Reduce no-shows with healow AI-Powered No-Show Prediction Model

Discover how HealthWorks for Northern Virginia uses eClinicalWorks® and healow® AI-powered No-Show Prediction Model to reduce no-shows. In this eClinicalWorks 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.