Delivering 97% prescription verification accuracy for NA’s largest eyewear company with QDox

Retail
case study

Business Impacts

60%

reduction in manual efforts

77%

document-level extraction accuracy

97%

field-level data extraction accuracy

4000

prescriptions processed per month

Customer Key Facts

  • Country : Canada
  • Industry : Retail

Problem Context

The client is one of the largest online contact lens retailers in North America and the largest seller of prescription eyeglasses online worldwide. They receive ophthalmic prescriptions from their end customer as part of the prescription glasses and contact lens purchasing. These prescriptions are used to claim insurance by end customers using the client’s website/application. They must verify the submitted ophthalmic prescriptions, extract data and store it for compliance and claims. They, therefore, required a scalable solution to automate this process of extraction and verification of prescriptions with high accuracy.

Challenges

  • Handling a large set of documents in a batch
  • Entity extraction in the semi-structured documents

Technologies Used

Amazon Textract

Amazon Textract

Amazon SageMaker

Amazon SageMaker

AWS Lambda

AWS Lambda

Amazon Simple Queue Service (SQS)

Amazon Simple Queue Service (SQS)

AWS CodePipeline

AWS CodePipeline

Elastic Container Registry (ECR)

Elastic Container Registry (ECR)

AWS Identity and Access Management (IAM)

AWS Identity and Access Management (IAM)

AWS Secrets Manager

AWS Secrets Manager

AWS Systems Manager

AWS Systems Manager

Amazon S3

Amazon S3

Amazon Aurora RDS

Amazon Aurora RDS

AWS Codecommit

AWS Codecommit

Solution

Quantiphi deployed QDox, a cognitive document processing solution built on Amazon Textract and various AWS machine learning services to automate the verification of ophthalmic prescriptions submitted in varying formats, including printed and handwritten in English and French.

Results

  • Ensured scalability of the extract and endpoint concurrency for a large set of documents.
  • Utilized vision and textual feature embedding models to facilitate entity extraction tasks for semi-structured documents.

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