Published on Monday, December 22, 2025
Discover how HealthWorks for Northern Virginia uses eClinicalWorks® and healow® AI-powered No-Show Prediction Model to reduce no-shows. In this eCW Podcast, host Adam Siladi talks with IT Director Jesse Burke about moving from reactive, blanket outreach to proactive, targeted support that removes real barriers for patients.
As a community health center, HealthWorks for Northern Virginia faces a no-show rate around 15% and serves a population that is roughly 60% self-pay. Transportation in the area is limited, childcare is costly, and missing work can mean missing income. Traditional after-the-fact reports and mass campaigns didn’t help fill the schedule or meet patients’ needs.
With the healow AI-powered No-Show Prediction Model, risk scores appear right on the resource schedule while staff are booking. Teams can see which visits are likely to cancel, reschedule, or no-show, ask better questions, tag barriers like transportation and daycare, and tailor their outreach. The analytics and reports quantify trends across the population, revealing patterns such as transportation driving more than half of missed visits. That structured data helped HealthWorks for Northern Virginia secure grants for non-emergency medical transportation, set up a transportation account to bridge gaps, and build partnerships with childcare providers.
What this really means is more patients get seen and fewer slots go unused. Staff focuses its efforts where it matters, use alternate contact methods when phones are off, confirm high-risk visits with extra touchpoints, and connect patients to the services they need to make it to the appointment. The practice gained a clear path to show funders both the size of the problem and how funds would be targeted, strengthening applications and outcomes.
If you want practical ways to use predictive analytics in eClinicalWorks to reduce cancelations, reschedules, and no-shows, and to improve access and make every appointment count, this conversation delivers the playbook.
“Now we can show real data, like 53 percent of missed visits are due to transportation, and we used that to win grants. Risk scores appear right on the schedule while we are booking, so staff can spot likely cancelations in time to help.”
– Jesse Burke, IT Manager, HealthWorks for Northern Virginia
Topics From This Episode
Due to missed appointments, last-minute cancelations, and reschedules, you could be missing out on as much as $50K a year. healow AI-Powered No-Show Prediction Model uses AI machine learning to predict your no-shows with up to 90% accuracy, allowing you to conduct outreach to improve your show rate.
