AI isn’t a buzzword—it’s driving measurable results in healthcare. Part one of this 3-part series explores real-world success stories and lessons learned, including:
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Editor’s note: AI is no longer a distant promise in healthcare—it’s reshaping how systems deliver care, understand data, and run operations. But with so much hype, it’s hard to see what truly works.
This 3-part series highlights real use cases and lessons from AI in healthcare, with one goal: to help leaders find traction today and move toward better patient care, sustainable systems, and stronger margins.
Across the healthcare landscape, organizations are moving beyond pilot projects and applying AI and predictive analytics to solve specific, high-stakes problems.
From pediatric asthma to nurse scheduling, we’ve observed a noticeable shift involving AI in healthcare. Yet, one data point stands out: despite pouring $30-40 billion into AI, only 20% of AI tools reach pilot stage. And most of these projects fail. The leading reason isn’t the technology itself—it’s the lack of a clear, enterprise-wide strategy to guide adoption.
Despite this failure to launch, our recent conversations with healthcare leaders and data scientists reveal a clear pattern: healthcare leaders are leaning on the most effective AI models with frontline leaders in mind.
Their use of AI includes making predictions, spotting patterns, automating routine work, and supporting complex decision-making.
In our work with clients, AI in healthcare is transforming both clinical practice and community health. It helps to streamline workflows, support more accurate diagnoses, and enhance patient engagement.
Below are three powerful use cases that reflect this trend. Each one is grounded in the realities of sustaining clinical work and community health.
At a Texas regional health system, Parkland Health, a predictive model helped reduce asthma-related emergency room (ER) visits by over a third.
Leaders saw a troubling pattern emerging among kids from low-income and minority families. They were repeatedly showing up in ERs with uncontrolled asthma. Instead of waiting for these visits to happen, healthcare leaders asked how they could predict who’s at greater risk and intervene earlier.
They built a predictive model leveraging clinical and social data. This data was used to flag children who were likely to visit the ER within 90 days due to asthma. But the breakthrough for this health system came after the prediction: a coordinated response that included bilingual text messaging, community health worker visits, and environmental assessments to help families reduce triggers like mold and smoke.
The result? A 36 percent drop in asthma-related ER visits.
Flu vaccination rates also went up. Plus, more kids had up-to-date asthma action plans. Families felt supported, with 92% of them recommending the program.
This wasn't just an AI success. It was a public health success powered by AI.
One Midwestern hospital system saved more than 5,000 nursing hours over 1.5 years by integrating length of stay (LOS) predictions directly into its EHR workflows.
UnityPoint Health built a real-time predictive model based on more than 120,000 inpatient encounters, using 225 clinical data elements. The model was deployed directly within their EHR, making predictions visible on daily rounding reports and patient lists—no extra clicks or dashboards needed.
The results speak volumes:
This wasn't about replacing clinicians; it was about giving them better information, faster.
A large multi-state health organization used AI-powered scheduling to improve staffing flexibility and reduce burnout among nurses.
As the workforce crisis worsens—with retirement rates climbing and contract nurse costs skyrocketing—this health system needed a better way to forecast staffing needs.
They built an AI-powered scheduling tool using encounter data and historical trends to predict staffing needs at the unit level. But the key to their success wasn't just the model; it was how they built it. Caregivers and operational leaders helped shape the tool from day one.
Without disrupting patient care or caregiver autonomy, the first rollout of the tool:
By combining prediction with transparency and flexibility, the health organization gave its staff a smarter way to plan and a system that adapts in real time.
While each initiative tackled a different problem, the core strategy was the same. Each scenario reflects best practices for using AI in healthcare—and built on key elements that are grounded in strong data foundations and deep healthcare expertise.
3 Keys to Successful AI Adoption in Healthcare:
For these health systems, AI isn't a buzzword; it's a practical tool that, when used wisely, can drive measurable outcomes. The opportunity now is to replicate these wins across more settings, more service lines, and more communities.
These examples reflect a broader trend. When AI is designed with patients and practitioners in mind, thoughtfully integrated into real workflows, and used purposefully—not simply layered on top—it has the power to transform care delivery and operational performance.
The next phase of progress in AI won't be defined by tech conferences or hype cycles. The future of AI in healthcare hinges on what happens on the ground: smarter data, better decisions, and outcomes that move the needle.
Ready to achieve measurable improvement with the power of AI? Reach out to explore how we can tailor solutions to address your unique use cases and drive better patient outcomes.
