From Data to Insights: The Future of Medical Record Summarization with Text Generation Models

DigitalOwl is a game changer in the industry, making the medical record review process simple and easy. Technology is constantly evolving, and DigitalOwl remains the industry leader in using generative text as a crucial tool. In a recent webinar, co-founders of DigitalOwl Yuval Man (CEO) and Amit Man (CTO) discussed how AI is the future of medical record summarization.

What are text generation models?

Technology is a force for change, but with its constant innovations, it can take time to keep up. So what exactly is a generative model? In simplest terms, a text generation model is a branch of artificial intelligence (AI) that involves teaching machines to write like humans. Generative text models, like ChatGPT, Google’s Bard, and DigitalOwl’s models, can create new content that summarizes the central ideas or themes in the original text. The benefits of text generation models extend beyond their ability to generate human-like content. Unlike extractive models, generative text models can understand more granular and ambiguous information without needing a pre-defined entity list for extraction. For example, using a text generation model for summarizing medical records for underwriting will paint a medical picture full of nuance, context, and complex relations. A generative model reduces costs and time without compromising quality, making it the not-so-secret weapon of the future.

Why You Should Care

Generative models capture complex patterns and dependencies in data. DigitalOwls product has been designed with human understanding in mind, making the data output more interpretable and explainable than other machine learning models. Generative text models can also be fine-tuned to the specific needs of individual clients or applications, making them a flexible and adaptable tools for a wide range of clients and lines of business. DigitalOwl paves the way for a new era of AI-driven solutions that cater to a broad spectrum of client needs, fostering increased trust, customization, and adaptability in the ever-evolving world of artificial intelligence.

Capture More. Surface More

In this industry, the more information you have, the better decision you can make. DigitalOwl’s product helps condense all of the critical information while highlighting anything that could affect a decision, like contextual medical attributes or casual relationships like the side effects of medication. DigitalOwl knows context is vital in this profession and ensures our clients have all the information they need to make the most educated decisions.

Designed for Human Understanding

Individuals exert significant effort to transform information for machine understanding, effectively serving machines rather than benefiting from their assistance. When processing information, it is much easier to remember a plot over just a list of data. Hearing, “The King died, and then the queen died,” is just a succession of information that will easily be forgotten. In contrast, if you hear, “the King died, and then the queen died of grief,” it is much easier to remember because context is attached to the information. Generative text is unique because it is designed with our plot-driven memories in mind.

From Passive Consumption to Active (Tailored Engagement)

Typically it is much easier to consume information when it occurs as a conversation between two parties instead of just one party relaying information to the other. Generative is exciting because it is a conversation between the client and the generative text that ensures a tailored engagement process with the tool, allowing clients to get all the information they need efficiently and concisely. This experience allows for a more active consumption of data for our clients. Instead of passively reading the information, they can engage with it, creating a more rewarding and thoughtful approach to processing data. Working with these Generative Text models helps our clients process data more efficiently and accurately, allowing them to have a uniquely tailored experience with the data they are analyzing.

Challenges and Considerations

Despite the numerous benefits, implementing generative text models for medical record summarization is challenging. The four main challenges are hallucinations (accuracy), coverage, usefulness, and black box logic. Occasionally generative text can make up information or miss essential nuances. In the case of hallucinations, when a model creates data points that are nowhere to be found in the source material, it can cause many issues for underwriters. We want to eliminate this possibility and continue to refine it at DigitalOwl to make the most accurate model. Coverage can also be a challenge when dealing with generative text. At DigitalOwl, we want to ensure that our platform includes all the relevant information, not just what the generative text AI thinks is essential. When using these generative text models, it is crucial to know why the data is being put out so we strive to demystify the AI process. Black box logic, not knowing how to explain or track where the information output came from, can be unnerving in the generative text process. DigitalOwl continues to work to ensure every result has an explanation. Even with these challenges, the generative text is still a crucial tool in the future of medical record summaries.

DigitalOwl’s Unique Approach

Using its three proprietary technologies: entity extraction models, generative models, and the medical knowledge base, DigitalOwl solves the challenges and delivers a solution to clients with the highest accuracy, including all the information they need, is user-friendly, and allows users to see where each data point came from by clicking on it.

The combination of the three tools DigitalOwl developed enables the creation of generative text summaries verified by the entity extraction model and the unique medical knowledge base. The result is a medical chronology with powerful features like human-like summaries of the applicant, medical documents and impairments, detailed habit information (smoking, alcohol, and drug use) along with timelines, impairment rundown sections, medical codes (ICD, Snomed, etc.), providers sections, and more.

Adopting cutting-edge technology, like DigitalOwl’s generative text model, is essential in keeping up to date in medical record summarization. These AI systems can revolutionize the process by enhancing efficiency, accuracy, and providing deeper insights. Embracing the power of generative text models will undoubtedly play a significant role in shaping the future of medical record summarization.