Large Language Models are transforming Healthcare

Introduction

The rapid evolution of Natural Language Processing (NLP) has reshaped many industries, and with Large Language Models (LLMs) now in the mix, healthcare is ready for it’s own eureka moment. These advanced models are equipped to make sense of complex clinical data, take on tedious administrative tasks, and enhance decision-making in clinical settings. LLMs are organizing and extracting from health data in ways that make it more accessible and useful, all while ensuring control remains with the individuals and healthcare providers who generate it.

What are these LLMs?

LLMs represent a huge step forward from traditional machine learning (ML) models. While old NLP methods relied on strict rules and shallow models, LLMs—like GPT-4 and Meta’s Llama—are trained on massive datasets with billions of parameters! This enables them to not only understand the context of clinical information but also reason through complex inputs and generate human-like text. To learn more about them, you can check out this page: https://aws.amazon.com/what-is/large-language-model/

Why do we need LLMs and why does traditional NLP fail?

Traditional ML models required neat, structured data and a lot of manual effort. But LLMs? They can work with anything—like the often chaotic and free-form patient notes—offering much deeper insights and more flexibility. Their ability to grasp context and connect the dots makes them perfect for navigating the complexities of healthcare data.

Although LLMs can be applied across various healthcare domains, for this article, let’s focus on a particularly rich but challenging area: patient notes. These notes are packed with valuable information about patient histories, symptoms, and treatments—a true goldmine of clinical insights. But for years, we’ve struggled to fully tap into this resource due to the messy, unstructured nature of the data.

Traditional NLP methods haven’t been up to the task. They often stumble over medical abbreviations, clinician-specific writing styles, and the inherent complexity of patient narratives. LLMs, however, offer a way to mine this goldmine effectively. With their ability to understand unstructured data and capture context, LLMs are turning these once chaotic notes into actionable information.

Solutions

At Cognome, we are using LLMs to turn patient notes from chaotic scribbles into powerful tools for clinical decision-making and research. Here’s how:

• Identifying Sepsis Early: Sepsis is a critical, life-threatening condition. Our Sepsis-LLM scans patient notes in real-time to spot subtle signs of sepsis, assigning probabilities that help clinicians act quickly and potentially save lives.
• Mapping CPT Codes: Medical coding can be a drag. Enter Autochart-LLM, which automates the process by linking procedures in patient notes to CPT codes, cutting down on tedious admin work and boosting accuracy.
• Matching Patients to Clinical Trials: Matching patients to trials is tough. Our Clinical Trial Matching model reads patient notes and matches them to trials by aligning the content with trial criteria—speeding up the process and creating new treatment opportunities.

LLM Challenges and how we approach them

That said, LLMs aren’t without their challenges. They require significant computational power to train and deploy, and sometimes they can “hallucinate”—producing inaccurate or biased outputs. Then there’s the critical issue of privacy, especially in healthcare, where patient data must be handled with extreme care.

We’ve developed smart solutions to address these challenges:

1. Efficient Deployment: We use model compression techniques like distillation and quantization to reduce the computational load, making our LLMs more accessible without compromising performance.
2. Improving Accuracy: By integrating Retrieval-Augmented Generation (RAG), we enhance LLM outputs with relevant data from external knowledge sources, ensuring more precise and reliable results.
3. Fine-Tuning on Healthcare Data: Our models are fine-tuned on diverse, healthcare-specific datasets, optimizing their performance and accuracy in clinical settings.
4. Data Privacy: We prioritize patient privacy with techniques like differential privacy and de-identification using models like ClinicalBERT, ensuring data security while maintaining the ability to train on valuable healthcare data.

In future articles, we’ll dive deeper into each of these solutions, showing how Cognome is tackling the limitations of LLMs and pushing the boundaries of healthcare technology.