Case Studies

Operational results from real NEMT implementation patterns.

These anonymized case studies show how facilities and fleet teams reduced scheduling burden, increased ride reliability, and improved visibility after moving from phone-first coordination to RideVoy workflows.

Model Your Outcomes Review Pricing Guide

Impact snapshot across common deployments

The ranges below are based on recurring implementation patterns from organizations with similar operating profiles. Final results vary by workflow maturity, staffing, and patient mix.

Metric	Pre-implementation baseline	First 90 days on RideVoy
Fulfillment rate	78-86%	91-97%
Manual scheduling time	4.5-8.0 min per ride	1.5-3.0 min per ride
Coordinator status calls	High daily call volume	30-60% reduction
No-show rate (transport-related)	10-18%	5-11%

Case study details

Multi-site dialysis network

A regional operator managing recurring treatment transportation across six facilities was manually re-booking hundreds of repeat rides every week.

Implemented recurring ride templates by patient cohort
Standardized rider profile requirements for accommodations
Introduced proactive SMS notifications for pickup readiness

90-day outcome: scheduling time dropped by 62%, fulfillment rate improved from 83% to 95%, and missed treatment transport incidents decreased by 29%.

View dialysis solution ->

Regional outpatient clinic group

A clinic system struggled with post-discharge and follow-up ride reliability across multiple specialty departments.

Created shared transport workflows for discharge and return visits
Rolled out live ETA visibility through driver tracking
Added weekly ops reporting by facility and shift

90-day outcome: discharge transport wait times dropped by 34%, no-show rate for transport-reliant follow-ups improved by 22%, and coordinator escalation volume declined by 37%.

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Independent NEMT fleet operator

A 28-driver fleet relied on phone dispatch with limited visibility into acceptance speed, idle time, and exception trends.

Adopted centralized ride board workflows
Introduced KPI tracking by driver and route cluster
Added SLA thresholds for at-risk ride intervention

90-day outcome: average assignment latency dropped by 41%, rides per active driver increased by 14%, and same-day exceptions resolved within SLA rose from 68% to 93%.

View fleet solution ->

Implementation sequence that produced the strongest gains

Baseline mapping

Capture current fulfillment rate, no-show drivers, and manual coordination load.

Workflow configuration

Set recurring schedules, rider requirements, and exception routing by team role.

Operations launch

Transition active rides in phased cohorts, with coordinator and dispatcher onboarding.

30/60/90 optimization

Track trends weekly, tune exceptions, and standardize performance review cadence.

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We can estimate projected operational impact using your current scheduling profile, staffing model, and monthly ride demand.

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