Case Studies
A multidisciplinary pediatric rehabilitation center worked with Opmed to address a scheduling problem that had become increasingly difficult to manage manually. The resulting analysis showed approximately 29% higher provider utilization and 10% more billable hours.
Talk to our team
improvement in provider utilization
Increase in billable hours
Following PTO
Every day, teams have to coordinate patient treatment plans, therapist skills and availability, insurance requirements, coverage rules, room capacity, and organizational targets. Each factor is manageable on its own.
At Eyas Landing, a multidisciplinary pediatric rehabilitation center in Chicago, growth had made that scheduling puzzle increasingly difficult to manage manually.
Scheduling required approximately four to five hours of staff time each day. Provider utilization was uneven. Defined scheduling logic was not always applied consistently. Therapist PTO created additional reassignment work.
Eyas Landing worked with Opmed to explore a different approach.
The Challenge
Traditional scheduling depends heavily on people making individual decisions across dozens of operational constraints.
A scheduler may need to determine
Which therapists are available
Which providers have the right skills
What each patient’s treatment plan requires
How insurance rules affect scheduling
When patients can attend
How PTO and coverage changes affect the rest of the day
The Approach
Opmed analyzed Eyas Landing data and simulated scheduling scenarios incorporating therapist and patient availability, coverage hierarchy, insurance requirements, and other operational constraints.
The optimization algorithm then generated schedules designed to increase billable hours while maintaining continuity of care.
The goal was not simply to automate the existing process. It was to identify a better allocation of the resources already available.
The analysis and pilot validation revealed meaningful operational opportunity.
The optimized schedules made more effective use of available provider time, with the poster’s provider-level comparison showing improvement across the analyzed group.
Better allocation of provider capacity translated into more billable clinical time within the modeled schedules.
Automated coverage reduced some of the manual work associated with rebuilding schedules when therapist availability changed.
For Eyas Landing, the value of AI scheduling was not simply producing another calendar.
It was creating a way to evaluate operational complexity that would be extremely difficult to manage manually at scale.
Dr. Laura Mraz
Eyas Landing founder
Unused provider time limits capacity. Repeated schedule changes create additional work. And when the organization cannot use its existing resources efficiently, growth becomes more difficult.
Optimization gives teams another option.
Sometimes the first opportunity is understanding whether the resources already available are being allocated as effectively as they could be.
Eyas Landing’s experience illustrates that opportunity.
By using AI to evaluate patient needs, provider availability, operational rules, and resource constraints together, Opmed helped reveal capacity that was difficult to identify through manual scheduling alone.
This reflects Opmed’s broader approach to healthcare operations: forecast what is likely to be needed, allocate resources around those needs, and adjust as conditions change. That framework is central to Opmed’s current platform direction.