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Custom Healthcare LLM for Clinical Knowledge Retrieval

Custom Healthcare LLM for Clinical Knowledge Retrieval

A large hospital chain based in Boston, USA, managed thousands of medical records, diagnostic guidelines, and treatment protocols across multiple departments. Clinicians struggled to quickly retrieve precise medical information during consultations due to fragmented document repositories. The organization partnered with our AI engineering team to build a custom-trained healthcare LLM capable of delivering accurate, clinician-friendly summaries and treatment insights in seconds.

Image representing case study

92%

Improvement in retrieval accuracy

5X

Faster access to treatment guidelines

73%

Reduction in manual document lookup

About  

Problem Statement

  • Clinical staff spent excessive time searching across PDFs, EHR notes, and medical manuals.
  • No unified knowledge retrieval system existed for diagnosis, dosage references, or care pathways.
  • Traditional search engines returned irrelevant or overly generic medical results.
  • Clinicians needed concise, high-precision insights instead of long documents.
  • The system had to meet strict compliance and data-handling standards (HIPAA).

Industry: Healthcare

Services: Custom LLM Development, Clinical AI, Knowledge Retrieval

Region: United States

Our Tech Stack

Tech stack we used

Solution Approach

  • Collected and normalized clinical guidelines, medical textbooks, discharge summaries, and structured FHIR-compliant patient data.
  • Fine-tuned a LLaMA-3 medical variant using PyTorch with 28 specialty-specific datasets (cardiology, pediatrics, emergency care).
  • Implemented a retrieval-augmented generation (RAG) pipeline using FAISS to deliver context-grounded answers.
  • Integrated the model with the hospital’s FHIR API, enabling patient-specific query responses (labs, vitals, prior diagnosis).
  • Built a secure inference environment on Azure Confidential Computing for HIPAA compliance.
  • Added hallucination-reduction scoring and clinical-validation rules for treatment instructions.
  • Created a doctor-friendly UI that returned structured summaries: symptoms, risks, recommended investigations, and treatment options.

Benefits

  • Clinicians received reliable answers in seconds instead of manually searching multiple sources.
  • Delivered 92% improvement in accuracy over generic medical search tools.
  • Enabled fast summarization of lengthy clinical notes and EHR histories.
  • Reduced cognitive load for medical teams during high-pressure consultations.
  • Improved care consistency across departments with standardized guidance.

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