Patent ID: 11862302
Assignee: TELADOC HEALTH, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system for automatically generating, by one or more processors, a structured medical note during a remote medical consultation, the system comprising:
a provider tele-presence device in the vicinity of a medical provider and coupled to a communication network, the provider tele-presence device configured to provide two-way audio communication between the medical provider and a patient in the vicinity of a patient tele-presence device, wherein the provider tele-presence device is further configured to display, to the medical provider, information from a medical record of the patient and at least one the of the provider tele-presence device and the patient tele-presence device is to record audio data from communication between the medical provider and the patient;
a machine learning network implemented on the one or more processors receiving the patient's medical record and recorded audio data from the provider tele-presence device, the machine learning network configured to:
distinguish between speech of the medical provider and speech of the patient and remove other voices to obtain patient audio data and medical provider audio data, respectively,
electronically transcribe at least the speech of the medical provider from the medical provider audio data,
electronically derive patient audio meta-data from the patient audio data, the patient audio meta-data including one or more of slurring in patient speech and a pause duration between a medical provider question and a start of a patient answer; and
automatically generate the structured medical note including at least a portion of the medical record, at least a portion of the transcribed speech, and at least a portion of the patient audio meta-data, wherein the structured medical note is stored in a medical documentation server in association with an identity of the patient and includes at least a first field for subjective information, a second field for objective information, a third field for assessment information, and a forth field for treatment plan information; and

a feedback system implemented on the one or more processors configured to receive medical provider corrections to the automatically generated structured medical note and train the machine learning network based on the medical provider corrections.