Five real-world examples. Four questions to guide your next technology decision. Explore the roundup
Written by: American Hospital Association • Created: Sep 21, 2026
Teresa Arrington Director of Robust Process Improvement for Patient Safety and Quality Ochsner Health
Clinical process improvement, change management, patient safety and quality improvement
Using technology, data and change management to improve clinical outcomes and scale proven practices across the health system
Teresa Arrington brings together patient safety, clinical process improvement and change management at Ochsner Health.
Her work focuses on complex challenges where better processes can directly improve outcomes, including sepsis, mortality, and pressure injuries. Technology can be an important part of that work, but Arrington believes the starting point should always be focusing on the problem that needs to be solved.
That approach shaped Ochsner’s implementation of Epic’s sepsis predictive model, which helps care teams identify at-risk patients earlier and act appropriately. What began as a carefully selected pilot ultimately expanded across the health system as teams saw the impact and confidence in the approach grew.
For Arrington, successful adoption depends on more than a strong technology. It requires trusted relationships, meaningful clinician input, clear measures of success and the willingness to learn and adjust along the way.
I work in clinical process improvement and change management, with a focus on patient safety and quality.
A lot of our work is centered on improving outcomes in mortality, sepsis, and pressure injuries. These are complex problems that almost always involve multiple stakeholder groups. A big part of my role is bringing the right people together, understanding the problem and helping turn an improvement idea into something that can work in day-to-day clinical care.
Technology is often part of the solution, but it is never the starting point. We need to understand what we are trying to improve first.
One of the most important lessons is to make sure the technology solves a real problem and that everyone agrees on what success should look like.
With sepsis, we used Epic’s predictive model to identify patients at risk earlier and pair that information with actions care teams could take. But the technology alone was not enough. We had to think about the entire clinical process around it.
That included empowering nurses to act on their clinical suspicion, creating workflows that supported timely testing and treatment and making sure physicians, nurses, lab teams and others were all part of the work.
We started with a medium-sized hospital where we could meaningfully test the approach, learn and make adjustments. Once teams began seeing the results, the excitement built and other sites wanted to be next. Today, we use that model across Ochsner.
A lot of change management comes down to trust.
Clinicians need to know that when they raise a question or concern, someone is listening and will follow through. That can be as simple as responding when you say you will, being honest when you do not have an answer yet and communicating why a suggestion can or cannot be incorporated.
You also have to approach the work with humility. I am not a practicing clinician, so my role is to support the people who are doing that work every day and make sure their expertise shapes the solution.
With our sepsis work, we also built a way for providers to give real-time feedback on clinical alerts. We reviewed that feedback, determined whether changes were needed and communicated back to the clinicians. That kind of loop helps people see that their voice matters.
The first question is whether we have clearly defined the problem.
Sometimes something gets a lot of attention because of an individual experience, but the data may show that it is not actually a widespread issue. We have to understand the root problem before deciding where to invest time and resources.
From there, I think about the potential impact compared with the effort required. How many patients could this affect? How serious is the harm we are trying to prevent? What teams will need to be involved? Does the organization have the bandwidth and readiness to take it on?
You also need to consider the complete value story. Quality and safety outcomes are important, but so are the financial implications, workflow demands and investment required. The stronger that value story is, the easier it becomes to build support.
Measure what is happening and be willing to respond to what the data tells you.
When you introduce a change, you need enough runway to understand whether it is working. Then you come back to the data, share what you are seeing and make adjustments where they are needed.
That transparency is important for frontline teams and for leadership. If something is working, the data creates momentum. We saw that with sepsis. As sites began seeing the impact, people became excited about bringing the approach to their own teams.
It is also important to be upfront about tradeoffs. Not every initiative will deliver every benefit immediately. Leaders and frontline teams should understand what is being asked of them, what they can expect and what success should look like from the beginning.
One of the things that really struck me through the Accelerator is how universal many of the challenges are across healthcare.
We spend a lot of time reinventing the wheel. If organizations are willing to talk openly about what they have tried, what worked and where they struggled, we can make improvement faster and a lot less painful.
I already look to other organizations for ideas and lessons when we are tackling a new challenge. The opportunity with the Champion Network is to make that kind of exchange even easier and more intentional.
If we can learn from each other instead of starting from scratch every time, we can do an immense amount of good for patients across the country.
Start with a clearly defined problem and agree on what success should look like before choosing the technology. Use data to confirm the need, understand the potential impact and determine whether the opportunity is worth the effort required.
Build trust into the implementation. Engage clinicians early, create ways for them to provide real-time feedback and show how that input is being used. Adoption is stronger when frontline teams understand the goal, see that their concerns are taken seriously and know what the change is expected to accomplish.
Let the results guide what happens next. Give the change enough time to produce meaningful data, make adjustments when needed and share the evidence openly. When teams can see that an approach is improving care, that proof builds confidence and creates momentum for broader adoption.
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