At Slingshot, we harness the power of Large Language Models (LLMs) to drive innovation and impact within the DRG claims management space. While many people quickly conflate our work with the recent Generative AI revolution, wondering what distinguishes us from just a chatbot, LLMs are far more than just chatbots. They are versatile tools capable of transforming numerous fields through their ability to both generate and understand text. In this blog post, we aim to demystify LLMs and highlight their diverse applications to draw a distinction between us and many of the other uses for LLMs you’ve likely read all about, particularly focusing on how we at Slingshot utilize these models to extract meaningful insights to speed up healthcare claim auditing.

What is an LLM?

Many of us are tired of hearing the term “Large Language Model,” but bear with me. Most of the time, we discuss LLMs in terms of their ability to generate new content that appears similar to other content, like this blog post (which was definitely written with the assistance of an LLM). However, at Slingshot, we focus on the other powerful capability of LLMs: extracting meaning from text.

Fine Tuning

At Slingshot, we take these already impressive models and train them to excel at specific, complex tasks.

Fine-tuning is one of the processes we use to make our models exceptionally proficient at specific tasks. Imagine having a basketball team where each player is generally good at the game. That would be like using a production, consumer LLM like ChatGPT or Anthropic’s Claude. It might get the answer right to most questions but will miss sometimes, especially as the task becomes more specific and complex. Now, what if you could train one player to be the absolute best at a specific skill, like Michael Jordan with his slam dunks? That’s what we do through a process called fine-tuning.

At Slingshot, we take these already impressive models and train them to excel at specific, complex tasks. For example, we can fine-tune an LLM to become the Michael Jordan of identifying whether a certain diagnosis has been recorded in medical texts. This specialized training means our model can swiftly and accurately pick out crucial information, helping healthcare professionals save time and make better-informed decisions. And we can scale this out. We can create the Michael Jordan of checking for a specific criterion that you determine, knowing that validation is variable and complicated.

Security

At Slingshot, we understand that handling sensitive healthcare data requires the highest level of security and compliance. As a service provider for healthcare companies, we take this responsibility extremely seriously. We are proud to be HIPAA compliant and SOC2 Type II certified, ensuring that we meet rigorous standards for data protection and security.

Our commitment to security means that any information you send our way is treated with the utmost care. Our systems are designed with robust security measures to safeguard your data at every step. This includes encryption of data in transit and at rest, regular security audits, and strict access controls. By partnering with us, you can trust that their data is in safe hands.

Conclusion

Our fine-tuned Large Language Models are the Michael Jordans of complex clinical validations, designed to tackle tasks with unmatched precision and speed.

At Slingshot, we’re redefining what AI can do in the healthcare industry. Our fine-tuned Large Language Models are the Michael Jordans of complex clinical validations, designed to tackle tasks with unmatched precision and speed. With top-notch security measures that far exceed public chatbots, we ensure your sensitive data is always protected.

Curious to see our LLMs in action? Reach out to schedule a demo and discover how we can streamline your DRG claims management. And don’t miss my next blog, where I’ll explore the power of our advanced text annotations versus the simplicity of ctrl+f. Stay tuned for more exciting insights!