Source: Unite.AI
Ali Morin, Chief Nursing Informatics Officer at Symplr, is a nursing and healthcare technology leader with experience spanning bedside care, clinical systems, and nursing informatics. Before becoming Chief Nursing Informatics Officer in 2024, she served as symplr’s Vice President of Nursing Informatics and held senior informatics roles at Halo focused on clinical communication, implementation, and adoption. Earlier, Morin worked in clinical informatics and analytics at Cincinnati Children’s Hospital Medical Center and led major electronic clinical documentation and computerized provider order entry initiatives at Boston Children’s Hospital. Her background as a registered nurse gives her a clinical perspective on using technology and workflow design to reduce administrative burden and improve care delivery.
symplr is an enterprise healthcare operations software company focused on connecting and simplifying the administrative and operational systems that support healthcare delivery. Its cloud-based Operations Platform brings together capabilities spanning workforce management, provider data and credentialing, quality and safety, clinical communication and physician scheduling, talent management, spend management, compliance, access, and contract management, with the goal of replacing fragmented point solutions with more integrated workflows and data. The company is also incorporating artificial intelligence across its platform to support automation and decision-making while keeping healthcare-specific data governance at the center of its approach. symplr says its technology is used by nine out of ten U.S. hospitals and more than 400 U.S. health plans.
You’ve spent nearly three decades as a bedside nurse, clinical informatics leader, and now Chief Nursing Informatics Officer at symplr. Looking back across that journey, what are the biggest mistakes you’ve consistently seen technology companies make when designing AI for nurses, and how has your own frontline experience shaped the way you approach AI development today?
Healthcare has been here before. Electronic health records promised efficiency, but early on, they often created more administrative burdens because the frontline clinicians expected to use them daily weren’t involved in the design or implementation of these tools. I experienced this first hand with the first EMR I used at the bedside and again when workflow decisions were made in the board room with little nod in design sessions to the clinicians that would be clicking away when it went live. AI gives us a chance to get this right, but only if we don’t repeat that mistake.
I started my career more than 25 years ago as a pediatric critical care nurse, and I’ve spent the last two decades in nursing informatics. I’ve seen both sides: how technology helps when it’s built with clinicians’ perspectives and consultations, and how quickly it becomes a source of frustration when it isn’t. The mistake I still see is healthcare organizations designing solutions for the workflow they think a nurse has, not the actual environment, full of interruptions, competing priorities, and moments when documentation and patient care happen at the same time.
My frontline experience shapes a simple test I apply to every AI tool: Does it meaningfully support a nurse’s work, or does it become just another thing competing for their attention? If it’s the latter, it doesn’t matter how advanced the model is underneath; it’s just another competing priority that adds to a nurse’s workload and vies for their time.
You often argue that clinicians need to be involved throughout AI development, not just during deployment. What does meaningful clinician participation actually look like, and why do so many healthcare AI initiatives still get this wrong?
Nurse involvement in technology governance should be a structural expectation, built into how organizations evaluate and deploy technology from the start. That means making sure nurses are in the room every step of the way – from procurement to implementation to evaluation.
The first time a nurse sees a solution shouldn’t be after the decision is made and the staff is trained. Nurses who use these tools every day will identify challenges and opportunities that administrators, developers, and vendors may never see, simply because as clinicians, they live inside the workflow.
Most healthcare AI initiatives get this wrong because they treat clinical input as a checkpoint rather than a continuous process. Often, a nurse is asked for feedback during the demo or pilot, and that’s counted as “clinician involvement.” Real involvement looks more like the work I do at symplr, being in the room when we’re designing, developing and evolving our AI solutions. Clinician participation can also be seen in groups such as AONL’s Leadership, Innovation, Technology and Transformation Committee, or the HIMSS Vendor CNO/CNIO Working Group, where nurses are defining the competencies and governance healthcare organizations use to evaluate technology; they’re not just reacting to what’s already been built.
Nurse managers can spend a significant portion of their day creating and adjusting schedules. How is AI fundamentally changing workforce management, and where are you seeing the greatest measurable improvements in efficiency and staff satisfaction?
AI-driven scheduling and workload management tools remove a lot of the operational complexity that has long burdened nurse leaders. Tasks that once required endless manual coordination, such as building the schedule, adjusting it in real time, and covering last-minute call-offs, can increasingly be automated instead of consuming a nurse manager’s day.
On the efficiency side, we have data to show the impact. For example, with integrated scheduling in symplr’s existing workforce platform, the combined solution can now forecast staffing needs up to 120 days out with 96% accuracy. That’s a measurable shift, from reactive, day-to-day scrambling to an approach that allows nurse leaders and clinicians to plan.
It’s harder to link staff satisfaction to forecasted scheduling, but nurses who feel supported by the systems around them tend to stay in the profession longer. The National Council of State Boards of Nursing reported that of nurses who said they intended to leave the profession in the next five years, 41.5% cited stress and burnout as the cause, with workload, understaffing, and inadequate salary following as the other top reasons. When staff scheduling stops consuming a manager’s day, and coverage gaps are anticipated in advance, that’s the kind of positive change that can impact how people feel about their jobs.
Predictive staffing promises to anticipate shortages before they become crises. What data signals are proving most valuable, and how do you ensure these models support nurses rather than simply optimizing labor costs?
The signals that matter most are those that reveal a gap before the situation is urgent, like how far in advance shifts are getting filled, and how often a unit is leaning on last-minute incentives to cover a shift. Bellin Health is a good example. Once the provider’s clinical team had visibility into open shifts and coverage gaps, the team filled 65% of shifts two or more weeks out and 80% within a week, rather than scrambling at the last minute to close staffing gaps.
The way to keep the models honest is by measuring more than cost. Predictive scheduling saved Bellin Health more than a million dollars in labor costs. But what matters to its nurses is that 80% of shifts were filled early. A model built with nurse input goes beyond efficiency, safety and predictability to positively impact nurse satisfaction.
Healthcare has seen plenty of technology rollouts that increased administrative burden instead of reducing it. What distinguishes AI tools that nurses embrace from those that quickly become another source of frustration?
The tools nurses embrace are those that fade into the background. For example, documentation that writes itself while a nurse stays fully present with a patient, or workflows that eliminate repetitive tasks instead of adding new screens to check. The tools that become another source of frustration are usually implemented without being grounded in how nurses work, instead adding to the number of items that a nurse has to review, click, or double-check while caring for patients.
It comes down to transparency and fit. When leadership is transparent about what a tool does and why, and it’s built around real workflows rather than an idealized version, then the payoff is real – more time for patient care, more time for critical thinking, and more capacity to mentor newer nurses. But when it isn’t, technology becomes a hindrance in an already demanding job.
As AI development cycles continue to accelerate, are you concerned that healthcare organizations may prioritize speed over clinical validation? What safeguards should exist before AI systems are integrated into patient care environments?
Yes, the focus on speed rather than validation is a real concern. The pace of AI development is fast, and healthcare’s regulatory and safety requirements exist for good reason. The organizations that get this right treat clinical validation and nurse governance as part of the design process from day one, not something added before go-live to satisfy a checklist.
For me, the baseline safeguard is that nurses and other frontline clinicians are evaluating these tools before they touch patient care. Beyond that, health systems need transparency about what a system is doing, and a clear process for clinicians to flag when something isn’t working. National governance structures, like those being built by industry groups such as AONL and HIMSS, give organizations an actionable framework instead of leaving every health system to tackle this alone.
Beyond scheduling, where do you believe AI has the greatest untapped potential to reduce burnout among nurses while simultaneously improving patient outcomes over the next five years?
Documentation is where I still see the most room to grow. Ambient tools are only beginning to capture the in-between moments of care that have historically gone undocumented. There’s a lot more ground to cover over the next few years, both in giving nurses time back and in building a more complete record of what transpired during a patient encounter.
Virtual nursing is another area with real room to grow. Right now, it’s mostly used for patient education, admission documentation, discharge planning, and remote monitoring. The bigger opportunity is scaling that support, so more bedside nurses get time back to focus on the patient in front of them rather than the paperwork around them. It’s important to note, though, that ambient tools and virtual nursing can’t replace a clinician’s judgment. The technology simply removes tasks that are quietly competing with it.
Looking ahead, what advice would you give healthcare executives who want to build AI strategies that nurses will champion rather than resist, and what role should nursing leadership play in shaping the next generation of clinical AI?
My advice is to involve nurses in technology procurement, implementation, and evaluation from the beginning. Nurses who work with these tools every day will identify challenges and opportunities that others may overlook. Also, invest in digital leadership development now, not years from now. We can’t expect nurse leaders to guide complex technology decisions without giving them the tools or knowledge to do it well.
Nursing leadership’s role is to make sure that clinical involvement is intentional, not incidental. Through my work on the AONL committee, we’ve defined the digital competencies nurse leaders need: digital fluency, the ability to collaborate across stakeholders and to align technology decisions with real clinical workflows and outcomes. Programs like the Karlene Kerfoot Nursing Leadership in Technology Education Grant, which symplr created in partnership with The DAISY Foundation, exist to make sure more nurses have access to that kind of preparation.
When nurse leaders have a real seat at the table, technology aligns more closely with how care is delivered, adoption improves, the administrative burden eases, and patient safety and outcomes improve. That’s the whole point.
Thank you for the great interview, readers who wish to learn more should visit symplr.