BPOs vs. AI Medical Records Summary: Risks and Concerns

In the ever expanding world of modern claims, high turnover, heavy workloads, and an aging workforce have all made it hard for claims teams to keep up. As claims volumes rise and experienced adjusters retire, enterprises are increasingly looking for support when processing claims. Whether this support is automated through an AI medical records summary or outsourced, such as offshore labour, will depend on the company and its needs. 

Business process outsourcing (BPOs) is a method of subcontracting various business services to third party vendors. BPOs are typically offshore, labour based solutions for manual, time consuming tasks. In claims, these are tasks like labelling or tagging files, converting paperwork into PDF, uploading documents, removing duplicates, or chronologizing medical records. For many companies outsourcing, BPOs work for these tasks because the work is highly repetitive, easy to quality check, and relatively cost conscious.

BPOs have historically been the default for heavy manual workloads. Today, though, AI claims documentation platforms (CDPs) offer a different alternative through claims documentation software. While both AI-powered CDPs and BPOs can reduce manual workload for claims teams, they differ in the risks, efficiencies, and long-term scalability. Here are some of the key areas that claims teams should keep in mind. 

Security, Compliance, and Risk

Security risks exist in any form of automated support, but AI powered CDPs are designed with security and compliance in mind. Since AI is a newly regulated sector of the economy, AI claims documentation software for claims are built with compliance (and security) in mind – from the very beginning. 

In keeping with this compliance, AI risk tends to be highly visible. AI errors are logged, auditable, measurable, and accounted for in “human in the loop” controls. Although some of AI’s risks have made headlines, so much attention to the technology means that most tools are very careful to remain compliant. No one wants to be the next “AI hallucination” headline – so AI vendors are asked for SOC 2 Type II reports, penetration test results, or data residency guarantees. For enterprise level claims documentation software platforms, security is a priority from the beginning, and accuracy is built in. 

This is not always the case with BPOs, which tend to prioritize quality control and cost. Human error in BPO workflows is not caught as systemically as errors in AI. Plus, the human aspect of offshore work means sensitive health information is accessed by many human reviewers, credentials get reused or rotated across shifts, and data ultimately crosses borders – a risk that is not always communicated to clients or is inline with rising regulations.

AI Medical Records Summary Efficiency

AI medical records summary tools can augment your business processes where you are, which is a key component of their efficiency. With flexible, modular systems that can be built and adapted to your specific industry and line of business, an AI powered CDP is created to keep workflows running smoothly for you and your team. This means getting verifiable results back quickly, even when you have unexpected additional documents or co-mingled claimant files. Being able to add new documents into an AI powered CDP keeps your workflow flexible, organized, and responsive to your needs – so you can find documents quickly in meetings, update a file on the go, and keep up with large files that are ongoing. 

In contrast, BPOs often require more lead time and planning. Imagine you send a large file to be organized and indexed, then receive new documents related to it. Depending on your process, you might need to wait for the original file to come back before compiling – a delay you might not see while using AI. 

Domain Training for AI Medical Records Summary

Clinical expertise matters, especially in claims. An extensive and verifiable knowledge base is essential to train a working CDP. Similar to how AI medical record summary tools are designed with security in mind from the get-go, a domain trained model is reliable, flexible, and tailored to your company’s specific needs. A domain-trained claims model sees medical records, legal records, and correspondence differently. It understands the difference between the date of loss, the service date, and the date of reporting. It can put historical conditions and acute conditions into context. In short, it is already trained to do most of the work your organization needs. 

This is where traditional BPO models often fall short. While BPOs rely on manual processes and generalized workflows, they don’t invest in domain trained AI that learns from claims-specific data over time. As a result, outputs can be inconsistent, slower to scale, and heavily dependent on individual reviewers rather than a continuously improving intelligence layer.

A Strategic Choice for Claims Processing Automation

As claims organizations weigh their options, the difference between short-term relief and long-term advantage becomes clear. BPOs can help manage repetitive, manual tasks, but they weren’t built to scale with growing complexity, tightening regulations, or the need for consistent, defensible outcomes.

In an environment where claims are only becoming more complex, investing in AI medical record summary solutions isn’t just a productivity decision—it’s a strategic one.

Want to learn more? Our Wisedocs Buyer’s Guide is an in-depth playbook into choosing a CDP, designed to help first time buyers make informed choices about their document processing solution.