Systems and methods to implement commands based on selection sequences to a user interface are disclosed. Exemplary implementations may: store, electronic storage, a library of terms utterable by users that facilitate implementation of intended results; obtain audio information representing sounds captured by a client computing platform; detect the spoken terms uttered by the user present within the audio information; determine whether the spoken terms detected are included in the library of terms; responsive to determination that the spoken terms are not included in the library of terms, effectuate presentation of an error message via the user interface; record a selection sequence that the user performs subsequent to the presentation of the error message that causes a result; correlate the selection sequence with the spoken terms based on the selection sequence recorded subsequent to error message to generate correlation; and store the correlation to the electronic storage.

Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user.

Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user.

An example method includes storing healthcare information related to a patient on the cloud-based clinical information system and determining if a first local information system of a first healthcare entity includes a first existing patient record associated with the patient. The example method also includes communicating a first command from the cloud-based clinical information system to an edge device located at a facility associated with the first healthcare entity and in communication with the first local information system. The edge device is to automatically attach a first portion of the healthcare information to the first existing patient record if the first local information system includes the first existing patient record or automatically generate a first new patient record to be populated by the first portion of the healthcare information if the first local information system does not include the first existing patient record based on the first command.

An example method includes storing healthcare information related to a patient on the cloud-based clinical information system and determining if a first local information system of a first healthcare entity includes a first existing patient record associated with the patient. The example method also includes communicating a first command from the cloud-based clinical information system to an edge device located at a facility associated with the first healthcare entity and in communication with the first local information system. The edge device is to automatically attach a first portion of the healthcare information to the first existing patient record if the first local information system includes the first existing patient record or automatically generate a first new patient record to be populated by the first portion of the healthcare information if the first local information system does not include the first existing patient record based on the first command.

A method is provided for treating a patient with an arrhythmia. In some embodiments, the method collects a first patient arrhythmia cardiogram from the patient. The method identifies a first target location and a first ablation pattern associated with a first library arrhythmia cardiogram that is similar to the first patient arrhythmia cardiogram. The method then performs a first ablation near the first target location, factoring in the first ablation pattern, and after the ablation, collects a second patient arrhythmia cardiogram of the patient. The method continues to identify a second target location and a second ablation pattern associated with a second library arrhythmia cardiogram that is similar to the second patient arrhythmia cardiogram. The second library arrhythmia cardiogram is identified, in part, based on ablation characteristics of the first ablation. The method then performs a second ablation near the second target location, factoring in the second ablation pattern.

The present invention relates to a method for providing medical equipment and medicine information, and includes: a medical paper database that provides medical papers; a medical equipment and medicine database that provides medical equipment and medicine information; and a service server that finds, from the medical equipment and medicine database, information about medical equipment and medicine and posts the found medical equipment and medicine on a network, and also periodically searches the medical paper database for the papers in which the posted medical equipment and medicine have been described to provide a user terminal with paper information and links.