Case Study: Experian Health

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Experian Health Revenue cycle management Based in Franklin, Tennessee Serving over 13,000 healthcare organizations, including nearly 60% of all US hospitals.

Experian Health is a subsidiary of Experian plc, a company most known for its credit reporting services. The Experian Health subsidiary provides revenue cycle management, identity management, patient management, and care management solutions designed to help providers make better-informed decisions while limiting risk.

 

Founded in 1996, Passport Health Communications was purchased in 2013 by Experian plc. Experian plc had already moved into the healthcare payments space with earlier acquisitions, the Passport purchase helped fill out its offerings. The subsidiary was renamed Experian Health in 2016.

Experain Health processes the prequalification of medical benefits for health providers. For example, when a physician concludes that surgery is needed, the patient’s records are first sent to the insurance company to determine coverage. Experian Health provides a clearing house for those “prequal” transactions.

Experain Health's system was hitting its capacity every month when organizations would run their prequals to plan the coming month. The prequal results were needed in order to schedule medical procedures and associated resources such as doctors, nurses, operating rooms, etc.

During the monthly peak, the system would slow to a crawl and crash. Though its system was already over capacity, the company was adding clients at a rapid pace. Usage was going up! Experian Health’s CTO realized they were at a turning point and brought in The AI Team to help.

Experian Health's prequalification processing system was unable to handle monthly peaks, but usage as rapidly increasing. Something had to be done fast, hopefully without requiring a rip-and-replace of the existing system.

In 4 weeks AI delivered these benefits:

  • Eliminated service interruptions during monthly peak periods
  • Scalability for handling future increases in business
  • Solution leverages existing software and technology
  • Smooth transition with minimal training or post-deployment support

The Challenge

When a provider sends a prequal request to Experian Health, it is then routed to the most efficient processing service. There could be 15 services equipped to handle a particular request, but the request is routed to the one best-equipped to expedite processing of that request type.

Experian Health’s 316-server solution was Enterprise Service Busbased (NServiceBus), but the interface with payment processors was single-threaded, each transaction handled one at a time. The system used RavenDB to house the underlying “saga data” needed to track the state of each transaction as it was processed by the system.

Before AI was called in, Experian Health’s high water mark was six million transactions per day, and usage was increasing rapidly. The underlying payment processors were also taking transactions from services other than Experian’s, and some of them were unable to keep up.

For example, Medicaid/Medicare received the greatest number of transactions, but that system is antiquated and unable to handle the incoming requests from various services such as Experian’s. In fact, the Medicaid/Medicare payment processing system is only able to survive on the basis of voluntary throttling by the competing services using it. Without that cooperation, the system becomes unusable as HTTP-based transactions begin timing out for everyone.

Problems would arise due to the Experian system’s single-file handling of transactions with payment processors such as Medicaid/ Medicare. When such a service stalled, Experian’s system would grind to a halt. Just one processor with a problem could slow down Experian’s ability to process all types of prequal requests.

After its analysis, The AI Team proposed reducing the amount of meta-data being tracked by NServiceBus, the Enterprise Service Bus used by Experian Health’s processing system. A database upgrade was also recommended as was a switch to a more multithreaded solution for preventing any one payment processor from bottlenecking processing.

As a result, Experian Health now easily keeps up with monthly peaks in processing volume, and they have an architecture that will scale to handle much more than the original six million transactions/ day maximum. Despite the rearchitecting, Experian was able to retain most of their original solution. No extensive conversion or retraining was necessary.

The entire engagement took only four weeks.

Project Goals

The AI Team was brought in because of their expertise and experience with the NServiceBus ESB technology used by Experian Health’s existing system. The immediate goal was to fix the performance problems. The system had to reliably handle the monthly blast of prequal requests, expected to grow well beyond the previouslyexperienced six million transaction-per-day maximum. Scalability for the future was a must.

Ideally, the existing Experian Health solution could be adapted rather than replaced, allowing continued use of much of the existing code and technology stack. This would save time and money while minimizing the amount of migration effort and retraining needed.

The Requirements:

  • Keep the system running reliably during monthly peaks
  • Prevent any one payment processor from slowing the entire system
  • Increase future scalability
  • Reuse as much of the existing system as possible

The Solution

During their initial assessment, The AI Team looked at the rate that requests were going into each of the payment processors and took measurements from their queuing systems. From this it was easy to see which processors were working well and which were causing problems. It allowed the team to identify the 25 payment processors that were most likely to cause performance issues. Experian’s system was then re-architected so that rather than one queue feeding all payment processors, each of the top 25 processors now has their own queue. This prevents any one backlogged processor from bottling up the entire system.

AI also looked at the “saga data” being tracked for each transaction. As it turned out, there was much more data being tracked per transaction than necessary, greatly increasing the burden on the underlying database. AI’s recommended solution was to eliminate unnecessary saga data and upgrade the database used to track it. They switched from RavenDB to Microsoft SQL Server and NHibernate.

The database changes alone got Experian Health over the six million transactions-per-day hump. The queuing changes have the company well-prepared as continuing increases in business demand higher levels of scalability.

Solution Details

  • Decreased the amount of data tracked for each transaction
  • Migrated from RavenDB to Microsoft SQL Server and NHibernate
  • Re-architected NServiceBus configuration for maximum scalability, enabling requests to be fed to multiple payment processors in parallel

Results

Before the project, Experian Health’s prequalification processing system would become bogged down and crash during monthly peak processing, and new clients were being added at a rapid pace.

The AI Team worked together with Experian for four weeks to modify the system, which now easily handles peak monthly workloads. Plus, it is architected to scale well as the business continues to grow.

The new system uses much of the same technology stack as it did previously, including the NServiceBus ESB. A lot of the processing software was preserved as well. No massive migration and retraining was necessary. In fact, the entire engagement was over in four weeks. When AI left, Experian was well-equipped to maintain the system with their existing personnel and has not made a "911" call to AI for support since the project’s end.

The Payoffs

  • Rapid results
  • No more service interruptions during monthly peak periods
  • Scalability for handling future increases in business
  • Solution leverages existing software and technology
  • Smooth transition with minimal training or post-deployment support