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Automated the medical coding process to increase the productivity by 40% and achieve the accuracy rates of 95-98%


Client Background

The client is a leading healthcare technology company based out of the US.


  • The client needed to automate the manual coding process to have more consistent and accurate results
  • The client needed to extract data from unstructured and non-standard document formats, including paper, fax, and digital
  • The client also needed to perform a sentiment analysis of the data captured from the patient medical charts
  • Manual review of patient medical charts was a tedious process and prone to errors


  • Used Optical Character Recognition to extract data from the scanned medical charts and pulled out relevant medical information
  • Integrated multiple data sources to automate the query process
  • Created an automatic rule-based priority queuing mechanism using NLP
  • Ran the extracted medical data through the NLP engine and captured the sentiment of a particular record by:
    • Forming a semantic space word bank for each disease containing a set of important medical keywords that determine the presence of a disease
    • Training the custom models using this word bank

Tools & Technologies

Python, Open NLP, Leadtools

Key benefits

  • Improved process productivity by 40%
  • Increased output rate (15-16 charts per day) using NLP compared to the manual coding rate (10-11 charts per day)
  • Provided accuracy rates of 95-98% in identifying medical conditions
  • Reduced overall administration costs
  • Easy access to information expedites care for those in need (patient & doctor)
Key Benefits of Automated Medical Chart Review



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