Interface for patient-provider conversation and auto-generation of note or summary
A computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene.
1. A machine learning based method for automatically processing a note based on a conversation between a patient and a healthcare provider, comprising:
displaying, by a computing device, a note summarizing the conversation, wherein the note comprises automatically extracted words or phrases in a transcript of an audio recording of the conversation, wherein the extracted words or phrases relate to one or more medical topics relating to the patient, and wherein the extraction of the words or phrases is performed by one or more trained machine learning models;
generating, by the one or more trained machine learning models, a billing code associated with a given medical topic of the one or more medical topics, wherein the billing code is based on the automatically extracted words or phrases; and
displaying, by the computing device, the billing code alongside a portion of the note that includes the extracted words or phrases.
2. The method of claim 1 , further comprising:
displaying, by the computing device, a textual description to support the billing code, wherein the textual description is based on the transcript and the billing code.
3. The method of claim 1 , wherein the generating of the billing code comprises:
providing information related to a medical symptom by providing one or more labels for phrases associated with the billing code.
4. The method of claim 1 , wherein the one or more trained machine learning models comprises a clinical decision support model.
5. The method of claim 1 , further comprising:
displaying, by the computing device, the transcript of the recording in substantial real time with the rendering of the audio recording.
6. The method of claim 1 , wherein the note comprises one or more classifiers associated with the billing code, and wherein the one or more classifiers is indicative of a medical attribute associated with the billing code.
7. The method of claim 1 , wherein the displaying of the billing code and the textual description further comprises:
receiving, via the display of the workstation, an indication to toggle to a billing tab displayed by the display of the workstation; and
displaying, in response to the indication to toggle to the billing tab, the billing code and the textual description in a display associated with the billing tab.
8. The method of claim 1 , wherein the one or more trained machine learning models comprises a named entity recognition model, and the training of the named entity recognition model comprises supervised learning based on labeled speech data to recognize the extracted words or phrases.
9. The method of claim 8 , wherein the training of the named entity recognition model is further based on a corpus of medical textbooks using deep learning word embedding, a lexicon of medical ontologies, and a systematized nomenclature of medicine (SNOMED).
10. The method of claim 8 , wherein the training of the named entity recognition model is further based on annotated transcripts from doctor-patient conversations.
11. The method of claim 1 , further comprising:
providing, by the computing device, a minimization tool for minimizing a region of the display where the transcript is displayed to toggle to a note-only view.
12. The method of claim 1 , wherein the transcript and the note are editable.
13. The method of claim 1 , further comprising:
displaying, by the computing device, a minimized icon on a display of an electronic health record screen which, when activated, toggles to the audio recording of the conversation and the transcript thereof.
14. The method of claim 1 , further comprising:
automatically generating suggestions of alternative words or phrases for words or phrases in the transcript; and
providing one or more tools to approve, reject or provide feedback on the generated suggestions to thereby edit the transcript.
15. The method of claim 1 , further comprising:
generating at least one of a suggestion of a topic to follow-up with the patient, and a suggestion of a clinical problem; and
displaying, by the computing device, the at least one of the suggestion of the topic, and the suggestion of the clinical problems.
16. The method of claim 1 , further comprising:
identifying an inaudible word or phrase in the audio recording of the conversation;
automatically generating a suggestion of one or more alternative words or phrases to replace the inaudible word or phrase; and
displaying, by the computing device, the one or more alternative words or phrases to replace the inaudible word or phrase.
17. The method of claim 16 , further comprising:
generating a confidence score associated with the suggestion of the one or more alternative words or phrases, and
wherein the displaying of the one or more alternative words or phrases comprises displaying the associated confidence score.
18. The method of claim 1 , further comprising:
determining, from the audio recording, an incorrect or incomplete mention of a medication by the patient;
automatically generating a suggestion of one or more alternative medications to replace the incorrect or incomplete mention of the medication; and
displaying, by the computing device, the one or more alternative medications.
19. A computing device, comprising:
one or more processors; and
data storage, wherein the data storage has stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing device to carry out operations comprising:
displaying a note summarizing the conversation, wherein the note comprises automatically extracted words or phrases in a transcript of an audio recording of the conversation, wherein the extracted words or phrases relate to one or more medical topics relating to the patient, and wherein the extraction of the words or phrases is performed by one or more trained machine learning models;
generating, by the one or more trained machine learning models, a billing code associated with a given medical topic of the one or more medical topics, wherein the billing code is based on the automatically extracted words or phrases; and
displaying the billing code alongside a portion of the note that includes the extracted words or phrases.
20. An article of manufacture comprising one or more non-statutory computer readable media having computer-readable instructions stored thereon that, when executed by one or more processors of a computing device, cause the computing device to carry out operations comprising:
displaying a note summarizing the conversation, wherein the note comprises automatically extracted words or phrases in a transcript of an audio recording of the conversation, wherein the extracted words or phrases relate to one or more medical topics relating to the patient, and wherein the extraction of the words or phrases is performed by one or more trained machine learning models;
generating, by the one or more trained machine learning models, a billing code associated with a given medical topic of the one or more medical topics, wherein the billing code is based on the automatically extracted words or phrases; and
displaying the billing code alongside a portion of the note that includes the extracted words or phrases.