Claims professionals habitually spend hours sifting through hundreds of pages of medical records for every single claim. Now, thanks to generative AI that sorts and flags key information up front, claims professionals can skip the document grind and focus on what matters: making smart calls and avoiding expensive slip-ups.
However, this miraculous time-saving efficiency isn't without its challenges. Along with the ability to rapidly process and extract meaning from vast collections of complex documents, many organizations have stumbled using AI for document processing by setting unrealistic expectations, leading to widespread disillusionment when the technology fails to deliver.
Three specific challenges directly affect the success of AI document systems: workforce adoption issues, compliance risks, and cost concerns.
First, workforce adoption issues arise when employees, without proper expectation-setting, experience immediate frustration. This causes them to conclude, "This isn't working," at the first sign of error, often resulting in abandoned projects before the AI system can demonstrate its value. Second, in highly regulated processes, errors can trigger significant legal and financial consequences that create substantial risk. Third, organizations frequently underestimate the operational costs of running sophisticated AI models at scale.
These challenges are particularly evident in highly regulated insurance processes that involve complex and lengthy documentation with significant compliance requirements but can be avoided with understanding of the technology's limitations and wise usage and mindful oversight of the programmed skillset.
Take Medicare Set-Asides (MSAs) managed by Medicare secondary payer compliance companies. MSAs are complex financial arrangements primarily used in workers' compensation and liability claims to allocate funds for future medical treatment. Handling MSAs demands analysis of extensive medical records, billing statements, physician recommendations, and prescription histories.
Claims professionals invest 15 to 20 hours manually reviewing an average of 300 to 500 pages of medical documentation per claim. Complex cases can often exceed 1,000 pages. This creates a large opportunity to leverage AI to help with the understanding and processing of data. However, mistakes can come at a significant cost, potentially resulting in rejected MSA submissions, delayed settlements, additional reserve requirements, and even long-term Medicare recovery actions against insurers or claimants who failed to properly protect Medicare's interests.
These potentially costly consequences make a thoughtful AI implementation essential for MSA processing. Success with AI for document processing occurs when it is used as a tool that enhances workflows. This is where intelligent document processing (IDP) systems demonstrate their potential, as they can combine AI with document management technologies to transform how complex, unstructured documents are handled.
By presenting AI as an enhancement to the claims professional's workflow rather than a replacement, a company is able to address both workforce adoption concerns and error risks simultaneously. The key is creating a system where claims professionals maintain decision-making authority while the AI handles the time-consuming organizational tasks. This integration is what makes document processing improvements possible.
Breaking down a typical MSA review process, roughly 30% to 40% of that time is spent on manual document organization and navigation. This includes sorting pages, identifying document types, and locating relevant information across hundreds of pages. The IDP system tackles these challenges by handling the initial heavy lifting. It digitizes and organizes documents, identifying important details automatically. Claims professionals can then work with this pre-organized data, significantly reducing the time spent on manual document sorting and navigation. The result is a structured foundation that allows claims professionals to navigate efficiently through what was once an overwhelming volume of information.
The most effective implementations of these systems incorporate human verification. Claims professionals begin with the AI-organized information, make refinements and corrections where needed, and then use this enhanced foundation to perform their specialized analysis. This verification step ensures accuracy while still capturing significant time savings. Once the claims professional confirms or corrects the AI's initial processing, the system can then perform more sophisticated tasks with the validated information.
For example, the AI system can identify and extract date references across hundreds of pages of documents, creating an initial chronological sequence. Rather than manually finding each date throughout hundreds of pages, claims professionals review the pre-assembled timeline to verify its accuracy and completeness. They can spot missing events, incorrect dates, or sequence errors by reviewing the overall pattern of care rather than hunting for individual date references page by page. Once the claims professional validates this timeline, correcting any errors they find, the system uses this confirmed data to generate a comprehensive chronological view of medical events.
This could also work with keyword flagging. The AI system can be programmed to identify critical terms such as "surgery" throughout the documentation, whether this is in images or PDFs. This is especially valuable because surgical procedures often represent significant costs that must be accounted for in MSA calculations. When the AI highlights these terms, claims professionals can navigate to relevant sections instead of manually sifting through them with the risk of overlooking something. When poor document quality causes the system to inadvertently miss important keywords, claims professionals can flag them, helping the system learn and improve.
This brings us to the challenge of managing operational costs. Sophisticated IDP systems address this by intelligently determining the appropriate level of AI processing needed for each document. Rather than routing everything through the most expensive large language models, these systems analyze document complexity, classification certainty, and business value. This analysis allows them to allocate computational resources optimally. Routine documents can be processed using lightweight models, while only complex or high-value documents require advanced generative AI capabilities.
This intelligent resource allocation creates significant cost savings without sacrificing performance. As claims professionals verify results and provide corrections for document misclassifications, missed medical events, procedure code errors, and ambiguous treatment dates, the system gradually improves its ability to assess document complexity and determine appropriate processing levels. Rather than creating additional verification work, the system focuses human attention only on elements with low confidence scores or high business impact.
By using this feedback to come up with better instructions, the system is able to learn from claims professional corrections to recognize similar documents in the future, becoming more efficient with each processed claim. This creates a positive cycle where accuracy increases while resource requirements decrease over time, addressing the operational cost challenge head-on.
This approach to implementing IDP systems provides solutions to the challenges related to workforce adoption issues, compliance risks, and cost concerns. It prevents employee frustration by positioning the claims professional as the decision-maker while the AI serves as a sophisticated but at times imperfect assistant. It maintains crucial quality controls to reduce legal and financial risks by keeping the responsibility directly with the claims professional. Through learning the type of intelligence that is needed, it also manages operational costs effectively over time.
This MSA case demonstrates how AI can enhance human judgment in document-intensive processes. Even when claims professionals must still review key documents, the value comes from making that review more structured and focused. By creating a feedback loop that continuously improves performance and managing computational resources intelligently, organizations can transform initial AI disappointment into sustainable success. This balanced approach delivers better outcomes for all stakeholders while avoiding the pitfalls that derail many AI initiatives.
