customer stories

SafeRide Health automates background verification for vendors saving 52% of their cost using Nanonets

Up to 500%

Increase in Team Efficiency

~52%

Cost Reduction

5 Minutes to 1 Minute

Reduction in Processing Time

80%

Files processed with no intervention
Industry
Healthcare
Document types
16 different types, including, Vehicle Registrations, Insurance documents, Drivers Licenses, SSN Certificates, etc.
Location
Texas, United States of America
Integrations
ShareFile and Salesforce

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Client:

SafeRide Health

  1. Introduction: SafeRide Health provides non-emergency medical transportation to Medicare and Medicaid health plan members and care provider patients for vulnerable populations across the United States.
  2. Headquarters: Texas, United States of America
  3. Founded: 2016
  4. Founders: Robbins Schrader, Ben Salter, and Whit Schrader
  5. Industry: Healthcare
  6. Employee Count:~250-501
go to workfLow

Nanonets has significantly automated our driver and vehicle credentialing processes, allowing us to reduce manual workload by 80% and increase team efficiency by up to 500%. Their professionalism and responsiveness have been exemplary, making them a pleasure to work with. Nanonets' solutions have not only streamlined our operations but also enabled us to focus more on strategic initiatives.

Evan Rader
Network Manager, SafeRide Health

The Challenge

Summary: Scaling the process of background verification of the network of vendors, drivers and vehicles and maintaining a database of relevant data from over 16 different types of documents in Salesforce to aid audits.

Description: To be able to provide non-emergency medical transportation catering to an array of vulnerable population sectors, SafeRide Health partners with a network of transportation vendors , but not before carefully vetting their backgrounds. They had a manual process where they took up to 16 different types of documents from each driver, including their vehicle registration and insurance documents, as well as their first aid/CPR training certificates, wheelchair securement training certificates, etc. Once processed, they manually entered relevant data points for each driver/vehicle based on their vendors in their Salesforce account.With the increase in demand, this process was proving to be cumbersome and not scalable. This prompted them to seek an OCR vendor that could automate this process reliably and in a cost-efficient manner, allowing them to scale their vendor network management process.

The Solution

With our generative-AI powered OCR models, we were able to simplify and automate this process from end-to-end. It came down to the following 5 critical steps:

  1. Import of documents: All vendors securely send their documents through ShareFile, which are automatically processed by Nanonets.
  2. Classification of documents: Nanonets’ classification model intelligently identifies each of the 16 possible types of documents shared and directs it to the relevant OCR model for the data to be extracted.
  3. Data extraction: Nanonets’ trained custom OCR models identify specified data points from different types of documents and extract them with high accuracy.
  4. Validation of files and flagging discrepancies: The files then pass through validation checks put in place to weed out discrepancies, based on logics defined by SafeRide Health. In case of an error, the file is flagged and a member of the SafeRide team is notified and the files with no errors are approved automatically. 80% of the total files fall in the latter category.
  5. Export to Salesforce: The files with errors are redirected to a dedicated “pending” folder in Salesforce, to be manually vetted by the SafeRide team, whereas the files with no errors are mapped to their respective Salesforce folder.

I highly recommend Nanonets for their innovative approach and exceptional service.

Network Manager, SafeRide Health
Evan Rader

The result

Before
5 Minutes per document
After
1 Minute per document
Cost Reduction = ~52%

The Result

The result? With up to 16 different documents per individual onboarded and a total volume of 15,000 pages per month, processing each page took roughly about ~5 minutes. This was a significant drain on resources for SafeRide health, as they sought to scale their operations. After Nanonets automated the entire process for them, there were significant savings, allowing them to focus on planning their expansions.

  1. The average processing time went down from 5 minutes per document to 1 minute per document, only in the documents that had a discrepancy.
  2. Nanonets only flagged the files which had a discrepancy, which enabled SafeRide Health to review files with errors only. This was only about ~20% of the files. The number of documents to be reviewed dropped from 15,000 to only about ~3000.
  3. They had 2 FTEs dedicated to manual review of files. The total cost of maintaining these FTEs was about ~61,000 USD. After Nanonets, they ended up saving ~52% of the entire cost of operation.

SafeRide Health has a customer satisfaction score of 9/10 and hopes to automate other operations in the future, with Nanonets as well, as they move to expand their market.