Abstract: The present invention relates to systems and methods of express tracking for patient flow management in a distributed environment. Particularly, aspects are directed to a computer implemented method that includes initiating a check-in process that includes prompting a user to scan an identifier, processing the identifier using a cascade machine-learning architecture comprising of a multi-task convolutional neural network model and a recurrent neural network model to obtain the classification of the identifier and member identification information, determining whether the user is known user based on the member identification information, when the user is a known user, verifying user data saved in the computing system associated with the identifier, once the user data is verified, determining whether the user has a scheduled appointment; and when the user has a scheduled appointment, checking the user in for the scheduled appointment.

Abstract: The present invention relates to systems and methods of express tracking for patient flow management in a distributed environment. Particularly, aspects are directed to a computer implemented method that includes initiating a check-in process that includes prompting a user to scan an identifier, processing the identifier using a cascade machine-learning architecture comprising of a multi-task convolutional neural network model and a recurrent neural network model to obtain the classification of the identifier and member identification information, determining whether the user is known user based on the member identification information, when the user is a known user, verifying user data saved in the computing system associated with the identifier, once the user data is verified, determining whether the user has a scheduled appointment; and when the user has a scheduled appointment, checking the user in for the scheduled appointment.

Abstract: The present invention relates to systems and methods of express tracking for patient flow management in a distributed environment. Particularly, aspects are directed to a computer implemented method that includes initiating a check-in process that includes prompting a user to scan an identifier, processing the identifier using a cascade machine-learning architecture comprising of a multi-task convolutional neural network model and a recurrent neural network model to obtain the classification of the identifier and member identification information, determining whether the user is known user based on the member identification information, when the user is a known user, verifying user data saved in the computing system associated with the identifier, once the user data is verified, determining whether the user has a scheduled appointment; and when the user has a scheduled appointment, checking the user in for the scheduled appointment.

Abstract: The present invention relates to systems and methods of express tracking for patient flow management in a distributed environment. Particularly, aspects are directed to a computer implemented method that includes initiating a check-in process that includes prompting a user to scan an identifier, processing the identifier using a cascade machine-learning architecture comprising of a multi-task convolutional neural network model and a recurrent neural network model to obtain the classification of the identifier and member identification information, determining whether the user is known user based on the member identification information, when the user is a known user, verifying user data saved in the computing system associated with the identifier, once the user data is verified, determining whether the user has a scheduled appointment; and when the user has a scheduled appointment, checking the user in for the scheduled appointment.