AI Product Development
Non-emergency medical transport (NEMT) companies often face significant inefficiencies in their manual dispatch processes. Dispatchers have to individually review trip cards, check addresses, and manually assign drivers based on availability, location, and pickup times. It is a time-consuming process that’s prone to human error.
This manual approach makes it difficult to identify trips along similar routes, leading to redundant driver assignments and extended dispatch times. The inefficiency increases driver work hours, reduces on-time performance, and limits revenue per driver hour, hindering NEMT providers’ ability to scale operations and meet client needs effectively.
Tobi sought to leverage AI eliminate these inefficiencies by incorporating AI into its NEMT platform. The idea was to automate trip assignments, optimize routes, and improve dispatch efficiency while maintaining compliance with NEMT requirements.
To build this new AI feature, Tobi turned to their long-time development partner: Taazaa.
Taazaa developed Run Suggestions, an assisted automated dispatch feature, to enhance Tobi’s dispatch process. The system automatically analyzed unassigned trips, grouping those with similar routes, pickup times, and destinations, and suggested optimal driver assignments based on availability, location, and shift schedules.
Dispatchers could review these suggestions, deselect any unsuitable matches, and apply the assignments, significantly reducing manual effort. For example, trips along the same route, such as multiple pickups in a single area, were combined for a single driver, streamlining logistics. The feature was built with a focus on scalability, with plans to incorporate will-call trips (unscheduled return trips) in future updates.
Deployed in a test environment, the system demonstrated the ability to assign trips in seconds, compared to the minutes required for manual dispatching, setting the stage for broader production use.
The Run Suggestions feature transformed Tobi’s dispatch operations, delivering measurable improvements in efficiency and performance. In a single-day study with a customer transitioning from manual to assisted automated dispatch, Tobi achieved a 32% increase in revenue per driver hour, reflecting optimized trip assignments and higher productivity.
Driver work hours decreased by 15%, as the system minimized redundant routes and maximized route efficiency.
Additionally, 100% on-time performance was achieved, ensuring patients reached their appointments reliably, a critical factor for NEMT services. By automating route grouping and driver assignments, dispatch times dropped from an estimated 3-5 minutes per trip to under 10 seconds for more than 100 trips, allowing dispatchers to focus on exceptions rather than routine tasks.
The feature’s success in the UAT environment, coupled with its potential to integrate will-call scheduling, positions Tobi for further operational gains and scalability, earning trust from stakeholders and paving the way for enhanced client satisfaction.
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