edge of the real work.

In this conversation, Dr. David M. Williams offers a different frame. He argues that quality should function as an organizational strategy: clarifying purpose, understanding the system, choosing the right work, building capability, and creating conditions for learning. For leaders trying to move beyond scattered projects and initiative fatigue, this conversation offers a more coherent way forward.

Key Ideas Explored

•  Quality is not a department. It is a way an organization pursues its purpose. 

•  Many “errors” reflect poorly designed systems, not isolated individual failures. 

•  Project work loses power when it is reactionary, peripheral, or poorly aligned. 

•  Leaders need a theory for how quality works across the organization. 

•  Shared methods make improvement more teachable, scalable, and reliable. 

•  Improvement capability must connect to governance, priorities, and daily work. 

Takeaways for Quality Leaders

•  Revisit your organization’s purpose and what it demands of the system. 

•  Examine whether your improvement work is focused on core work or safer side projects. 

•  Look for signs that quality is structurally marginal. 

•  Build a shared improvement method, not a patchwork of frameworks. 

•  Invest in helping teams get better at rigorous improvement. 

•  Treat implementation and spread as part of the work. 

•  Ask whether quality is changing how the organization actually operates.

Continue the Conversation

Connect with David M. Williams, PhD via his website or LinkedIn profile.

His next QOS Series starts in April 2026: 

https://davidmwilliamsphd.com/qos-series/

Resources & Frameworks Referenced

• Quality as an Organizational Strategy

• The QOS Field Guide

•  W. Edwards Deming’s system of profound knowledge 

•  Deming’s “production viewed as a system” 

•  The Model for Improvement 

•  IHI Whole System Quality 

•  The Chief Quality Officer Professional Development Program 

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Quality functions in healthcare often struggle with perception. Too frequently, they are viewed as auditors or enforcers rather than strategic partners in improvement. In complex environments like post-merger health systems, this perception can become an even greater barrier to progress.

In this episode, Lisa Harton, DNP, MBA/MPH, RN shares a grounded, experience-based approach to reshaping the role of quality by focusing first on relationships, mindset, and psychological safety. Her work offers practical insight for leaders trying to move from compliance-driven activity toward true system improvement.

Key Ideas Explored

• Why quality teams must first become trusted partners before driving accountability

• Using appreciative inquiry to unify teams after a merger of equals

• How clinicians move through “stages of grief” when confronted with performance data

• What healthcare underestimates about the human side of high reliability

• The role of boards and governance in advancing quality strategy

• Why changing mindsets is prerequisite to changing behaviors

Takeaways for Quality Leaders

• Start culture change by intentionally redesigning the relationship between quality and operations

• When clinicians question data, lean into joint learning rather than defensiveness

• Use established frameworks to create shared language across the organization

• Invest deliberately in teamwork and communication training, not just technical fixes

• Engage boards with accessible tools that build confidence in quality oversight

• Recognize and celebrate small wins to build momentum and trust

• Anchor improvement work in purpose and shared aspiration, especially during mergers

Continue the Conversation

Connect with Lisa on LinkedIn to continue the discussion.
This episode is especially useful for quality leaders navigating culture change, mergers, or reliability work.
If you found this conversation valuable, consider sharing it with a colleague or leaving a brief rating or review.

Resources & Frameworks Referenced

• Appreciative Inquiry framework

• IHI Whole System Quality framework

• IHI's Framework for Effective Board Governance of Health System Quality

• Barbara Fredrickson’s Positivity research

• TeamSTEPPS

• High reliability principles (nuclear industry examples)

• Root Cause Analysis (RCA)

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic

Healthcare organizations are rich with intelligence, talent, and commitment. Yet leaders across systems feel exhausted, constrained, and stuck solving the same problems year after year.

In this conversation, Dr. Laura Desveaux challenges the idea that improvement is primarily about adding more initiatives. Instead, she reframes leadership as the disciplined practice of learning, from everyday evidence, from diverse voices, and from the tensions we often try to resolve too quickly. This episode explores what it means to lead a true learning health system in operational reality.

Key Ideas Explored

• The gap between current performance and true system potential

• Learning health systems as a way of operating, not a series of projects

• De-implementation and “subtraction neglect” in healthcare

• Holding paradox: efficiency and humanity, population and individual care

• The role of co-design and implementation science in scaling improvement

• Asking better questions as a leadership intervention

Takeaways for Quality Leaders

• Start every initiative by clearly naming the problem you are trying to solve.

• Before adding a new project, ask what can be removed to create capacity.

• Integrate multiple forms of evidence: data, lived experience, front-line insight.

• Move beyond either/or thinking. Many leadership challenges are both/and.

• Build routines that embed learning into daily operations, not just pilot cycles.

• Revisit meeting structures, reporting formats, and governance processes with subtraction in mind.

• Anchor teams to shared outcomes while staying flexible on the path to get there.

Continue the Conversation

Connect with Dr. Laura Desveaux on LinkedIn or visit her website to follow her work in learning health systems and leadership development.
This episode is especially useful for senior leaders, quality executives, and clinicians navigating complex system change.
If this conversation resonated, share it with a colleague and consider leaving a review.

Resources & Frameworks Referenced

Leading Quality is a podcast for healthcare leaders committed to improving systems, culture, and outcomes.

If you found this episode valuable, follow the show, rate and review the podcast, or share it with a colleague working to improve care.

Connect with Jason Meadows on LinkedIn for more insights on healthcare quality and leadership.

Help us build this podcast  community from the ground up: share your top insight from this episode and where you’re seeing it in your own work. I read every response and will share what we’re learning over time in future episodes and other ways.

New episodes published every other Thursday at 7AM Eastern Time.

Credits:

Host, Writer, and Executive Producer
Jason Meadows, MD

Produced by
Thrive Healthcare Improvement

Edited by
Milan Milosavljevic