Methods, systems, and computer-readable media are disclosed herein for providing objective electronic medical record (EMR) system adoption analysis. In an aspect, a subjective input and an objective input is received by a user via a user computing device. The subjective input is comprised of at least one of knowledge assessment related to an EMR system, an attitude assessment related to the EMR system, or a practice assessment related to the EMR system. The objective input is comprised of demographic data related to the user of the EMR system. A score is then calculated for the objective and subjective input, wherein calculating the score comprises applying a weight to the objective and the subjective input to determine one or more of a knowledge score, an attitude score, or a practice score. It is determined that at least one of the knowledge score, attitude score, or practice score, is below at least one predetermined threshold. And, responsive to determining this, an intervention module associated with at least one of the knowledge score, attitude score, or practice score, is automatically transmitted to the user.

Methods, systems, and computer-readable media are disclosed herein for providing objective electronic medical record (EMR) system adoption analysis. In an aspect, a subjective input and an objective input is received by a user via a user computing device. The subjective input is comprised of at least one of knowledge assessment related to an EMR system, an attitude assessment related to the EMR system, or a practice assessment related to the EMR system. The objective input is comprised of demographic data related to the user of the EMR system. A score is then calculated for the objective and subjective input, wherein calculating the score comprises applying a weight to the objective and the subjective input to determine one or more of a knowledge score, an attitude score, or a practice score. It is determined that at least one of the knowledge score, attitude score, or practice score, is below at least one predetermined threshold. And, responsive to determining this, an intervention module associated with at least one of the knowledge score, attitude score, or practice score, is automatically transmitted to the user.

A processor may receive a recording. The recording may include one or more user guidance sequences and be displayed to a user. The processor may receive a first user input response to an initiation of a first user guidance sequence. The processor may determine, in response to receiving the first user input, that there is a discrepancy between the first user input and the first user guidance sequence. The processor may automatically modify the playback setting of the recording. The processor may display the discrepancy between the first user input and the first user guidance sequence to the user.

Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

A method, system and computer readable medium for interactive learning between computing devices interconnected across a network with a server includes at least one guide operable computing device that communicates with student operable computing devices. During a learning session, the processor of the student operable computing device generates a student interface displaying a question from a library and captures an answer input thereto during a learning session. The processor of the guide operable computing device generates an interface displaying one or more questions; any associated possible answers and notes thereon and for displaying the answer input from the student operable computing device. The processor of the at least one guide operable computing device captures input on said student answer input via the interface for transmission across the network to the student operable computing device for display during the session.

A method, system and computer readable medium for interactive learning between computing devices interconnected across a network with a server includes at least one guide operable computing device that communicates with student operable computing devices. During a learning session, the processor of the student operable computing device generates a student interface displaying a question from a library and captures an answer input thereto during a learning session. The processor of the guide operable computing device generates an interface displaying one or more questions; any associated possible answers and notes thereon and for displaying the answer input from the student operable computing device. The processor of the at least one guide operable computing device captures input on said student answer input via the interface for transmission across the network to the student operable computing device for display during the session.

A learning system includes a non-transitory memory, and one or more hardware processors configured or programmed to read instructions from the non-transitory memory to cause the learning system to perform operations including generating a user knowledge mesh including generating topic nodes each corresponding to a topic included in the user knowledge mesh, and generating concept nodes each corresponding to a key learnable concept, wherein each of the topic nodes is connected to another one of the topic nodes, each of the concept nodes is connected to one of the topic nodes, and each of the key learnable concepts includes one or more interactions related to the key learnable concept.

A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.

A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.