Abstract: An automated system updates electronic medical records (EMRs) based on dictated reports, without requiring manual data entry into on-screen forms. A dictated report is transcribed by an automatic speech recognizer, and facts are extracted from the report and stored in encoded form. Information from a patient's report is also stored in encoded form. The resulting encoded information from the report and EMR are reconciled with each other, and changes to be made to the EMR are identified based on the reconciliation. The identified changes are made to the EMR automatically, without requiring manual data entry into the EMR.

Abstract: A computer system and method identifies topics in conversations, such as a conversation between a doctor and patient during a medical examination. The system and method generates, based on first text (such as a document corpus including previous clinical documentation), a plurality of sentence embeddings representing a plurality of semantic representations in a plurality of sentences in the training text. The system and method generate a classifier based on the second text, which includes a plurality of sections associated with a plurality of topics, and the plurality of sentence embeddings. The system and method generate, based on a sentence (such as a sentence in a doctor-patient conversation) and the classifier, an identifier of a topic to associate with the first sentence. The system and method may also insert the sentence into a section, associated with the identified topic, in a document (such as a clinical note).

Abstract: A method and system for tracking user attention is disclosed. The method and system receives, by an attention tracking component, a non-audio input from a user. The method and system receives, by a speech processing component, audio input from the user. The method and system determines, by a results processing component, that the attention tracking component received the non-audio input within a first time interval proximate to a second time interval during which the audio input was received. The method and system performs, by the results processing component, at least one actionable command identified within the audio input, based upon the determination that the attention tracking component received the non-audio input within the first time interval and a determination hat the received audio input includes the at least one actionable command.

Abstract: A computer system and method transcribe a spoken dialogue, such as a dialogue between a physician and a patient, into a document, such as a clinical note. As the document is generated, if content is detected in the dialog which corresponds to a content template, the content template is inserted into the document. Fields in the content template may also be filled using information from the dialog and/or information external to the dialog.

Abstract: An automated system updates electronic medical records (EMRs) based on dictated reports, without requiring manual data entry into on-screen forms. A dictated report is transcribed by an automatic speech recognizer, and facts are extracted from the report and stored in encoded form. Information from a patient's report is also stored in encoded form. The resulting encoded information from the report and EMR are reconciled with each other, and changes to be made to the EMR are identified based on the reconciliation. The identified changes are made to the EMR automatically, without requiring manual data entry into the EMR.

Abstract: An automated system updates electronic medical records (EMRs) based on dictated reports, without requiring manual data entry into on-screen forms. A dictated report is transcribed by an automatic speech recognizer, and facts are extracted from the report and stored in encoded form. Information from a patient's report is also stored in encoded form. The resulting encoded information from the report and EMR are reconciled with each other, and changes to be made to the EMR are identified based on the reconciliation. The identified changes are made to the EMR automatially, without requiring manual data entry into the EMR.

Abstract: A computerized system processes the speech of a physician and a patient during a patient encounter to automatically produce a draft clinical report which documents the patient encounter. The draft clinical report includes information that has been abstracted from the speech of the physician and patient. The draft report is provided to the physician for review. Producing the draft clinical report automatically, rather than requiring the physician to prepare the draft clinical report manually, significantly reduces the time required by the physician to produce the final version of the clinical report.

Abstract: A computer system increases the efficiency with which billing codes may be generated based on a chart, such as a medical chart. The computer system provides the chart to a computer-assisted coding (CAC) module, which produces an initial set of billing codes and an initial assessment of the accuracy and/or completeness of the codes. The computer system decides whether to send the initial set of billing codes to an initial human reviewer. If the computer system sends the initial set of billing codes to the initial human reviewer, then the initial human reviewer reviews the chart and the output of the CAC module, and attempts to fix errors in the CAC output. The system provides the chart and the current (initial or modified) codes to a final human reviewer, who may be more highly skilled than the initial human reviewer, for final verification and modification.

Abstract: A system includes a data record (such as an Electronic Medical Record (EMR)) and a user interface for modifying (e.g., storing data in) the data record. The data record includes both free-form text elements and discrete data elements. The user interface includes user interface elements for receiving free-form text data. In response to receiving free-form text data via the free-form text user interface elements, a suggested action is identified, such as a suggested action to take in connection with one of the discrete data elements of the data record. Output is generated representing the suggested action. A user provides input indicating acceptance or rejection of the suggested action. The suggested action may be performed in response to the user input.

Abstract: A computer system automatically generates a first diagnosis of a patient based on input such as one or more medical images. The computer system receives input representing diagnostic intent from a human user. The system determines whether to provide the first diagnosis to the human user based on the first diagnosis and the diagnostic intent, such as by determining whether the first diagnosis and the diagnostic intent agree with each other, and only providing the first diagnosis to the human user if the first diagnosis disagrees with the diagnostic intent of the human user.

Abstract: Inputs provided into user interface elements of an application are observed. Records are made of the inputs and the state(s) the application was in while the inputs were provided. For each state, a corresponding language model is trained based on the input(s) provided to the application while the application was in that state. When the application is next observed to be in a previously-observed state, a language model associated with the application's current state is applied to recognize speech input provided by a user and thereby to generate speech recognition output that is provided to the application. An application's state at a particular time may include the user interface element(s) that are displayed and/or in focus at that time, and is determined by an operating system hooking component embedded in the automatic speech recognition system.

Abstract: Speech is transcribed to produce a transcript. At least some of the text in the transcript is encoded as data. These codings may be verified for accuracy and corrected if inaccurate. The resulting transcript is provided to a decision support system to perform functions such as checking for drug-drug, drug-allergy, and drug-procedure interactions, and checking against clinical performance measures (such as recommended treatments). Alerts and other information output by the decision support system are associated with the transcript. The transcript and associated decision support output are provided to a physician to assist the physician in reviewing the transcript and in taking any appropriate action in response to the transcript.

Abstract: A method for selective modification to one of a plurality of components includes receiving, by an engine, a draft transcript including at least one concept content. The method includes accessing, by a first component in a plurality of components executed by the engine, a mapping between content data and codes to identify a code mapped to the at least one concept content. The method includes modifying the draft transcript to include the identified code. The method includes receiving input representing a status of the identified code. The method includes accessing a data structure storing an indication that the first component identified the code. The method includes modifying a reliability score for the first component. The method includes determining that the first component has a reliability score that fails to satisfy a predetermined threshold. The method includes modifying execution of the first component, based on the determination.

Abstract: Inputs provided into user interface elements of an application are observed. Records are made of the inputs and the state(s) the application was in while the inputs were provided. For each state, a corresponding language model is trained based on the input(s) provided to the application while the application was in that state. When the application is next observed to be in a previously-observed state, a language model associated with the application's current state is applied to recognize speech input provided by a user and thereby to generate speech recognition output that is provided to the application. An application's state at a particular time may include the user interface element(s) that are displayed and/or in focus at that time, and is determined by an operating system hooking component embedded in the automatic speech recognition system.

Abstract: A system includes a data record (such as an Electronic Medical Record (EMR)) and a user interface for modifying (e.g., storing data in) the data record. The data record includes both free-form text elements and discrete data elements. The user interface includes user interface elements for receiving free-form text data. In response to receiving free-form text data via the free-form text user interface elements, a suggested action is identified, such as a suggested action to take in connection with one of the discrete data elements of the data record. Output is generated representing the suggested action. A user provides input indicating acceptance or rejection of the suggested action. The suggested action may be performed in response to the user input.

Abstract: A system includes a data record (such as an Electronic Medical Record (EMR)) and a user interface for modifying (e.g., storing data in) the data record. The data record includes both free-form text elements and discrete data elements. The user interface includes user interface elements for receiving free-form text data. In response to receiving free-form text data via the free-form text user interface elements, a suggested action is identified, such as a suggested action to take in connection with one of the discrete data elements of the data record. Output is generated representing the suggested action. A user provides input indicating acceptance or rejection of the suggested action. The suggested action may be performed in response to the user input.

Abstract: An automatic speech recognizer is used to produce a structured document representing the contents of human speech. A best practice is applied to the structured document to produce a conclusion, such as a conclusion that required information is missing from the structured document. Content is inserted into the structured document based on the conclusion, thereby producing a modified document. The inserted content may be obtained by prompting a human user for the content and receiving input representing the content from the human user.

Abstract: Inputs provided into user interface elements of an application are observed. Records are made of the inputs and the state(s) the application was in while the inputs were provided. For each state, a corresponding language model is trained based on the input(s) provided to the application while the application was in that state. When the application is next observed to be in a previously-observed state, a language model associated with the application's current state is applied to recognize speech input provided by a user and thereby to generate speech recognition output that is provided to the application. An application's state at a particular time may include the user interface element(s) that are displayed and/or in focus at that time, and is determined by an operating system hooking component embedded in the automatic speech recognition system.

Abstract: Inputs provided into user interface elements of an application are observed. Records are made of the inputs and the state(s) the application was in while the inputs were provided. For each state, a corresponding language model is trained based on the input(s) provided to the application while the application was in that state. When the application is next observed to be in a previously-observed state, a language model associated with the application's current state is applied to recognize speech input provided by a user and thereby to generate speech recognition output that is provided to the application. An application's state at a particular time may include the user interface element(s) that are displayed and/or in focus at that time, and is determined by an operating system hooking component embedded in the automatic speech recognition system.

Abstract: A computerized system processes the speech of a physician and a patient during a patient encounter to automatically produce a draft clinical report which documents the patient encounter. The draft clinical report includes information that has been abstracted from the speech of the physician and patient. The draft report is provided to the physician for review. Producing the draft clinical report automatically, rather than requiring the physician to prepare the draft clinical report manually, significantly reduces the time required by the physician to produce the final version of the clinical report.