Athenahealth was bleeding opportunity in the one place it should have been leading — patient access.

Forty percent of all booked appointments were being disrupted. No-shows, cancellations, reschedules. It looked like a scheduling nuisance. It was actually a structural failure in how care was delivered.

Patients weren’t getting treated on time. Providers were sitting idle. Athena’s clients were losing seventy million dollars a year. Revenue, access, and outcomes — all getting crushed by broken behavior patterns the system didn’t recognize.

I didn’t wait to be assigned the problem. I found it. Buried inside millions of appointment records. The patterns were loud. Pediatrics saw late cancellations. Behavioral health had chronic reschedulers. Urban and rural locations behaved differently. New patients were more erratic. Existing ones had habits. None of this was captured.

Athena’s scheduling system had no memory. No differentiation. Everyone was offered time slots like it was a sandwich menu. No feedback loop. No behavioral prediction. No adaptation.

Internally, it was worse. Call centers were overloaded. Staff were following up manually. Agents had no context. Rebooking was reactive and inefficient. The patient experience felt more like airline rebooking than healthcare.

This wasn’t a UX issue. It was an optimization problem disguised as admin work.

I made the case to leadership. This wasn’t just inefficiency. It was a greenfield opportunity. If we could predict which appointments would fall through, we could build a system that filled gaps before they opened.

I built and led an Intelligence Team to take it on.

We started with behavior mining. I used association rule mining to identify the highest-risk segments for NCR: no-shows, cancellations, reschedules. Then layered in demographic patterns, historical slot usage, clinic location, and appointment type.

We built models that assigned a disruption risk score to every appointment.

Then we acted on it.

We launched a dynamic overbooking tool. Not shotgun-style double-booking, but precision targeting. High-risk patients got paired with flexible overbook windows. Clinics could fill open slots without creating chaos.

We built a waitlist engine. When someone canceled, the system automatically notified others who preferred that time. Slots got filled in real time.

We launched a self-scheduling tool. Patients could pick times based on their preferences. No more back-and-forth. No more call queues.

The platform reduced appointment disruptions by 40 percent. Providers gained $5 million in recovered revenue. More importantly, they saw fewer gaps. Patients were seen faster. Clinics ran smoother.

Athenahealth’s market share in EHR jumped from 2 percent to 3.2 percent. It moved from seventh to fifth nationally. A scheduling engine fixed what marketing could not.

We didn’t just make scheduling smarter. We made the system care about time the way people live it.