Closing the Gaps: AI’s Role in Tackling the Insurance Industry’s Biggest Challenges

Medical record reviews are a perfect fit for artificial intelligence (AI). They’re tedious, labor-intensive, and demand meticulous attention to detail across hundreds, sometimes thousands, of pages. For underwriters, this type of repetitive work can quickly lead to fatigue, burnout, and even mistakes. People simply aren’t designed to spot a single line of new text hidden among hundreds of nearly identical pages—but AI is. This is precisely where AI excels. It never gets bored, doesn’t tire, and works around the clock, pinpointing discrepancies and inconsistencies with unmatched precision across massive datasets.

How AI Models Drive Efficiency

​​How does the AI achieve this? When it comes to medical record reviews, two primary types of AI models play a crucial role: discriminative models and generative models. Understanding how these models work is key to grasping how AI revolutionizes the medical review process. Discriminative models shine when it comes to sorting and extracting critical details. They classify, categorize, and surface key information from even the most complex medical records. Whether it’s identifying whether a record mentions diabetes or pulling out medication names and diagnoses, these models are built to ensure precision and efficiency.

Generative models, on the other hand, take things a step further. They don’t just analyze; they create. They’re capable of generating responses to open-ended questions, providing dynamic and contextually relevant insights. Imagine asking, “What are this applicant's potential diabetes complications?” and receiving a detailed response that feels as though it was written by a medical professional. This level of versatility and depth is transformative for medical record reviews.

Specialized vs. General-Purpose AI

With the rise of generative AI tools like ChatGPT, Claude, and Microsoft CoPilot, I’m often asked if these general-purpose models can be used for medical record reviews. It’s an exciting question because it shows that people are beginning to see the incredible potential of AI in this space. But there’s an important distinction between these general-purpose tools and specialized AI like DigitalOwl’s. General-purpose AI isn’t designed for the specific nuances of medical record reviews. It may not understand industry-specific terminology or extract the precise information needed for critical decision-making. Specialized AI, however, is built for this exact purpose.

It is important to ensure that the AI you’re using is specifically trained by experts who understand the unique challenges of the life insurance industry. A well-designed AI should draw on a robust and up-to-date knowledge base, covering a wide range of medical disciplines, to deliver insights that are accurate, relevant, and tailored to the needs of insurance professionals.

By leveraging this comprehensive Medical Knowledge Base, DigitalOwl’s AI goes beyond basic entity recognition, interpreting and contextualizing medical details with precision. It excels at identifying connections between impairments, medications, and comorbidities, as well as assessing the severity of conditions. This capability makes it an indispensable tool for underwriters, enabling more informed and confident decision-making.

The Importance of Accuracy and Transparency

Another key difference between specialized AI and general-purpose AI lies in accuracy and transparency. At DigitalOwl, we’ve designed our AI to avoid “hallucinating”—a term used to describe when AI makes up information. If our system isn’t confident in its findings, it doesn’t fabricate answers. Instead, it errs on the side of caution, surfacing details for underwriters to review. This level of transparency is critical. Users can trace every piece of information back to its source document with just one click, ensuring that the process is explainable and fully auditable. Trust is the foundation of effective AI, and transparency is how we build it. 

• DigitalOwl’s AI avoids “hallucinating” or fabricating answers when unsure.
• Every piece of extracted information is traceable back to the source document with a single click.
• This explainability fosters trust and aligns with regulatory expectations.

In contrast, general-purpose AI often operates as a "black box," meaning it’s unclear where the information originates or how conclusions are reached. While these tools excel at a wide range of tasks, such as drafting emails or brainstorming content ideas, they are not purpose-built for medical records. As a result, they frequently fall short of meeting regulatory standards for transparency, auditability, and explainability—critical requirements in the life insurance industry. 

To learn more about how to evaluate the reliability of AI, download our white paper.

Understanding AI Errors

That’s not to say specialized AI is perfect. Like any system, it can make mistakes. However, when AI is specifically designed and trained for medical record reviews, it can be optimized to minimize errors that could affect critical decision-making. 

AI errors generally fall into two categories: misses, also known as recall errors, and noise, also referred to as precision errors. Misses happen when AI fails to surface relevant information—something that could have serious implications in medical reviews. For this reason, our AI is designed to err on the side of inclusion, flagging anything it’s uncertain about for human evaluation. 

On the other hand, noise occurs when AI extracts details that are technically accurate but presented out of context. For example, it might misinterpret family medical history as the patient’s own. These errors can cause skepticism of AI outputs. However, with features like DigitalOwl’s click-to-evidence tool, noise errors are easily verified and corrected, reinforcing confidence in the results. 

The Benefits of Adopting AI

Still, switching from manual medical record reviews to an AI-powered process can feel daunting. For underwriters, changing processes they’ve relied on for their entire careers is no small ask. But the benefits should not be overlooked. By taking over repetitive, time-consuming tasks, AI allows underwriters to focus on what they do best: making critical decisions and applying their expertise to higher-value areas.

The efficiency gains with AI are nothing short of transformative. Customers using DigitalOwl report saving 60% to 70% of the time they would have spent on manual medical record reviews. This isn’t just about working faster; it’s about working smarter. AI enables consistency across reviews, eliminating the variability that can arise from individual judgment, such as differing levels of experience or unconscious biases. This standardization ensures a higher degree of accuracy and reliability, which is critical in an industry where decisions can have far-reaching implications.

These benefits are becoming even more crucial as the insurance industry faces an impending talent shortage. According to the U.S. Bureau of Labor Statistics, the number of insurance professionals aged 55 and older has surged by 74% in the past decade. Within the next 15 years, 50% of the current workforce is expected to retire, leaving over 400,000 positions unfilled. This looming retirement wave creates a substantial knowledge gap as experienced professionals exit the industry. AI can help bridge this gap by streamlining workflows and equipping newer professionals with tools that surface critical information, helping them make informed decisions without requiring years of on-the-job expertise.

Burnout is another challenge that AI is uniquely positioned to address. A 2022 study reported a 39% burnout rate in the insurance industry, placing it among the top five sectors for burnout. Repetitive, time-consuming tasks like medical record reviews contribute significantly to this issue, leading to stress, disengagement, and high turnover rates. By automating these tasks, AI not only enhances efficiency but also lightens workloads, allowing professionals to focus on higher-value, strategic work. This shift not only improves employee satisfaction but also fosters a more engaged, productive, and resilient workforce.

Selecting the Right AI Provider

Not all AI service providers are created equal. Implementing an AI solution requires careful consideration of critical factors to ensure privacy, security, and regulatory compliance. A responsible AI provider should diligently adhere to the principles and ethics of transparency, fairness, accountability, privacy, and security. While not exhaustive, the following examples illustrate how DigitalOwl is committed to upholding these standards.

Transparency

Transparency ensures that users can trace how decisions are made, understand the data and logic behind the AI’s actions, and verify that the system operates fairly and without bias. This is crucial for building trust in AI systems, particularly in high-stakes fields like healthcare, insurance, and legal services. Here are examples of practices that a transparent AI provider should uphold:

• Link extracted data to source documents.
• Make the business logic behind the recommended decision clear

Fairness

AI systems should operate without bias and should treat all individuals and groups equitably. This requires the data used to train models is representative and that the algorithms do not perpetuate existing inequalities. Ensuring fairness is crucial for maintaining trust and ethical standards, particularly in applications where AI decisions can significantly impact people’s lives.

• Limit the role of AI to answering predefined questions.
• Implement bias mitigation strategies.
• Conduct regular audits of AI outputs to detect potential biases.
• Use diverse training data to ensure representation across different demographics.
• Continuously monitor and adjust AI models to address any identified biases.

Privacy and Security

This refers to the measures taken to protect sensitive data and ensure that AI systems operate in a manner that safeguards personal information from unauthorized access, breaches, or misuse.

• Adhering strictly to SOC2 Type II and HIPAA
• Implementing robust data security policies and practices
• Use industry-standard best practices for encryption to protect information both in
• transit and at rest.
• Proactively identify and fortify potential vulnerabilities through comprehensive
• annual security audits.
• Facilitate secure transmission, deletion, and retrieval processes.
• Have a comprehensive incident response plan that includes defined roles and
• communication protocols.
• Backups are encrypted and geographically distributed, with regular testing of
• restore processes.

AI is no longer a futuristic concept but a transformative tool that’s being used to address some of the insurance industry’s most pressing challenges. By enhancing the speed, accuracy, and consistency of medical record reviews, AI tools empower underwriters to focus on strategic, high-value work. As the industry navigates workforce shortages and rising demands for efficiency, adopting the right AI solutions is essential to staying competitive and delivering superior outcomes.