Part 1: Predictive analytics can help improve NEMT, on-time rides

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If NEMT providers can execute only one user-facing technology, what should it be?

One technology NEMT providers can implement is developing and rolling out a rider app. Many people use apps to order from restaurants and grocery stores, so familiarity with them continues to grow. In addition, apps, in general, have become a significant part of our everyday lives as more and more people use smartphones.

An NEMT app supports the improvement of the rider experience, which is crucial to NEMT success and happy customers. Riders get more visibility into and control of the service they may use several times a week.

For decades, NEMT providers have relied on paper as part of the operational aspect of the business. An app-based, paperless system can help provide a safer and more timely experience for riders.

How can NEMT organizations better use the data they have to improve ride quality?

NEMT providers have a lot of data that can be used to improve performance. We can use predictive analytics to help improve performance by reacting to business challenges in the same way other digital businesses react to theirs via predictive models and insights.

Predictive analytics can help NEMT organizations by:

  • Helping transportation providers deliver more rides
  • Increasing the safety and quality of the transportation
  • Matching certain drivers and patients on a routine basis where there has been a proven record of success
  • Identifying transportation network congestion to allow those areas to improve on-time performance

How can predictive analytics play a role in non-emergency medical transportation?

NEMT providers have a considerable amount of data that can help optimize how transportation providers are utilized. By anticipating things like rider no-shows and complaints ahead of time, we can provide the network with the signal it needs to maximize productive utilization of transportation resources to provide for the best possible cost and quality of service.

There are a few practical ways NEMT providers can use predictive analytics:

  • Predictive models to forecast rider no-shows during reservation intake and understanding and mitigating the reasons that contribute to no-shows
  • Real-time identification of late pickups using a live data stream and actions needed to recover those rides
  • Identification of fraud, waste and abuse activities by looking for pattern anomalies


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