How WellRithms achieved 30 times faster bill processing with AWS

WellRithms achieved 30 times faster bill processing by combining AWS services with its deep medical billing expertise. This blog post explores how the company transformed document preparation from a manual constraint into a scalable, AI-powered capability, and the results they achieved.

By continuously investing in AI, WellRithms transitioned from a manual, reviewer-dependent workflow to a scalable bill intelligence solution. With the resulting gains in operational capacity and shorter delivery timelines, the business can onboard new clients without creating equivalent operational constraints.

Leadership quote

“This innovation collaboration represents the next evolution of healthcare payment intelligence. Working alongside AWS, we’re combining intelligent document processing, AI, and our proprietary healthcare expertise to automate one of the industry’s most manual processes. The result is higher-quality data, faster workflows, and a solution that continuously learns and improves as it scales.”

Kelvin Yip, Chief Revenue Officer, WellRithms

The industry problem

Medical billing in the United States is inherently complex, with significant variation in how providers generate and present billing information. Hospitals, clinics, and specialty providers all produce itemized bills in their own way. Examples include different layouts, uneven scan quality, inconsistent line-item structures, and billing conventions that vary from one provider to the next.

This complexity creates more than administrative and operational burdens. The problem also contributes to the unsustainable rise in healthcare spending in the United States. Research estimates that three out of four medical bills contain some error. The Journal of the American Medical Association (JAMA) and the Centers for Medicare and Medicaid Services (CMS) put the figure even higher, estimating that up to 30 percent of all U.S. healthcare spending qualifies as waste. Efforts to increase accuracy and transparency in medical billing have the potential to significantly improve financial and health outcomes for patients and payers across the U.S.

For organizations responsible for reviewing those bills and determining fair reimbursement, that variation increases operational cost. Converting documents into text is only part of the problem. The harder part is turning highly variable medical billing documents into structured, validated, review-ready data that can support accurate, timely, and transparent payment decisions. When information is difficult to interpret and validate, it becomes harder to deliver the fair, accurate, and transparent reimbursement decisions that healthcare stakeholders depend on.

WellRithms and its mission

WellRithms helps organizations bring fairness, accuracy, and transparency to medical bill review. The company combines clinical expertise, AI-powered advanced analytics, and physician-informed review logic. These capabilities support more precise payment decisions and help clients manage complex healthcare costs more effectively.

That work happens at the line-item level, which is a key differentiator for WellRithms. Many bill review approaches evaluate charges in the aggregate and apply broad adjustments across an entire bill. With WellRithms, each charge is analyzed on its own merits. One of many case studies shows that WellRithms reduced a $7.3 million hospital bill by more than $4 million, avoided provider disputes, and offloaded financial risk. That level of precision helps support that reimbursement reflects the care that was delivered, supporting fair and defensible outcomes for all parties involved.

The business challenge

As WellRithms continues to grow, demand for accurate medical bill review continues to increase. Clients expect faster, more consistent results, creating pressure to scale operations without expanding manual document preparation at the same rate. At the same time, healthcare organizations increasingly expect reimbursement decisions that are timely, transparent, consistent, and defensible.

WellRithms’ review system already applied AI-powered advanced analytics and physician-informed logic to medical bills. As bill volume and document variability increased, the document preparation step became a strategic constraint. Complex itemized bills couldn’t be passed directly downstream as raw optical character recognition (OCR) output. They had to be interpreted, structured, normalized, and validated before they could support reliable review by WellRithm’s existing system. The business opportunity was to transform document processing from a manual dependency into a scalable document intelligence capability.

With advances in intelligent document processing (IDP), Amazon Bedrock foundation models, and cloud-based AWS AI services, WellRithms saw an opportunity to combine these capabilities with its medical billing expertise to meet rising client expectations, support future growth, and strengthen its competitive position.

The partnership and solution approach

WellRithms built its IDP solution on AWS. The team used the AWS Generative AI Innovation Center IDP Accelerator as a reference architecture and adapted it to the unique challenges of medical itemized billing. By combining proven cloud infrastructure with AI capabilities on AWS, WellRithms accelerated innovation and positioned the solution for long-term growth. The outcome is a differentiated solution that transforms highly variable medical billing documents into review-ready data. The solution delivers greater scalability, operational efficiency, and reviewer focus on higher-value clinical and billing decisions.

A key decision in the solution design was to move beyond a single extraction approach and instead implement a tiered document processing strategy. Medical bills vary significantly in quality and complexity. They range from clean digital documents to low-quality scans, multi-page itemized statements, and highly variable provider-specific formats. Rather than treating every document the same, WellRithms uses a combination of extraction techniques. Each technique is applied based on the characteristics of the individual bill.

Traditional OCR-based approaches handle simpler and cleaner documents. More advanced AI-powered techniques—including large language models (LLMs) and vision-language models (VLMs)—handle documents that require layout-aware understanding. WellRithms continuously benchmarks these approaches against real-world billing documents to evaluate accuracy, consistency, and cost. This allows the company to determine which extraction method performs best for different document types. The result is a scalable foundation for document intelligence that can improve over time without requiring wholesale changes to downstream review processes.

The transformation

The new document processing capability changed how WellRithms operates; from how bills are prepared to how reviewers spend their time. Before this capability, a complex itemized bill could arrive and immediately require manual attention. A reviewer with knowledge of medical billing codes, provider formats, and clinical context might spend a significant amount of time putting the document into a workable state before any review logic could be applied. As bill volume grew or document quality declined, that preparation burden scaled with it.

With the new adaptive workflow, the same bill moves through an automated process. Documents are routed through different processing paths based on workflow-defined criteria, and the appropriate extraction and structuring approach is applied. By reducing administrative preparation work, experts can spend more time applying clinical, billing, and payment integrity expertise where it creates the greatest value. The operating model is changing as a result. Document handling is no longer a time-consuming process but rather an automated capability that prepares work for human experts.

Results

The AI-powered solution demonstrated strong technical performance and operational readiness during the validation period. The automated workflow successfully processed 2,820 pagesand98,377 extracted lines, validating the solution’s ability to handle complex and data-intensive bills with various lengths.

The results demonstrate a significant transformation in processing efficiency, as shown in Table 1. Under the previous manual workflow, a single bill required approximately 8 hours of human effort. The same bill can now be processed in 15–20 minutes of expert review time with an average AI processing time of 70.1 seconds per bill. This represents approximately 30 times faster processing capability (Table 1). The turnaround time decreased from 5 business days to 1 business day (5 times faster), supporting faster client delivery and improved client experience.

Table 1. The key performance improvements provided by AI on bill processing.

*Assumes continuous AI processing time and excludes human review and quality assurance.

The validation also demonstrated that AI performance remains strong across various bill quality levels and page lengths. The current page processing throughput is 1,167 pages per hour. An AI processing speed of 3.1 seconds per page provides a buffer of approximately 9.5 times processing capacity, assuming continuous AI processing time and excluding human review and quality assurance. These results demonstrate that the AI-powered solution can handle significantly higher bill volumes without linear increases in operational staffing resources.

Broader business impact

The strategic implication of this transformation extends beyond operational efficiency. The AI solution is making document preparation more scalable and adaptive. Wellrithms can now process higher bill volumes and more diverse provider formats while maintaining client-expected quality. Specialized reviewers can spend more of their time where their expertise has the highest impact: clinical validation, billing exceptions, judgment-based analysis, and payment integrity review.

The immediate result is a more scalable and defensible operating model. In this model, document intelligence amplifies existing expertise. Human judgment is focused on the decisions that create the most value. The model strengthens the consistency, transparency, and defensibility of the reimbursement process.

Looking ahead

Adaptive document processing is a foundation to build on. Where today’s solution transforms documents into structured data, the next generation will help reviewers evaluate that information in a broader billing context.

With more reliable, structured data flowing into review workflows, WellRithms is positioned to layer deeper intelligence on top of that data. The team is exploring advanced agentic architectures capable of reasoning across documents, identifying exceptions, validating extracted information, and assisting reviewers throughout the full bill review lifecycle.

With continuous innovation in technology, WellRithms’ mission remains unchanged: helping organizations make fair, accurate, and defensible reimbursement decisions at scale.

To explore how intelligent document processing can transform your own workflows, visit the AWS generative AI Innovation Center IDP Accelerator on GitHub or contact your AWS account team to discuss your document processing challenges.

References

• Medical billing errors growing, says Medical Billing Advocates of America
• Waste in the US Health Care System: Estimated Costs and Potential for Savings
• $7.3 million hospital bill by more than $4 million, avoided provider disputes, and offloaded financial risk