Abstract: A machine learning system may be used to suggest clinical questions to ask during or after a patient appointment. A first encoder may encode information and a second encoder may encode second information related to the current patient appointment. An aggregate encoding may be generated using the encoded first information and encoded second information. The current patient appointment may be clustered with similar appointments based on the aggregate encoding. Outlier analysis may be performed to determine if the appointment is an outlier, and, if so, which features contribute the most to outlier status. The system may generate one or more questions to ask about the features that contribute the most to outlier status.

Abstract: A computer-implemented method and apparatus for development and use of a medical form. One example system receives a request on a first computer to create the medical form. The medical form includes user selected fields corresponding to patient information. The system further receives signals on the first computer, where the signals describe form fields for the medical form. The system communicates the medical form from the first computer to the first tablet device for display on the first tablet device. The system receives form values, wherein at least some of the form values correspond to information about an appointment. Responsive to receiving a completion signal, the system generates a clinical narrative for the appointment using the completed form values.

Abstract: A machine learning system may be used to predict clinical questions to ask on a clinical form. A first encoder may encode first information and a second encoder may encoder second information from a medical record of a past appointment. The first and second encoded information and additional encoded information may be used to predict a clinical question to ask by using a reinforcement learning system. The reinforcement learning system may be trained by receiving ratings of questions from users.

Abstract: A computer system may parse a set of medical events of a patient and determine when the patient has been taking a first medication and a second medication. The computer system may determine the duration of time in which the patient has been taking the first medication. An expected duration of time for the course of treatment may be provided. When it is determined that the actual course of treatment differed from the expected duration of treatment, then the system may flag a potential drug interaction. When enough of these flags are determined, an indication of a potential drug interaction may be stored and a prompt or notification sent to other health practitioners about the potential drug interaction.

Abstract: A computer-implemented method and apparatus for development and use of a medical form. One example system receives a request on a first computer to create the medical form. The medical form includes user selected fields corresponding to patient information. The system further receives signals on the first computer, where the signals describe form fields for the medical form. The system communicates the medical form from the first computer to the first tablet device for display on the first tablet device. The system receives form values, wherein at least some of the form values correspond to information about an appointment. Responsive to receiving a completion signal, the system generates a clinical narrative for the appointment using the completed form values.

Abstract: A computer-implemented method and apparatus for development and use of a medical form. One example system receives a request on a first computer to create the medical form. The medical form includes user selected fields corresponding to patient information. The system further receives signals on the first computer, where the signals describe form fields for the medical form. The system communicates the medical form from the first computer to the first tablet device for display on the first tablet device. The system receives form values, wherein at least some of the form values correspond to information about an appointment. Responsive to receiving a completion signal, the system generates a clinical narrative for the appointment using the completed form values.

Abstract: A machine learning system may be used to predict clinical questions to ask on a clinical form. A first encoder may encode first information and a second encoder may encoder second information from a medical record of a past appointment. The first and second encoded information and additional encoded information may be used to predict a clinical question to ask by using a reinforcement learning system. The reinforcement learning system may be trained by receiving ratings of questions from users.

Abstract: A machine learning system may be trained to assist physicians with claims by automatically adjusting the claims to make them more likely to be accepted by a payer or by outputting a predicted probability that the claim will be accepted. The machine learning system may use one or more encoders that encode codes, clinical notes, and claims into separate vector spaces, where the vector spaces relate similar entities. The encoded codes, clinical notes, and claims may be decoded by a decoder to predict codes comprising an adjusted claim. Alternatively, the decoder may output a predicted probability that the claim will be accepted for payment. The encoders and the decoder may be machine learning models that are trained using ground-truth training examples.

Abstract: A machine learning system may be trained to predict codes for a physician code schedule. The machine learning system may use one or more encoders that encode codes, code schedules, and claims into separate vector spaces, where the vector spaces relate similar entities. The encoded codes, code schedules, and claims may be decoded by a decoder to predict a code to add to the code schedule. The encoders and the decoder may be machine learning models that are trained using ground-truth training examples.

Abstract: A machine learning system may be used to suggest clinical questions to ask during or after a patient appointment. A first encoder may encode information and a second encoder may encode second information related to the current patient appointment. An aggregate encoding may be generated using the encoded first information and encoded second information. The current patient appointment may be clustered with similar appointments based on the aggregate encoding. Outlier analysis may be performed to determine if the appointment is an outlier, and, if so, which features contribute the most to outlier status. The system may generate one or more questions to ask about the features that contribute the most to outlier status.

Abstract: A computer system may parse a set of medical events of a patient and determine when the patient has been taking a first medication and a second medication. The computer system may determine the duration of time in which the patient has been taking the first medication. An expected duration of time for the course of treatment may be provided. When it is determined that the actual course of treatment differed from the expected duration of treatment, then the system may flag a potential drug interaction. When enough of these flags are determined, an indication of a potential drug interaction may be stored and a prompt or notification sent to other health practitioners about the potential drug interaction.

Abstract: A computer-implemented method and apparatus for development and use of a medical form. One example system receives a request on a first computer to create the medical form. The medical form includes user selected fields corresponding to patient information. The system further receives signals on the first computer, where the signals describe form fields for the medical form. The system communicates the medical form from the first computer to the first tablet device for display on the first tablet device. The system receives form values, wherein at least some of the form values correspond to information about an appointment. Responsive to receiving a completion signal, the system generates a clinical narrative for the appointment using the completed form values.

Abstract: A computer-implemented method and apparatus for development and use of a medical form. One example system receives a request on a first computer to create the medical form. The medical form includes user selected fields corresponding to patient information. The system further receives signals on the first computer, where the signals describe form fields for the medical form. The system communicates the medical form from the first computer to the first tablet device for display on the first tablet device. The system receives form values, wherein at least some of the form values correspond to information about an appointment. Responsive to receiving a completion signal, the system generates a clinical narrative for the appointment using the completed form values.

Abstract: A computer-implemented method and apparatus for development and use of a medical form. One example system receives a request on a first computer to create the medical form. The medical form includes user selected fields corresponding to patient information. The system further receives signals on the first computer, where the signals describe form fields for the medical form. The system communicates the medical form from the first computer to the first tablet device for display on the first tablet device. The system receives form values, wherein at least some of the form values correspond to information about an appointment. Responsive to receiving a completion signal, the system generates a clinical narrative for the appointment using the completed form values.

Abstract: A computer-implemented method and apparatus for development and use of a medical form. One example system receives a request on a first computer to create the medical form. The medical form includes user selected fields corresponding to patient information. The system further receives signals on the first computer, where the signals describe form fields for the medical form. The system communicates the medical form from the first computer to the first tablet device for display on the first tablet device. The system receives form values, wherein at least some of the form values correspond to information about an appointment. Responsive to receiving a completion signal, the system generates a clinical narrative for the appointment using the completed form values.