At Boone Health, access to care is more than a scheduling metric — it’s a commitment to our patients and our community. Like many healthcare organizations, we’re always striving to overcome the operational and clinical challenges that come with missed appointments. No-shows can disrupt provider schedules, delay care, and create inefficiencies that ripple across the organization.
To address this, we implemented predictive no-show modeling by leveraging MEDITECH’s Revenue Cycle solution, and the results have been transformative.
Turning insight into action with predictive modeling
As an early adopter of predictive no-show modeling, we embraced the opportunity to not only reduce missed appointments but also fundamentally evolve how we manage access, scheduling, and patient engagement. Our organization realized that leveraging innovative technology to improve these processes would strengthen operational resilience for years to come.
Since implementing predictive no-show functionality, we’ve embedded risk scores directly into our scheduling workflows. Each appointment receives a no-show probability score, allowing our teams to identify higher-risk patients in advance and intervene proactively.
The model is fully integrated into our day-to-day operations. In December 2025, our teams generated more than 96,000 appointment predictions — averaging roughly 3,100 registrations per day. Despite this high volume, the predictions are delivered in real time, supporting decision-making without slowing down registration or scheduling processes.
Most importantly, the model is achieving 93% accuracy in predicting no-show behavior — giving our providers and clinic managers confidence in the data and enabling smarter scheduling decisions.
Proactive outreach that reduces missed appointments
At Boone Health, our teams have adopted a straightforward but effective workflow to decrease no-show rates. For patients with a no-show risk score of 50% or greater, staff takes the following steps:
Even though automated reminders are already in place, this additional layer of targeted outreach has helped reduce missed appointments, lowering our no-show rate from about 7% to 3% since using the solution. What makes this approach powerful is its precision. Instead of broadly overbooking or calling every patient, we focus our efforts where the data shows the greatest likelihood of risk. That allows us to allocate staff time more strategically, while ensuring patients receive the care they need.
For example, through conversations with patients, we’ve identified transportation as one of the most common drivers of missed appointments. Many of our patients rely on family members, caregivers, or community resources to get to appointments. When those arrangements fall through, cancellations or no-shows often follow.
The no-show risk score gives our schedulers a prompt to ask more specific questions:
In pain management clinics, patients may miss visits due to transportation challenges or because their pain levels make travel difficult. Knowing that risk ahead of time allows us to explore alternatives, including virtual visits when clinically appropriate. In smaller clinics, staff members often know their patients personally and anticipate these barriers. The model is especially helpful in larger or growing practices, where familiarity may not yet exist. It ensures consistency across teams and supports new providers through expansion.
Building trust through transparency
Adopting any new AI-driven technology requires trust. Our providers want to know: How accurate is the model? Can we rely on it when making scheduling decisions? With access to Business and Clinical Analytics dashboards, we know the data is available to help compare predicted no-shows with actual outcomes over time.
As we explore these metrics more deeply, they will allow us to track historical trends in predicted vs. actual no-shows, adjust risk thresholds, and analyze results by appointment type or demographic detail. Having this level of visibility helps build confidence among providers and leadership as we continue to incorporate predictive modeling into our scheduling strategies and access planning.
Operational efficiency meets better patient outcomes
The impact of predictive no-show modeling goes beyond operational metrics. By identifying high-risk appointments earlier, we can reduce unused appointment slots, optimize provider schedules, increase access for other patients waiting for care, and minimize delays in treatment.
One of the most impactful use cases has been strategic double booking. In several of our clinics, especially smaller sites with limited exam rooms, the no-show risk score helps determine when it may be appropriate to double-book an appointment slot. When a patient’s no-show risk exceeds 70–75%, teams use that insight to evaluate whether another patient can safely be added to the schedule.
This has been particularly valuable in small clinics operating with one exam room, specialty settings such as pain management, and situations where providers routinely accommodate brief or procedural visits. Rather than overbooking blindly, our teams make data-informed decisions that allow us to fill otherwise unused time, maintain productivity, and expand access without increasing provider burden.
There’s also an important nuance we’ve discovered: sometimes a high-risk patient shows up because our team intervened. In those cases, the prediction did exactly what it was meant to do — it flagged risk early enough for us to take action. That’s not a failure of accuracy; it’s a sign of success, showing that predictive models don't replace human judgment, but enhance it.
Looking ahead
One of the most exciting aspects of this initiative is what comes next. We’ve recently expanded predictive no-show functionality to additional scheduling areas, including acute and imaging services. Each department may use the insights differently — whether as a cue for proactive outreach or as a tool to determine whether last-minute add-ons can be accommodated. This flexibility is key to ensuring our technology can adapt to the unique workflows of each clinic, not the other way around.
As we continue refining workflows and expanding dashboard use, we see predictive no-show modeling as a foundational capability in our revenue cycle strategy, which has evolved from billing and collections to actively shaping patient access strategy. Predictive intelligence isn’t just about forecasting behavior — it’s about creating opportunities to intervene earlier, serve patients better, and ensure every appointment slot represents real access to care.
For Boone Health, that’s a win for our providers, our staff, and most importantly, our community.
Learn more about MEDITECH’s industry-leading Revenue Cycle solution.