Cascade Editor

Machine Learning


Automated Medical Transcription

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  • The application automates the entire medical transcription process.
  • Doctors are allowed to dictate notes about a patient encounter using a mobile application.
  • The dictated notes get transcribed to text using speech to text solutions that utilize machine learning.
  • Transcribed text gets submitted as a task to a queue where an authorized transcriptionist can pick up the task and modify the textual content if any.
  • Automated coding and annotation are performed on the corrected content. Codes such as ICD-10 get applied and added to the EMR against the respective fields.
  • Relevant information is extracted from the text and made available in a structured fashion to the EMR.
  • Once the transcriptionist submits the corrected case, the changes sync back to the EMR.


  • The dictation module is a native iOS application.
  • Integration with OpenEMR, OpenMRS, Bahmni through SDKs and APIs.
  • Speech to text was done using customized algorithms plugged into the Kaldi engine. This was important to enable good speech to text for medical terminologies.
  • A Queuing mechanism was set up using a relational database for persistence.
  • Workforce module was developed using Node.JS
  • User management was done using Passport.JS and has support for LDAP.
  • ML Components to extract vitals was primarily done using a custom word embeddings model that consumed dependency trees. Also, external ontologies such as SNOMED-CT, RXNorm etc were integrated into this algorithm and this was used to annotate and enrich the text.
  • Java 8
  • PostgreSQL
  • StanfordNLP Universal Dependencies
  • Node.JS with Express
  • Passport.JS
  • Docker
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