Amazon technologies, inc. (20240331821). MEDICAL CONVERSATIONAL INTELLIGENCE simplified abstract

MEDICAL CONVERSATIONAL INTELLIGENCE

Organization Name

amazon technologies, inc.

Inventor(s)

Vijit Gupta of Mercer Island WA (US)

Matthew Chih-Hui Chiou of Seattle WA (US)

Amiya Kishor Chakraborty of Seattle WA (US)

Anuroop Arora of Seattle WA (US)

Varun Sembium Varadarajan of Redmond WA (US)

Sarthak Handa of Seattle WA (US)

Amit Vithal Sawant of New Brunswick NJ (US)

Glen Herschel Carpenter of Arvada CO (US)

Jesse Deng of Seattle WA (US)

Mohit Narendra Gupta of Seattle WA (US)

Rohil Bhattarai of Seattle WA (US)

Samuel Benjamin Schiff of New York NY (US)

Shane Michael Mcgookey of Seattle WA (US)

Tianze Zhang of Long Island City NY (US)

MEDICAL CONVERSATIONAL INTELLIGENCE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240331821 titled 'MEDICAL CONVERSATIONAL INTELLIGENCE

Simplified Explanation: The patent application describes systems and methods for summarizing medical audio conversations using machine learning models and natural language processing.

• **Key Features and Innovation:**

   - Utilizes a transcription machine learning model to generate a transcript of medical conversations.
   - Employs natural language processing to create a summary of the transcript.
   - Includes machine learning models to identify medical entities, speaker roles, sections of the transcript, and extract phrases for the summary.

• **Potential Applications:**

   - Medical record keeping and documentation.
   - Improving communication between healthcare professionals.
   - Enhancing patient care by providing concise summaries of medical conversations.

• **Problems Solved:**

   - Streamlining the process of summarizing medical audio conversations.
   - Facilitating information retrieval and analysis in healthcare settings.
   - Improving the efficiency of medical documentation.

• **Benefits:**

   - Saves time for healthcare professionals.
   - Enhances accuracy and completeness of medical summaries.
   - Improves overall communication and collaboration in healthcare settings.

• **Commercial Applications:**

   - Title: "Medical Audio Summarization Technology for Healthcare Communication"
   - This technology can be utilized by healthcare institutions, telemedicine companies, and medical transcription services to streamline documentation processes and improve patient care.

• **Prior Art:**

   - While not explicitly mentioned in the abstract, prior art in the field of medical transcription, natural language processing, and machine learning can be explored to understand the existing technologies and innovations in this area.

• **Frequently Updated Research:**

   - Stay updated on advancements in machine learning models for transcription and natural language processing in healthcare applications.

Questions about Medical Audio Summarization Technology:

1. What are the potential privacy concerns associated with using machine learning models to summarize medical conversations?

   - Answer: Privacy concerns may arise due to the sensitive nature of medical information shared in conversations. Implementing robust data security measures and ensuring compliance with healthcare regulations can mitigate these risks.

2. How can this technology be integrated with existing electronic health record systems to improve data management in healthcare settings?

   - Answer: Integration with EHR systems can streamline the process of updating patient records with accurate and concise summaries of medical conversations, enhancing the overall efficiency of healthcare operations.

Original Abstract Submitted

systems and methods for performing medical audio summarizing for medical conversations are disclosed. an audio file and meta data for a medical conversation are provided to a medical audio summarization system. a transcription machine learning model is used by the medical audio summarization system to generate a transcript and a natural language processing service of the medical audio summarization system is used to generate a summary of the transcript. the natural language processing service may include at least four machine learning models that identify medical entities in the transcript, identify speaker roles in the transcript, determine sections of the transcript corresponding to the summary, and extract or abstract phrases for the summary. the identified medical entities and speaker roles, determined sections, and extracted or abstracted phrases may then be used to generate the summary.