Key Insights: Designed a PoC for an NLP-based platform to summarize medical documents, targeting reductions in routine work that can take 70% of a practitioner’s time and 30% of healthcare costs.

About the Client

A technology company focused on developing AI platforms for healthcare organizations, aiming to streamline processes and improve access to critical medical information.

The Challenge

The client wanted to build a proof-of-concept platform capable of processing medical papers and generating concise, accurate summaries.

Key challenges included:

• Limited availability of datasets for training and evaluation.
  
  
• Difficulty in establishing objective metrics to benchmark model performance.
  
  
• Need for medical subject matter experts (SMEs) to extend the dataset and validate results.
  

Marvik’s Approach

We began with a product discovery phase to assess feasibility, focusing on:

• Understanding achievable results given the current state of the art.
  
  
• Producing an early-stage prototype of the platform.
  
  
• Compiling a product backlog and defining dataset requirements.
  
  
• Designing an architecture for an MVP that included:
  
  
  • An NLP pipeline to extract key messages from core medical dossiers.
    
    
  • An API and frontend with a human-in-the-loop approach to refine automatic processing.
    
    

The Results & Impact

• Delivered an architecture and prototype to accelerate MVP development.
  
  
• Outlined a clear dataset strategy and role for SMEs in validating outputs.
  
  
• Addressed a problem where AI can significantly reduce the 70% of time practitioners spend on administrative tasks and the 30% of healthcare costs tied to them.
  
  

Why This Matters

By applying NLP to medical literature, healthcare professionals can shift focus from reading and processing to acting on critical information, improving efficiency, decision-making, and patient outcomes.