Using Large Language Models to Obtain Insights from Patient’s Medical Records

using large language models to obtain insights from patients medical records

The emergence of large language models (LLM) stems from the need of healthcare providers to make swift and efficient clinical decisions. Doctors need up-to-date patient data for accurate diagnoses, effective treatment planning, and improved outcomes. 

Advancements in healthcare technology like AI, machine learning, and large language models are propelling us into a new era that emphasizes a data-driven, highly personalized, and targeted care experience. Traditional data analysis and manual interpretation may struggle to keep pace with rapid medical breakthroughs, potentially diminishing care quality. 

But how can providers quickly and accurately process this new wealth of data? 

They can embrace innovative large language models that redefine how healthcare providers can engage with patient information for superior care and outcomes.

Challenges and Solutions for Deriving Care Insight from Healthcare Data

Challenges-and-Solutions-for-Deriving-Care-Insight-from-Healthcare-Data

 

Every healthcare record links to an enormous amount of data. For example: FHIR LLM, a healthcare data exchange standard, uses complex data formats such as JSON and XML. But these formats don’t connect, making it challenging for healthcare professionals and chatbots to interpret data effectively.

Ensuring accuracy in medical records requires correct terminology, careful documentation, close attention to detail, and data privacy and security. 

Several steps help providers analyze and understand vast patient datasets:

  • Efficiently access patient medical records, quickly retrieve relevant information, and provide a personalized response for each user.
  • Provide accurate information about the patient.
  • Demonstrate medical knowledge and be able to deliver well-informed and articulate responses.
  • Integrate with existing systems to ensure a streamlined workflow for healthcare professionals and minimize disruptions in operations.

The Technology Behind & Beyond Healthcare Large Language Models

The-Technology-Behind-Beyond-Healthcare-Large-Language-Models

 

Most research about extracting patient insights revolves around natural language processing (NLP). KMS Healthcare adopts a novel approach that couples the power of large language models with other technologies.

Innovative Approach: REALM and RAGs

The KMS Healthcare chatbot approach draws inspiration from the retrieval-augmented language model pre-training (REALM), applied to healthcare.

This model includes integrating retrieval augmentation generations (RAGs). These elements combine domain-specific knowledge using advanced language model frameworks such as Langchain and Llama-Index. RAGs help chatbots understand healthcare language better.

REALM and RAGs make the KMS Healthcare chatbot a dynamic repository of healthcare intelligence with unmatched precision and depth.

Automated Question-Answering System

An automated question-answering system in the KMS Healthcare chatbot improves patient care and outcomes for healthcare professionals:

  1. Efficient Patient History Retrieval:

The KMS Healthcare system retrieves and processes patient history records using the CONNECT API. This transforms complex data into an easy-to-understand format, addressing token limit issues and making it accessible for healthcare professionals.

  1. Advanced Information Retrieval:

Integration with the Llama Index and Langchain improves information retrieval, providing the chatbot with a vast knowledge base for more efficient and comprehensive healthcare decision-making.

  1. Automated Evaluation Process:

An automated evaluation process checks the accuracy and reliability of system responses against reference documents. This ensures that the chatbot always provides trustworthy information for exceptional healthcare decision-making.

Technology Advantages of the KMS Healthcare Chatbot Approach

KMS Healthcare has comprehensively researched large language models to pinpoint patient record insights that will enhance healthcare data management for providers and software vendors.

An LLM chatbot can analyze patient data and provide customized and relevant information for doctors. 

Chatbot integration with Microsoft Azure helps meet strict compliance with HIPAA data privacy regulations in a trusted cloud environment.

These chatbot solutions connect with industry-leading systems like Epic and Cerner to streamline access to patient records and promote a unified healthcare workflow. LLMs quickly analyze complex patient records, offering healthcare professionals rapid, accurate, and personalized insights for more effective patient consultations. 

Research: Healthcare Chatbots Using Large Language Models to Extract Insights From Patient Records

KMS Healthcare has published research that explains how to decode complex patient records using a large language model in healthcare, delivering quick and accurate insights.

An example of patient history information for doctors’ access

Learn how technology integrates patient medical records from various healthcare providers to give doctors a complete understanding of patient health history, including family background, past medications, allergies, and lab results. 

Demo of patient information inquiry

Demo of patient information inquiry

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