Leveraging Azure Machine Learning to improve billing accuracy and consistency

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โ€œ This is the beginning of how we will change the way we work with data โ€

GM - Pharmacy

Case Study

Industry

Health Care

Company Size

10,001+ employees

Problem Space

Predictive Analytics

Azure

Insights

Machine Learning

Billing

Technology Used

Python

AzureSQL

AzureVNet

AzureDataFactory

AzureMachineLearning

Business Context

The ability to provide accurate invoicing for patient stays in hospital is a significant challenge for the healthcare industry. Inaccurate billing often results in unpaid claims from medical aids or patient dissatisfaction and a poor patient experience.


Challenge

Improve the billing process through insights and predictive analytics
  • High number of touch points involved in every case that is billed resulted in slow billing, billing errors and write-offs from stale claims not actioned timeously.

Solution

Azure machine learning solution that validates a patientโ€™s bill in near real-time
  • A custom-built rules engine to enable business to prevent known billing issues before a patient is discharged or final billed.
  • PowerBI dashboard to support operationalisation and adoption of the solution.

Billing quality assurance solution

Improved billing efforts resulting in higher quality bills that are paid faster

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Reduced medical aid claim rejections and short payments

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Reduced number of touch points for the majority of cases and directed focus on complex cases

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Improved billing accuracy and efficiency resulting in reduced days sales outstanding