MDaudit uses AI to improve revenue outcomes for healthcare customers

MDaudit provides a cloud-based billing compliance and revenue integrity software as a service (SaaS) platform to more than 70,000 healthcare providers and 1,500 healthcare facilities, ensuring healthcare customers maintain regulatory compliance and retain revenue. Working with the top 60+ US healthcare networks, MDaudit needs to be able to scale its artificial intelligence (AI) capabilities to improve end-user productivity to meet growing demand and adapt to the changing healthcare landscape. MDaudit recognized that in order to meet its healthcare customers’ unique business challenges, it would benefit from automating its external auditing workflow (EAW) using AI to reduce dependencies on legacy IT frameworks and reduce manual activities needed to manage external payer audits. The end goal was to empower its customers to quickly respond to a large volume of external audit requests and improve revenue outcomes with AI-driven automation. MDaudit also recognized the opportunity to evolve its existing architecture into a solution that could scale with the growing demand for its EAW module.

In this post, we discuss MDaudit’s solution to this challenge, the benefits for their customers, and the architecture involved.

Solution overview

MDaudit built an intelligent document processing (IDP) solution, SmartScan.ai. The solution automates the extraction and formatting of data elements from unstructured PDFs that are part of the Additional Documentation Requests (ADR) service for Payment Review that customers of MDaudit receive from commercial and federal payers across the country.

Designed with client-level isolation at the document level, MDaudit customers start by uploading their ADR documents via a web portal to Amazon Simple Storage Service (Amazon S3).

This prompts an AWS Lambda function to initiate Amazon Textract. Using Amazon Textract for optical character recognition (OCR) to convert text images into machine-readable text, MDaudits’s SmartScan.ai can process scanned PDFs without manual review. The solution also uses Amazon Comprehend, which uses natural language processing (NLP) to identify and extract key entities from the ADR documents, such as name, date of birth, and date of service. The OCR extract from Amazon Textract and the output from Amazon Comprehend are then compared against preexisting configurations of data objects stored in Amazon DynamoDB. If the format isn’t recognized, the solution conducts a generalized search to extract relevant data points from the PDFs uploaded by the customer. The new configuration is then sent to the human-in-the-loop using Amazon Augmented AI (Amazon A2I). After the configuration has been approved, it’s stored and made available for future scans, thus enhancing security. By using Amazon CloudWatch in the solution, MDaudit monitors metrics, events, and logs throughout the end-to-end solution.

Benefits

In the post pandemic era, the healthcare sector is still grappling with financial hardships characterized by thin margins as a result of staffing shortages, reduced patient volumes and the upsurge in inflation. Simultaneously, Payer’s post payment recovery audits have skyrocketed by more than 900% and aggravating the situation further, Revenue cycle management (RCM) workforce reductions by 50-70% have put them in a precarious position to defend against the overwhelming impact of these post payment audits. The external audit workflow offered by MDaudit streamlines the management and response to external audits through automated workflows, successfully safeguarding millions of dollars in revenue. With the integration of AI-driven capabilities, using AWS AI/ML services, their innovative solution SmartScan.ai introduces further time savings and enhanced data accuracy by automatically extracting pertinent patient information from lengthy audit letters, which can vary from tens to hundreds of pages. As a result, customers are now capable of managing a much higher volume of demand letters from Payers, increasing their productivity by an estimated tenfold. These advancements lead to improved efficiencies, significant cost savings, faster response to external audits and the retention of revenue in a timely manner.

The Initial adaptation statistics indicate that the average processing time for an ADR letter is approximately 40 seconds, with accuracy rates approaching 95%+. In the last 12 months of launching SmartScan.ai, MDaudit’s customers have successfully responded to $100M worth of payer audit requests and retained approximately 95%+ in revenue, including pre-payment audits.

Our approach to innovation centers on collaboration with our ecosystem partners, and AWS has proven to be a valuable strategic ally in our healthcare transformation mission.” says Nisheet Goenka, VP of Engineering at MDaudit. “Our close cooperation with AWS and our extended account team not only expedited the development process but also spared us four months of dedicated engineering efforts. This has resulted in the creation of a solution that provides us with meaningful data to support our Healthcare customers.”

Summary

This post discussed the unique business challenges faced by customers in the healthcare industry. We also reviewed how MDaudit is solving those challenges, the architecture MDaudit used, and how AI and machine learning played a part in their solution. To start exploring ML and AI today, refer to Machine Learning on AWS, and see where it can help you in your next solution. If you’re a small or medium business owner exploring AI usage, read an alternate version of this story with less technical language on the AWS Smart Business blog.

About the Authors

Jake Bernstein is a Solutions Architect at Amazon Web Services with a passion for modernization and serverless first architecture. And a focus on helping customers optimize their architecture and accelerate their cloud journey.

Guy Loewy is a Senior Solutions Architect At Amazon Web Services with a focus on serverless and event driven architecture.

Justin Leto is a Senior Solutions Architect At Amazon Web Services with a focus on Machine Learning and Analytics.