The End of Medical File Review Bottlenecks

We've been doing it wrong for decades.

Insurance companies spend weeks reviewing medical files that machines can process in seconds. The industry has accepted this inefficiency as an unavoidable reality.

Until now.

The traditional approach to medical file review involves humans reading through hundreds or thousands of pages, taking notes, and condensing information into summaries. If anything is missing, they start over.

This process is not just slow. It's fundamentally flawed.

Why We've Been Stuck

The challenge has always been document inconsistency. Medical records come in wildly different formats. The same provider might deliver documents in completely different formats every month. Previous automation attempts failed because they were keyword-driven. They expected similar document structures across providers.

In the real world, there's an endless variety of document formats. Earlier solutions were brittle, breaking down when confronted with unexpected layouts or terminology. Traditional NLP tools struggled with this lack of formatting consistency. The industry resigned itself to human-only review as the only viable option.

Large language models have changed everything.

The Transformation

Nomad Data’s Doc Chat processes approximately 250,000 pages of documents per minute. Let that number sink in.

It produces summaries in custom formats defined by the organization. More importantly, after creating the initial summary, users can continue to interrogate the document. They can ask follow-up questions that immediately update the summary. This real-time interaction was impossible with traditional methods.

The computer never gets bored. It never gets distracted. It reads page 1,500 with the same attention it gave to page 1. It has no short attention span. And it does all this in seconds.

Consistency Through Customization

Doc Chat uses what we call "presets" - custom formats for summaries that can vary depending on the type of review. When the system follows this format, you get standardized output across all medical files. This consistency is something humans struggle to maintain.

People have different styles. They focus on different elements. They get tired.

With Doc Chat's presets, the same summary format is enforced across all documents. This standardization eliminates the quality inconsistencies that plague manual reviews.

Real-World Transformation

One client was processing medical documentation for litigation claims reaching 10,000 to 15,000 pages. It previously took them six to twelve weeks to summarize these documents and create even a basic table of contents. With Doc Chat, they completed the same summarization, including follow-up questions, in about 30 minutes.

Think about that transformation. A three-month process reduced to half an hour.

Other clients have seen medical summarizations that took days or weeks now completed in 10-15 minutes.

What happens to your business when these bottlenecks disappear?

Beyond Speed: Quality Improvements

AI doesn't just work faster. It often uncovers information that human reviewers consistently miss.

In one case, the system noted material differences in how a patient described an incident as they moved through time and met with different medical providers.

Humans struggle to hold large amounts of information in their heads simultaneously. They miss connections between documents separated by hundreds of pages. Doc Chat compares documents and pages instantly, enabling more sophisticated summaries and fraud detection capabilities.

All of this happens in minutes rather than weeks.

The Human Element Reimagined

We're not replacing humans. We're upgrading their role.

Humans are being replaced in the summarization process, but not in the review process. The summarization now takes minutes instead of weeks. The human role shifts to asking follow-up questions. With the summary in hand, reviewers know where to dig deeper. They know what should be appended.

This combines the best of both worlds. AI handles the rote reading and summarization. Humans apply their expertise where it matters most. The new model allows people to use the creative thinking part of their brain more than just the rote reading part.

Many employees previously tied up in summarization can be repositioned into other roles. Companies can also slow their hiring as volumes grow and focus on investing in the capabilities of their workforce.

Data Enrichment

Doc Chat connects directly to external data sources to enrich summaries. It can verify information against third-party data. It can add context that isn't mentioned anywhere in the medical records.

The system can even incorporate cutting-edge science and research, providing color that isn't in the original documents. This capability transforms summaries from mere condensations into intelligence-enhanced documents.

Economic Impact

The first impact is efficiency gains for insurance companies. This improves margins and bottom lines. Ultimately, competition will drive some of these savings to consumers through lower prices. Enhanced fraud detection reduces fraudulent payments, producing material savings for both consumers and healthcare providers.

According to McKinsey, health insurers using AI for claims processing can save between $150 million to $300 million in administrative costs and approximately $380 million in additional savings for every $10 billion in revenues. These figures highlight the economic transformation potential.

Barriers to Adoption

Despite these benefits, many companies hesitate to implement AI solutions. Insurance is highly regulated. Regulations around artificial intelligence are extremely new with vague definitions. Companies worry about compliance, bias, and making the wrong decision.

The biggest risk, however, is making no decision at all.

While companies deliberate, competitors are implementing these technologies. They're becoming more efficient. They're offering more competitive prices to consumers. The cost of inaction exceeds the risk of action.

The Paradigm Shift

We're witnessing a fundamental change in how medical expertise is deployed.

The new model has computers handling all rote reading, summarization, and basic fraud detection. They present detailed summaries to humans, who decide how to investigate further. This leads to improved efficiencies, better outcomes, and lower costs for all parties. It also reduces claims backlogs that result from waiting on the summarization step.

We're moving from "review everything" to "focus human expertise where it matters most."

The Future of Medical File Review

The transformation we're seeing is just the beginning.

As AI capabilities continue to advance, we'll see even more sophisticated analysis and integration with other systems. The companies that embrace this change now will define the future of insurance. Those that cling to manual processes will find themselves increasingly unable to compete on speed, accuracy, or cost.

The end of medical file review bottlenecks isn't just possible.

It's happening now.