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Revolutionizing Underwriting with AI- Powered Medical Summarization: The Rise of Language Generation




The importance of medical record summarization

Medical records and other medical documents can be lengthy and complex, making it challenging for insurance professionals to quickly and accurately understand and extract insights from them. In order to improve these processes, tools that can efficiently summarize this type of information in a clear and concise manner are required.

In the past, summarizing medical records was a time-consuming and labor- intensive task, typically performed by humans. This process was prone to errors and could be influenced by the subjective biases of the individuals summarizing the medical documents. However, advances in artificial intelligence (AI) have made it possible to automatically summarize medical records and documents.

What are Language Generation Models

Language generation models are a type of AI that are specifically designed to generate written text. These models are trained on large datasets of text and able to generate new text that is similar in style and content to the text they were trained on. These models are also able to use the patterns and structures learned from the training data to generate text that is coherent and flows naturally, making it difficult for humans to distinguish the automatically generated text from one written by a person.

Language generation models are being increasingly adopted in various fields, such as journalism, customer service, marketing, finance and education. These models offer the potential to streamline processes and reduce the workload of tasks that require large amounts of written communication.

The Advantages of Language Generation Models for Medical Summarization Compared to Traditional AI Solutions

The difference between traditional AI solutions for medical summarization and the newer language generation models is significant and cannot be overstated. While the output of traditional AI solutions is lists of “medical concept entities”, language generation models generate actual text. This fundamental difference between a “list of items” and a narrative text that is easier to understand and digest is the major advantage of language generation models. These models are able to capture context, narrative and complex relations in a way that traditional AI cannot, making them a valuable tool for medical summarization.

For example, consider the following two summaries: An entity recognition-based AI summary:


Conditions:

Hypertension

Diabetes, Type 2

heart attack

Procedures:

Coronary artery bypass surgery

Blood pressure medications

Hgb A1c

Referrals:

Cardiologist

A generative model-based summary: “The patient is a 55-year-old male with a history of hypertension and diabetes who was admitted to the hospital for a heart attack and underwent a coronary artery bypass surgery. He has made a good recovery and will be discharged with a prescription for blood pressure medication and a referral to a cardiologist for follow-up care. He also has a history of Type 2 diabetes; his most recent Hgb A1c was 7.2 It is important to note that the patient has a tendency to skip his medications.”

While the traditional summary provides a list of conditions, procedures and referrals - the generative summary offers a cohesive narrative, providing context and an understanding of the patient’s medical history and treatment.

The Impact on Underwriting

The use of language generation models for medical summarization has the potential to revolutionize the field of underwriting. Underwriting, or the process of evaluating the risk of insuring an individual or group, requires a thorough understanding of an applicant’s medical history and current health status. In the past, underwriters would have to review lengthy medical records and documents to extract this information, a time-consuming and potentially error- prone process.

However, with the use of language generation models, underwriters can quickly and accurately get a summarization of the medical records and documents, allowing them to make more informed decisions about an applicant’s risk. These models can capture context, narrative and complex relations in a way that traditional AI solutions cannot, providing a more complete and accurate understanding of an applicant’s health.

In addition, language generation models can reduce the workload of underwriters by automating the summarization process - enabling the underwriters to focus on the important task of evaluating the risk and making the required decisions and not on the manual task of sorting through documents to get an understanding of patient medical history - leading to a more efficient underwriting process and improved customer service.

Overall, the adoption of language generation models for medical summarization is a significant development that has the potential to greatly impact the field of underwriting. By providing a more thorough and accurate understanding of applicants’ medical histories and health status, these models can improve the efficiency and accuracy of underwriting, leading to better outcomes for both underwriters and applicants.




*DigitalOwl uses proprietary technology for its NLP and Generative Text capabilities and does not rely on third-party services


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