Nurse to Oracle: Healthcare Data & Innovation Strategy

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The Clinician’s Instinct Meets the Enterprise Stack

Healthcare’s most stubborn operational problems rarely begin in the boardroom. They surface at 1:00 a.m., during a staffing-capacity huddle, when a nurse has to decide between staying late to cover a shift or going home to feed a newborn. They show up in a supply closet when a frontline clinician opens a packet and discovers the cap needed to safely “scrub the hub” isn’t actually there. And they accumulate in the thousands of small, invisible frictions—extra clicks, workarounds, missing data—that quietly burn out clinicians and erode value.

Catherine Robison, MBA, BSN, RN has lived every layer of that reality. A former bedside nurse and nursing administrator in southeastern Virginia, she crossed the aisle into technology and now serves as Director of Healthcare Strategy at Oracle Health. Her mission sits at the intersection of leadership, wellness, and community collaboration: translate clinicians’ lived experience into data models and enterprise tools that organizations will actually use—and that actually change outcomes.

As Robison puts it, “we need better tools to do our job in healthcare.” But she’s equally clear-eyed about the core challenge: “Everyone wants to change the world… The trick isn’t having the idea. The trick is figuring out how to scale the idea.” This feature traces Robison’s journey from med–surg to tech, why she believes quantifying clinical work is a precondition for better care and better jobs, and how to build innovations that genuinely move the needle for patients, clinicians, and communities.

From Med–Surg to “Innovation Scientist”: A Journey Shaped by Real-World Pain Points

The night shift epiphany

Early in her career, Robison found herself on yet another winter capacity call—those familiar late-night huddles that stretch staff to their limits. She had already returned from maternity leave early because her unit was short. The call ended; the baby woke. The dissonance was clarifying. “We need better tools to do our job in healthcare,” she concluded. The issue wasn’t a lack of commitment; it was a lack of systems that respected time, energy, and reality.

Asking the right “naïve” questions

From the beginning, Robison’s approach to leadership was operationally grounded. “Why do I have to click so many buttons to transport the patient off the floor for a simple procedure?” That deceptively simple question nudged her into process improvement, professional practice, Magnet program work, and nurse management. She learned a pattern: frontline challenges often persist because they’re expressed in a language leaders can’t easily invest in—then solved with point solutions that sit outside core systems.

Crossing the aisle into tech

Robison joined Oracle a few months before the Cerner acquisition, working on a broad innovation portfolio that spanned epidemiology, public health, and life sciences. She loved the R&D but discovered her deepest interest was operational: the plumbing of healthcare. She now works inside Oracle’s North American applications team, where “back-office” data—human capital management (HCM), finance, supply chain, and scheduling—meets clinical reality. In plain terms: the people, money, and materials that make care possible.

Jargon check HCM (Human Capital Management): enterprise systems for workforce data (hiring, scheduling, payroll, career progression). EHR (Electronic Health Record): clinical system of record for patient care. “Point solution”: a narrow tool solving one problem in isolation rather than integrating with core platforms. “Scrub the hub”: disinfecting the access port of a central line to prevent bloodstream infections.

The Unifying Hypothesis: If We Can Quantify the Work, We Can Improve It

Robison’s north star is both simple and radical: healthcare can’t optimize what it can’t see. “Our work isn’t very well quantified,” she says of nursing. The result: clinicians ask for reasonable support (“We need more staff”), but leaders—especially those far from the bedside—can’t draw a clean line from workload to outcomes to dollars. Without that line, investments stall.

So Robison built a model early in her career to quantify time with patients and correlate it to cost. It worked—but only partially. It lived outside the health system’s core stack and demanded manual effort. The lesson? Good ideas die when they’re not embedded into existing workflows and data flows. At Oracle, her definition of success is different: co-create with customers and product teams so these measures live inside scheduling, HCM, finance, and the EHR—so leaders and clinicians both see the same truth and act on it.

“If we can create… leverage the technology that already exists, so that we can deliver joy back into healthcare,” she says, then we can track what matters—like two‑year RN turnover or the true cost of care—and change those numbers.

Leadership as Translation: Turning Clinical Truth into Business Cases

Healthcare has two valid but often incompatible dialects: the clinical language of outcomes, safety, and staffing—and the enterprise language of return on investment. Robison learned to connect them. A mentor’s advice stuck: talking only about patient outcomes won’t unlock budget; leaders also need to hear how this makes or saves money. That framing is not a betrayal of the mission; it’s the bridge that funds it.

A practical script emerges from her approach:

Name the micro‑problem precisely. (e.g., the missing central-line cap in the “cleaning packet.”)
Show its frequency and cost. (Delays, rework, infections averted, staff minutes lost, morale impact.)
Embed the fix in existing systems. (Supply chain templates, EHR order sets, scheduling rules.)
Translate to dollars and outcomes. (Retention, avoided agency spend, CLABSI reduction, HCAHPS improvements.)

This is leadership as stewardship. It’s also wellness strategy: fewer frictions and clearer priorities reduce cognitive load. The payoff compounds across teams and, ultimately, communities.

The Data Reality: Messy, Fragmented, and Full of Hidden Assumptions

Robison doesn’t sugarcoat the current state. “The data in healthcare is messy. It’s very, very messy.” Part of the mess stems from a naïve assumption that “if something is in the chart, then it’s true.” Clinicians know that documentation can be wrong, incomplete, or context-blind. Meanwhile, enterprise data lives in silos—HR here, the EHR there, supply chain elsewhere.

Every interface extracts a tax. “Every time you have an integration point… it’s one more spot where AI is going to become less efficient,” Robison notes. That’s a sobering counterpoint to breathless AI promises. Machine learning trained on flawed, disjointed data won’t magically repair upstream process problems; at best, it obscures them.

Jargon check Integration point: the handshake where two systems exchange data. Each handshake adds potential error and latency. AI vs. “agentic AI”: traditional AI models predict; agentic AI chains tools and steps to autonomously accomplish tasks. Both degrade when the underlying data is inconsistent or incomplete.

Begin with the End in Mind: Designing for Scalability, Not Heroics

Asked whether fragmentation is simply “the way healthcare is,” Robison refuses fatalism. “Oh, I refuse to believe that this is just the way that healthcare is. I’m an idealist at heart, and I believe that we’re going to change.” The route to change, however, isn’t another quick fix layered onto brittle workflows. It’s systems thinking.

Her advice: start with a precise description of the outcome you want, then work backward to the smallest measurable units of work that drive it. “We do not understand at scale the granular work that people do,” she says. But that’s exactly what we can measure—without “chasing people around with timers”—if we embed well‑designed telemetry into everyday tools: staffing rules that reflect real workload, supply chain templates aligned to infection prevention, and schedules that adapt to census and acuity without punishing staff.

Case in point: the central‑line cap A unit assumed compliance failure—nurses “not scrubbing the hub.” Robison walked to the closet, opened the packet, and discovered the needed cap wasn’t included. The fix wasn’t more training. It was a supply chain update, embedded in the kit build and verified in the EHR’s preference lists. The ROI? Fewer infections, less rework, safer care—and less moral injury for staff blamed for a system error.

Where Wellness, Leadership, and Community Collaboration Converge

Wellness is a systems property

Wellness initiatives often focus on resilience courses and meditation apps. Useful, yes, but insufficient if the work remains chaotic. Robison’s framing is refreshingly direct: wellness flows from sane workflows. Reducing clicks, interruptions, and scavenger hunts is a wellness intervention because it returns time to clinicians and reduces cortisol spikes. The nurse who gets to go home on time is a community health asset—the professional who returns tomorrow.

Leadership that listens like a clinician

Clinical training teaches pattern recognition under pressure: sense, prioritize, act, reassess. Robison argues those skills are portable—and powerful—in enterprise settings. “Your ability to communicate effectively over a short amount of time… your ability to walk into a room and know how someone is feeling both emotionally and physically and respond to that immediately… [and] assess a situation using limited… information and make a decision to act—that is a unique skill set.” Bring that to budgeting and product design, and the culture changes.

Community as co‑designer

Robison’s current role puts her in constant dialogue with customers and product teams. The most durable solutions, she argues, are “co‑authored”: frontline nurses, respiratory therapists, transport, infection prevention, schedulers, finance, and IT all shaping the same tool. That’s community collaboration at an enterprise scale—less a town hall than a product sprint.

Programs and Practices: What This Looks Like in the Real World

Advanced scheduling that respects real workload

Oracle’s applications work includes “some really cool stuff in advanced scheduling,” Robison says—an area where clinician well‑being and operational efficiency align. The idea: translate imprecise staffing ratios into dynamic workload models that consider patient acuity, care tasks, and team composition. When embedded in HCM and the EHR, schedules flex to demand without defaulting to mandatory overtime. The measurable outcomes leaders care about—turnover, recruitment costs, agency hours—move in the right direction.

Supply chain aligned to care

The central‑line example is a teachable pattern. Inventory templates should reflect infection‑prevention standards; picking lists should be validated against real‑world use; the EHR should nudge ordering toward the right bundle. The “program” here isn’t a standalone app; it’s a governance loop: frontline feedback → supply build → EHR preference → audit data → improvement. The win shows up in CLABSI rates and in trust.

Quantifying care time without a stopwatch

Robison’s early time‑with‑patients model illustrates how to operationalize “invisible” work. Today, that logic can be embedded across systems: task lists, documentation timestamps, transport metrics, and messaging data combine to approximate true workload by shift and role. Leaders get a defensible business case; teams get relief before burnout escalates to attrition.

Translating outcomes to enterprise value

Robison is blunt about the language shift. A friend once told her she needed to connect the dots to dollars. That sparked a durable habit: present improvements with dual currencies—lives and line items. The deck that lands investment is the one that shows a million‑dollar annual savings and a safer, calmer unit.

What AI Can—and Cannot—Fix

The last few years have seen AI framed as healthcare’s universal solvent. Robison is optimistic but pragmatic. AI amplifies whatever system it touches. If the substrate is disjointed, AI becomes a glossy veneer over entropy. If the substrate is integrated, the same AI unlocks deeper insights and more humane workflows.

Hence the emphasis on platform consolidation. When EHR, HCM, finance, and supply chain speak the same data language, leaders can ask—and answer—questions that actually matter: Where are we spending per patient per nurse? Which micro‑bottlenecks cost the most morale? Which schedule patterns predict resignations? That’s how AI gets pointed at the right problems.

The Moral of the Story: Clinicians Belong in the Design Studio

Robison’s closing advice to nurses and other clinicians is part affirmation, part call to action:

“Don’t lose sight of the fact that you are uniquely skilled and gifted to drive the change that you want.”
“Technology is going to change the way that we deliver care. We need to be the ones that are directing and driving and defining how care is delivered.”
Learn the enterprise dialect—how to describe value in terms of both outcomes and dollars.
Cultivate curiosity about platforms and data models; know enough to shape roadmaps.
Start small, measure precisely, and embed fixes where the work already happens.

In the end, Robison’s north star is personal. “I have been around death and I know what people say on their death bed,” she reflects. Success, for her, is moving the numbers that actually change lives: “if we can change that number of how many nurses are leaving the profession, if we can change the number associated with cost of care, that will feel like success.”

Actionable Key Takeaways

Quantify the invisible. Inventory the “micro‑frictions” on a unit (extra clicks, missing supplies, avoidable trips). Use timestamps, task lists, and message metadata to model workload without clipboards.
Translate to dollars and outcomes. Pair each improvement with both a clinical metric (e.g., CLABSI, falls, HCAHPS, turnover) and an economic metric (agency hours avoided, vacancy days reduced, overtime cut).
Embed, don’t bolt on. Prioritize solutions that live inside existing platforms (EHR, HCM, supply chain, finance). Point solutions raise integration costs and die on the vine.
Design for the smallest unit of work. Begin with end outcomes, then work backward to measurable, granular tasks. Fix what staff actually touch.
Staffing: move beyond ratios. Pilot demand‑aware scheduling that accounts for acuity and task complexity. Protect off‑time; measure the impact on retention.
Tighten the supply loop. Validate kits against real‑world use (e.g., central‑line caps). Align EHR defaults with supply builds; audit exceptions.
Build a common language. Use “bridging artifacts” (one‑page problem statements, costed user stories) that clinicians and executives can both sign off on.
Aim AI at good data. Consolidate around fewer integration points. Let AI summarize, predict, and orchestrate on top of reconciled, governance‑backed data.
Treat wellness as design. Reduce interruptions and rework; protect predictable schedules; measure emotional labor alongside throughput.
Make it a community project. Co‑design with nurses, transport, environmental services, infection prevention, finance, and IT. Durable change is a team sport.

Conclusion: Changing the System by Respecting the Shift

Healthcare transformation doesn’t start with a moonshot. It starts with the humble decision to respect a clinician’s time, attention, and judgment—and then to encode that respect into the systems that run the enterprise. Robison sums up the ethos with characteristic clarity: “I refuse to believe that this is just the way that healthcare is.” The path forward is not mysterious: listen like a clinician, measure what matters, embed fixes where work happens, and speak in a language that funds the mission. Do that consistently and the outcomes change—on the unit, at the balance sheet, and across the community.

Along the way, we should protect and celebrate the uniquely clinical superpower Robison named at the outset: “your ability to walk into a room and know how someone is feeling both emotionally and physically and respond to that immediately.” That skill—paired with data and design—may be the single most important engine of healthcare innovation we have.

the most important and pertinent information whether it's you know doing report or handoff or whatever it is. Your ability to walk into a room and know how someone is feeling both emotionally and physically and respond to that immediately. [Music] Hello, this is Zach with the American Journal of Healthcare Strategy and you are listening to the Clinicians and Leadership podcast series where we focus on empowering clinicians from bedside to boardroom. Today we have the honor of being joined by Miss Katherine Robinson. Miss Robinson, thank you for joining us today. Um, do you mind taking a second to introduce yourself, tell us a little bit about your experience um, and and your current role and how you got to where you are today? Absolutely. And Zach, thank you so much for having me on. And I've been really looking forward to this conversation. And um please go ahead and call me CJ. I have the worst name in the world to everyone in the world. It's Katherine Robinson. No end in the middle, but I go by CJ. So I blame my husband for my challenging nickname. So most important thing to know about me is I am a nurse by background. Um I started out my career as a med surge nurse um at a healthcare system here in southeastern Virginia. Um and pretty quickly moved into all things like nursing administration and process improvement. So I remember even like the second day as a nurse I was like why do I have to click so many buttons to transport the patient off the floor for a simple procedure and that question landed me you know chair of our partnership council and then magnet program director and nurse manager manager professional practice and all those good things. Um but in the execution of that role and um I remember the minute I had had enough uh I realized that fundamentally one of there there are a lot of big challenges associated with delivering health care in America but in and in general. Um but one of them is associated with the tools that we use. So, um I remember it was like midnight or 1:00 a.m. some middle of the night. We were on yet another staffing capacity management huddle which we always had in January and I was going to have to go in and staff my unit and um I had three little kids at the time and my little baby girl was only a couple months old. I'd gone back to work too early cuz I felt bad about leaving my hospital short staffed. And um as I went back to bed at like, you know, middle of the night knowing I wasn't going to have to wake up, she woke up and needed to be fed. And that's when I just was like, we need better tools to do our job in healthcare. And um that ultimately is what led me to Oracle. So I joined Oracle a couple months before they acquired Cerner. I was on an innovation team, ended up leading a portion of that team. Um, and it was a great experience, you know, and I had, you know, I will say when I made that transition from clinician to technology, I had like, you know, all of the all of the grand ambitions and aspirations. And what a cool title, right? Like to be an innovation scientist. Um and that that work was really focused on you know a really broad range of healthcare everything from epidemiology to public health to life sciences. Um and I realized in that role one that my true love was all about like healthcare operations. I love figuring out how we can not just work more efficiently, but really the fundamental hypothesis of my career is that if we can quantify the work that we deliver in healthcare elegantly, that is going to allow us to make decisions about how we optimize and where we invest and how we support our patients in a much more targeted, nimble, granular way than we ever had. So now, and I know I'm being long-winded, uh, Zach, so please forgive me, but now I am the director of healthcare strategy on the, um, North American applications team at Oracle. And I know that that's a mouthful and nobody really knows what that means. Um, basically what I do and what applications really are focused on is looking at all of like what we call the back office data. Everything from human capital management. So, you know, things like career progression and onboarding and who's working where and when, all of this, like we're doing some really cool stuff in advanced scheduling that I really love as a nurse and stuff around finances. You know, what's the and I don't I won't even get into the I I have no idea honestly how the like finance department works, but I do know that I can get access to how much we're actually spending on things. And that's what I really like in supply chain. But where I get so excited, especially sitting in this place at this point in time, is I get to work with customers. I get to work with development teams and product teams on how do we combine all of this data in a way that really supports and supports and drives all of the transformations in healthcare that we've been really longing for. CJ, I I love that story and I I think it's fascinating for a lot of reasons, but but I think one of the ones that I think is I find most fascinating is that that's not necessarily something that you your your current role is not something that when you started off as a nurse, you set out to to step into rather you said and thought there's there's got to be better ways to to solve these problems. There's there's got to be solutions that we can come up with. There's got to be ways to make things easier. And I think that that is just the the one of the fundamental reasons why we need people with that clinical experience that are serving in leadership positions and and part of the reasons why we have this podcast. And and so I I I think that that story is is something that resonates with a lot of nurses, a lot of medical staff, a lot of people that serve clinically around the world and around this country is recognizing that there are areas where their solutions and and and innovations need to happen and because they are the ones that deal with it every single day and understand they're like, man, these are a lot of hoops to jump through for this little thing. And so, um, I I I think that that story plus plus your background and experience is I'm just really excited to talk to you today about all these things, CJ. And so, I I guess starting off, um, I'm curious as we're diving into innovation, what what role does leadership play in in innovation and how do you define success in your current role? Yeah, those are two really great questions. Um, I learned a lot about innovation and the efficacy of innovation in my pri prior role. So, I'll share a little a few of those uh lessons learned. Um I've been teased recently like that I just have an inventor's mindset and an innovator's mindset. And here's the fundamental thing about innovation. Everyone wants to change the world. Everyone has good ideas on how to change the world. The trick isn't having the idea. The trick is figuring out how to scale the idea. And so, particularly in healthcare, like you go talk to any clinician, they're going to have they're going to know what what they need to fix a lot of problems that folks in leadership probably don't even know exists. Like I remember this one point this one time I was in a room and we were talking about like why folks weren't like scrubbing the hub effectively and we were like, "Hey, the the thing that you want the cap that you want us to put on the central line isn't in the the cleaning packet." and our leaders were insisting that the thing is in the cleaning packet. And then finally I just walked down the hall from the supply closet, opened the the packet and they were like I was like the the cap isn't the cap isn't in there. Um and all that to say like any clinician understands what the challenge is. The pro the the big challenge that we face in healthcare is articulating what the challenge is in a way that our leadership understands, measuring it so that our leaders care about investing to fix the challenge and then embedding the solution into something that already exists so that we are not spending additional dollars for point solutions. Instead, we're we're taking the the the solution and we're elegantly embedding it into current processes. And so, I'll give you an example of of that, right? And I'll I'll take it from my my own career. So, really early on, I knew like one of the big challenges in nursing is that our work isn't very well quantified. So, like I get paid an hourly rate, but how that money is really being used in terms of how it's impacting like where I'm working, like that is that is we have no visibility into that. And so, whenever you're making an argument for more staffing, it's really hard to to draw the line between, hey, one, I'm all these inefficiencies exist in my in my work. two, I need more staff because we're falling short on this and you know we're missing out on these outcomes etc. Like it's really hard to draw draw like a really hard straight line so that someone who's never touched a patient a CFO etc can understand the value of my work and therefore invest accordingly. So worked with the team, built a whole what ultimately turned out to be a point solution, right? Where it was like basically a model that quantified the amount of time we spent with patients and we could correlate it to um how much money that was worth, right? But at the end of the day, it was just something that sat outside our system. And because it sat outside our system required a lot of work, a lot of manual effort, etc. And so coming now over into Oracle, when I think about how I've defined success in this role, it is utilizing my knowledge of what I won't even say what work looks like in a clinical setting because I haven't touched a patient the second you stop touching patients, right? Like you need to go talk to people who are touching patients. So really my understanding of how to translate what they what folks who are doing the work need understand what leaders who are non-clinians need from a business perspective and then an understanding of what technology is capable of. Bringing all of those things together so that it is really easy to create tools that people love to use, but more importantly generate the data that we can leverage from both a clinician and a leader perspective to really optimize health care to quote unquote change the world. And this is going to sound macob, but I'm a nurse, so I get to be a little bit macob. Think, you know, one of the gifts of being a nurse is that I have been around death and I know what people say on their deathbed and uh so I think a lot about like where I'm investing my time and what I want at the end of my life to be able to ruminate on. And so if at the end of my life I can get to a point where we are able to understand how much we're investing per patient per nurse where we're able to have um the the challenges that I experienced as a nurse when it came to like even just the the administrative burden of all of that if we can create like leverage the technology that already exists so that we can deliver joy back into healthcare and we'll we'll understand that like when we look at our two-year nursing turnover rates like if we can change that number of how many nurses are leaving the profession. If we can change the number associated with cost of care, that will feel like success. And I know that that's big and ambitious and it takes a whole world to accomplish those things. But now you know way more about my life story than you ever wanted to, Zach. Well, I I think you brought up some really interesting points, CJ, and and I I think one of the the interesting things that you you talked about is is being able to put problems that your your frontline clinical staff face using data to to put that into the language that your executive suites understand and and and it's not that I mean, when you look at the health care systems, often you see clinical staff and then executives and they're speaking, they have extensive experience. Both are very accomplished professionals who have have extended experience both educationally, you know, on vocationally, lot leadership, lots of lots of fantastic individuals serving in both of those areas, but but they're speaking different languages and they've gone to school to speak those different languages and their whole careers they've spoken those different languages. And so it it sounds like data is the thing that can bridge the gap between those two. And so um just knowing that that's such a fundamental component of just operating a health care system but also innovation um I mean without data no innovation can happen and so but but as we look at this a lot of these issues still persist you mentioned staffing you mentioned administrative burnins there's burnout there's there's a lot of these issues that are still plugging healthare systems so it it seems like that the data is often incomplete or lacking and so why is that the case Why why do healthcare organizations often lack data necessary to be able to implement solutions and innovations that truly fix these issues? Yeah, that is the billiondoll question, isn't it, Zach? Um, yes. Yeah, I do associated with it, but I'm going to answer that first piece too, what you were just saying about vocabulary. So to all of you know my friends out there who want to change the world and you've been delivering health care and you want to figure out how to do that. I got some really good advice from a friend of mine about a year ago when I was struggling with getting folks to care about what I cared about. And she said, "CJ, you always talk in terms of improving patient outcomes. Like that is the value that we're trying to drive." And she's a businesswoman, right? And she said, "I need to hear how is this making money or how is this saving money?" And that was like a light bulb moment for me. And it feels brutal. Like that feels brutal to change how our our vocabulary from well I'll save x number of lives but we all know if you we save x number of lives we're also going to save or generate x number of dollars. And so she's taking that extra step to talk about hey if we do this you know if we create this thing not only is it going to improve the way that we schedule and therefore improve retention of nurses but it will also save healthcare organizations up to a million dollars a year. Right. So, it's it's taken that extra step to translate what I care about and what the business cares about, which is saving and making money. So, your question was around why is the data so messy? Why can we not get the data that we want? There's a um that was fundamentally your question, right, Zach? Like if data is the the democratizing language that we use, why is it still so broken? I think there's a couple different um reasons for that. And there are some really important ways in which clinicians are uniquely positioned to address this one. You have different stakeholders who are analyzing the data and doing things with it. Right? So like in where I said I work with sales, I work with product, I work with dev, I work with clinicians, I work and when I say clinicians like delivering care and I work with like seuite leaders, right? All of those folks are looking at the data in a different way and they have a different understanding of how that data is generated. So there is a fundamental hypothesis that if something is in the chart then it's true. You and I both know that's not necessarily true. Someone can write a note, someone can document incorrectly. Like we get all like the BP cuff doesn't fit and the reading is weird so we you know do something different and we read again like we all know the data in healthcare is messy. It's very very messy. You got discrete data over here. You have you know subjective data over here. You have notes. It's it's very complicated and it takes a clinician to understand what is the data that you need to or in order to articulate what you're trying to get at versus not like what is valuable data and what isn't right because not all data is made equal it some is valuable and some some is not I don't need 10,000 you know heart rate readings to make you know when it comes to certain things that I'm trying to build associated with uh you know whether or not I'm doing a process analysis etc. In other cases I do need all thousand heart rate readings right takes a clinician to understand that distinction. So that's one one component. Two, in healthcare, we have a really fragmented platform ecosystem. And this is particularly challenging when you think about trying to leverage solutions like AI to improve things every and when you and as clinicians, you know, we might not be intimately invol like have a deep understanding of what our technical ecosystem, infrastructure, architecture of our organization looks like, but it is worth thinking about and it is worth understanding. Every time you have an integration point, that is an area where you have a different data model that's going to need to be translated. And every time you have an integration point, it's one more spot where AI is going to become less efficient. And so where as folks think about how can I leverage data to drive the insights that I want to make my case to understand where I can innovate understanding what data is valuable what data do you need that you do not have what data do you have that you don't need that's an important distinction then also really thinking through the at the end of the day are those like unique solutions these point solutions that they we leverage are they really worth it to And so where I sit, you know, I I thankfully I get to work for Oracle, which has a huge tech platform and our best innovations are coming from where we can combine EHR, EHM, and HCM and finance data to drive new insights where I can actually at the end of the day are are am going to be able to say things like, hey, you're investing this many dollars on these processes and this is the out impact it's having on your patient outcomes and ultimately your bottom dollar. Do you want to clean up some of those processes to make things easier? Um, and so that that's that is what I would say associated with why is the data so fragmented. A lot of stakeholders, folks that don't understand the data intimately, they don't understand how to use all of the different data. Um, and at the end of the day, what you really need is someone who understands all of the different all of the different ways that that data is being generated so that you can articulate a vision of how we can clean that up. Well, and CJ, I also loved how you just emphasized the importance of a clinician doing that role because not only are they, you know, best maybe best equipped to step into that role, but also they they have that patient centered perspective because they're they've provided care to those patients and and that's really the goal of of health care and healthcare organizations is to improve patient outcomes and and provide health care to those patients. And I I think it's I think those are really interesting things you bring up particularly I mean just thinking about innovation over the last couple years with with artificial intelligence platforms be becoming more prominent and a lot of individuals viewing that as kind of the oh this is going to fix health care and it's going to solve so many things but fundamentally if you're dealing with broken data and siloed data and fragmented data the that that is the same data that AI is going to be dealing with as well. And so if if anything, it it it sounds like it is just going to mask over problems and and be potentially maybe help a little bit, but but if but not see the true success that that we're hoping and and kind of counting on it being. And so I guess my next question is is you you've identified like why it's fragmented. How do we make it less fragmented? How what what can we do? Is is there anything that can be done or is is this just the way healthcare is? Um, oh, I refuse to believe that this is just the way that healthc care is. I'm an idealist at heart and I believe that we're in change. Um, so I've been really great. I I'm really I have friends who say brilliant things. Um, and you guys have probably all heard this, but I think the this the first step to addressing our fragmented data ecosystem and therefore fragmented health care system is to begin with the end in mind. So, yep, the data is super messy. The platforms are messy. The way we deliver health care is messy. And a lot of that is because we just keep layering processes upon processes. Something breaks over here, so we put something in place over here. We don't understand how it has an impact somewhere else, right? So um I challenge all of you like as you think about whatever is like your passion project that thing that you want to fix whatever it is to not stop at like that first like if we just do this then we'll fix it but to really think deeply about what is the fundamental thing that we need to understand why this exists. So an example for me I'll go back to that initial initial assessment right we do not understand at scale the granular work that people do right so when we talk about inefficiency what is that but compounded tiny moments of inefficiency in healthcare right I'm not stocked on my supplies the you know like my you know what whatever like stupid thing it is like I can't the bladder scanner's on a different unit whatever it is like people like we do not understand at a really really low level how people work and the cost and value of that work. Now don't judge me. I read Elon Musk's biography by Walter Isacson and um whatever you think about Elon Musk like his approach to driving efficiency in an organization is interesting and one of the and like I I'll be honest I read it like three years ago before all of the different things, right? So take all this with a grain of salt. But in that book, it talks about like his approaches to understand the cost of every single nut and bolt that goes into his cars, right? To understand really deeply like the entire supply chain associated with like a single whatever like thing that goes in the car for everything. And in healthcare, we have no way to do that. We have no way to understand at a like really deep level aside from chasing people around with timers and all this stuff the work that we do. And so when you are thinking about how do I fix this broken data ecosystem, it begins with why like that fundamental really deep level of why does the problem exist? Can I measure why the problem exists? And the first step in me is to get to an effective way to measure why the problem exists. And once you're able to measure that, then all of the other solutions just cascade from that. It's not easy. It's a lifetimes lifetime of work. But until you have an accurate assessment of why does this exist? What is actually happening? Nothing else that the solution will continue to be convoluted and cause challenges in other areas. Right? And I stand by that. I think I stand by that. I'm sure we'll get some comments around, nope, you're wrong and this is why. But I stand by that um for really anything, but particularly in in any of the challenges we we face in healthcare. Well, and CJ, I think one of the most fascinating things about that is is uh often when we when we talk about problems of inefficiency or just issues that healthcare organizations face, they're they're looking at at largecale issues like how is how is this affecting the whole unit? How is this affecting the floor? How is this affecting the the hospital? H how is this, you know, affecting the the the group of nurses? All of that. When when in in reality, I I think one of the most effective things that you just said is is looking at the the simple little inefficiencies that build up. And I and I think that that's something that that leaders throughout health care and and nurses and clinical staff can can do and look at and see like, hey, where what are the little parts about my day-to-day life that I I have to do every single day that end up taking time away from seeing patients, taking care of patients, doing doing things that that are supportive and and need to be done. Stocking your rooms. Those rooms have to be stocked because you never know when you're going to need a particular supply. But that inefficiency of having to take that extra time to do it builds and and I think that that is such a key actionable thing that that leaders, you know, whether that's managers, directors across across the country can can do is is by looking at their units, looking at their programs and and seeing where those little inefficiencies are and and asking those nurses. I think that's such a critical thing, too. Like your your background coming from being one of those nurses, it's like, man, this is really inefficient. This is really there's a lot of hoops. There's a lot of buttons to click. I I I think that that's such an actionable helpful thing to keep in mind is that that change change to make it big, it's got to start small. And and you gota or else that that big change may happen, but it's not sustainable. And so I I think that that's fascinating and loved loved the way you did that. And so um TJ, I before I let you go, just one one last question before I let you go. Thank you for your time today. Um, but but for I'm I'm just kind of a general broad question, but but for nurses that that maybe were in your shoes when you started out that career that that are experiencing those same h having to click all those buttons that are experiencing that frustration and and lack of innovation in ways um people that that recognize issues within their workplace um and and want to make a change but maybe don't know how. What advice would you give to them on on how to start the process of making improvements where they're at? Because they're they're the experts on on what what needs to improve. Maybe not necessarily how to improve it, but but what needs to improve. So, what what advice would you give to those people? Yeah. Um the first thing is, and I'll kind of expand this to any clinician out there, we have a unique skill set that many people do not have. Right? your ability to communicate effectively over a short amount of time. The most important and pertinent information, whether it's, you know, doing report or handoff or whatever it is, your ability to walk into a room and know how someone is feeling both emotionally and physically and respond to that immediately. That's a unique skill that is translatable over um into any into any setting. Your ability to assess a situation using limited amount of information and make a decision to act that's going to have an impact. That is a unique skill set. Your ability to get stuff done in a convoluted and complicated system in which everyone is exhausted and lives are at stake and do it over and over and over and over again. That is unique. That skill set is the powerhouse behind the change that you want to make. So, first I would just say don't get tired. Like you are tired. Let me rephrase. Don't lose the don't lose sight of the fact that you are uniquely skilled and gifted to drive the change that you want. too. All of the things that I've said about understanding the business side of the house, that is that is an addition to your superpower if you're able to translate what needs to happen and frame it in terms of business so that people who have never touched a clinician who have no idea what your life is like, but that they can understand in three sentences the value of what you're articulating. I would invest in skills that drive that. Three, technology is going to change the way that we deliver care. We need to be the ones that are directing and driving and defining how care is delivered, both in terms of the clinical setting, what it's doing for our patients outside of, you know, the four walls of the hospital down to how it's assessing how we're working and where our leaders are going to invest from a strategic perspective. And so it's important to educate yourself on the different tech available. What are the data structures? What's the difference between AI and agentic AI and machine learning? What are the implications of those things? Where should we be investing first? How should we be thinking about how our care is quantified? You know, should we be pursuing a staffing ratio model? Should be we we be pursuing a workload model? Like have an opinion on those things. Watch them carefully. think about those things and and then being able to balance who you are as a clinician, who you are as an innovator, and who you are as a business leader. Bringing all of those things together, that's how you change the world. Well, CJ, thank you so much for joining us today on the Clinicians and Leadership Podcast. We're we're grateful for the work that you're doing and the impact you're making, not just on on healthcare organizations, but but but in patients lives through your work and and your innovation and your and your leadership. And so, um, I it's just encouraging to hear your heart and passion on on this topic and just to, you know, be reminded that that change starts small and and that there are things that we can do that will make a positive impact and will make a change and that is something that is just very encouraging um that that that sometimes we can get discouraged when we we are facing these things. So, thank you for sharing with us today your your insights and experience and uh we wish you the best going forward. Thank you. Thank you so much, Zach. Always a delight to talk with you, my friend.