Cascade Editor

Machine Learning

NLP

Automated Medical Transcription

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.