The present disclosure provides a method for automatically collecting and matching laboratory data, including: obtaining a creation time of experimental data, determining target experimental data corresponding to a target time in accordance with the creation time, segmenting the target experimental data into a plurality data blocks, generating a data block index table, including at least one data block identifier, according to the data blocks, selecting a target matching mode from a plurality of predetermined matching modes according to the data block index table, obtaining the data block identifier upon determining the target experimental data in a storage node is loaded, and extracting data content in the target experimental data corresponding to the data block identifier by the target matching mode. This method may greatly reduce the number of string matching and may reduce the complexity of the algorithm.

A computer-implemented method to improve adherence level to a medical treatment plan or medication regimen is disclosed. The method comprises the steps to integrate with the payer, to create or manage a care plan, to engage caregivers, to connect to pharmacies and to engage with HCPs. The method may also comprise connection with one or more communities or may comprise connection with external resources. A system for improving treatment adherence level is disclosed. The system comprises a server, computerized devices for patients, computerized devices for caregivers and a network configured to allow communication between the different devices and stakeholders. The computerized devices for patients are configured to fetch and display treatment or medications plan data associated with the user. The computerized devices for caregivers are configured to fetch and display treatment or medication plans data associated with the one or more users (patients) being followed by the caregiver.

A computer-implemented method to improve adherence level to a medical treatment plan or medication regimen is disclosed. The method comprises the steps to integrate with the payer, to create or manage a care plan, to engage caregivers, to connect to pharmacies and to engage with HCPs. The method may also comprise connection with one or more communities or may comprise connection with external resources. A system for improving treatment adherence level is disclosed. The system comprises a server, computerized devices for patients, computerized devices for caregivers and a network configured to allow communication between the different devices and stakeholders. The computerized devices for patients are configured to fetch and display treatment or medications plan data associated with the user. The computerized devices for caregivers are configured to fetch and display treatment or medication plans data associated with the one or more users (patients) being followed by the caregiver.

Systems and methods for generating synthetic medical data are provided. A method may include retrieving a set of authentic electronic medical records from a database. The method may further include converting the authentic set of electronic medical records to a set of numerical vectors. The method may further include training a first neural network based on a random noise generator sample, the first neural network outputting synthetic electronic medical records. The method may further include training a second neural network based on the output synthetic electronic medical records and the set of numerical vectors, the second neural network outputting a loss distribution indicating whether the output synthetic electronic medical records are classified as authentic or synthetic.

A method, apparatus and computer program product refine an automated coding model, such as for a medical chart. For each respective candidate code from a set of candidate codes, the method predicts a probability of the respective code being contained in a medical chart. The method also selects one of the candidate codes as being contained in the medical chart based upon the probability and removes the selected candidate code from the set of candidate codes. The method then repeatedly predicts the probability of a respective code being contained in the medical chart, selects one of the candidate codes based upon the predicted probability and removes the selected candidate code from the set of candidate codes. The method further determines a categorical crossentropy loss as to permit adjustment of one or more parameters of the automated coding model.

A telesurgical mentoring platform with a wheeled base, a lower rack mounted on the base, an upper rack extending vertically from the lower rack, a compactly foldable articulated arm that is configured to extend horizontally outward away from the upper rack and configured to connect to a connector piece holding an end effectuator at its distal end, a tablet personal computer; the console configured to be readily mobilized on the floor of an existing operating room and is capable of providing a connectivity point for communication, audiovisual, and data transfer services in an operating room.

A telesurgical mentoring platform with a wheeled base, a lower rack mounted on the base, an upper rack extending vertically from the lower rack, a compactly foldable articulated arm that is configured to extend horizontally outward away from the upper rack and configured to connect to a connector piece holding an end effectuator at its distal end, a tablet personal computer; the console configured to be readily mobilized on the floor of an existing operating room and is capable of providing a connectivity point for communication, audiovisual, and data transfer services in an operating room.

A method for generating an electronic file associated with health care professionals (HCPs). The method includes receiving health information from a covered entity computing system and a file generation request from a user computing device. The method further includes matching HCP information with the health information and determining one or more statistical values based on at least one of the matched HCP information or the matched health information. The method further includes generating the electronic file including the one or more statistical values and at least a portion of the matched HCP information and providing the file to the destination address of the file generation request. The electronic file is generated as the file type identified by the file generation request, and is structured based on at least one of the file generation request or the file destination.

A method may include a method of automating processes for remote work. The method may include receiving, at a server, first login data from a client software application. The client software application may be executing on a user device of a remote worker user. The method may include authenticating the remote worker user based on the first login data. The method may include receiving, at the server, command data from the client software application. The command data may include data indicating to the server to launch a software application. The method may include launching, on the server, the software application. The method may include inputting, using a robotic process automation (RPA) process, second login data of the remote worker user into the software application. The method may include key site information, speech-to-text functionality, onboarding functionality, automated support, or activity logging.

The present invention relates to an artificial intelligence healthcare assistant device and an associated system allowing patients to better understand their healthcare and receive medical care immediately and periodically using artificial intelligence-based systems. The artificial intelligence device receives medical information of a patient and provides healthcare information, medication, and other treatment options in an easy-to-understand format. The system includes an AI server that also provides analysis on the patient information for detailed healthcare information. The device allows patients to post query to a medical expert and also determines if there is an emergency with the patient. A patient can interact in a variety of ways with the device and reduces chances of confusion in determining a healthcare plan for the patient.