LeanTaaS

Ingest 12 months of infusion appointment data.

Run constraint-based algorithms to identify 'sweet spots' for long and short appointments.

Generate optimized templates for the scheduling team.

Use real-time dashboard to monitor chair utilization.

Adjust future schedules based on actual patient arrival variances.

Analyze surgeon performance and block utilization trends.

Predict which blocks are unlikely to be used 14 days in advance.

Nudge surgeons to 'release' time through a mobile app.

Instantly notify waitlisted surgeons of the new availability.

Track 'recovered hours' and associated revenue growth.

Analyze real-time EHR data for 'discharge-ready' signals.

Predict probable discharge times for every patient on every floor.

Notify EVS teams to prioritize cleaning beds in high-demand units.

Alert transport teams to move patients to the discharge lounge.

Open ED throughput by 15% through earlier bed availability.

Aggregate data from all facilities into a centralized command center view.

Apply ML to predict bed shortages at specific hospitals within 24-48 hours.

Recommend patient transfers between facilities to balance the load.

Coordinate with leadership to open 'surge' units only when necessary.

Evaluate system-wide throughput efficiency via unified dashboards.

Forecast patient census 7 days in advance using predictive models.

Compare forecast to scheduled nursing shifts.

Identify shifts with potential safety risks due to understaffing.

Prompt managers to adjust float pools or call-in shifts earlier.

Reduce premium labor spend by optimizing current staff allocation.