Method and system for automatically generating template-based patient notes

A system for automatically generating template-based patient notes, the system comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which when executed by the processor, cause the processor to:
• receive patient data from Electronic Medical Records (EMR) of the patient and an audio input corresponding to a patient from a healthcare provider;
• generate primary insights text data based on the audio input through a speech-to-text conversion model or an autoregressive transformer model, wherein the primary insights text data comprise a plurality of unique natural language sentences;
• assign a primary category to each of the plurality of unique natural language sentences in the primary insights text data through a set of Machine Learning (ML) models, wherein the set of ML models is based on a Natural Language Processing (NLP) technique;
• assign at least one secondary category associated with the primary category to each of the plurality of unique natural language sentences in the primary insights text data through the set of ML models;
• and
• generate a final note corresponding to the patient comprising a combination of the assigned primary category, the at least one secondary category, the primary insights text data, and the patient data, based on a note template.

A non-transitory computer-readable medium storing computer-executable instructions for automatically generating template-based patient notes, the computer-executable instructions configured for:

• receiving, by a note generation device, patient data from Electronic Medical Records (EMR) of the patient and an audio input corresponding to a patient from a healthcare provider;
• generating, by the note generation device, primary insights text data based on the audio input through a speech-to-text conversion model or an autoregressive transformer model, wherein the primary insights text data comprise a plurality of unique natural language sentences;
• assigning, by the note generation device, a primary category to each of the plurality of unique natural language sentences in the primary insights text data through a set of Machine Learning (ML) models, wherein the set of ML models is based on a Natural Language Processing (NLP) technique;
• assigning, by the note generation device, at least one secondary category associated with the primary category to each of the plurality of unique natural language sentences in the primary insights text data through the set of ML models, that leverages Natural Language Processing techniques;
• and
• generating, by the note generation device, a final note corresponding to the patient comprising a combination of the assigned primary category, the at least one secondary category, the primary insights text data, and the patient data, based on a note template.