A method for reducing pre-authorization turnaround time is disclosed. The method includes, at a database, receiving historical data including a historical pre-authorization request and clinical information associated with a historical pre-authorization request. The method further includes receiving real-time data using an API gateway including real-time pre-authorization requests wherein the real-time data includes the real-time pre-authorization procedure and a clinical document category. The method further includes removing irrelevant data from real-time data and historical data to produce clean historical data and clean real-time data. The method further includes extracting data features required to train a machine learning model from the clean historical data and clean real-time data. The method further includes training the machine learning model by applying the extracted data features from the clean historical data and clean real-time data. The method further includes identifying prediction data results by applying the trained machine learning model.

A method for reducing pre-authorization turnaround time is disclosed. The method includes, at a database, receiving historical data including a historical pre-authorization request and clinical information associated with a historical pre-authorization request. The method further includes receiving real-time data using an API gateway including real-time pre-authorization requests wherein the real-time data includes the real-time pre-authorization procedure and a clinical document category. The method further includes removing irrelevant data from real-time data and historical data to produce clean historical data and clean real-time data. The method further includes extracting data features required to train a machine learning model from the clean historical data and clean real-time data. The method further includes training the machine learning model by applying the extracted data features from the clean historical data and clean real-time data. The method further includes identifying prediction data results by applying the trained machine learning model.

An example method includes receiving a request to create a surgical case corresponding to a patient identifier and a provider identifier and associating one or more of patient data and provider data with the surgical case based on the patient identifier or the provider identifier. Case data is retrieved from a medical management system using one or more of the patient identifier and the provider identifier to query the medical management system. A workflow state for the surgical case is initialized by setting statuses for preconfigured workflow items based on the case data. A view of the surgical case is displayed via a user interface. The view of the surgical case displays at least a portion of the case data and includes one or more status elements configured based on the workflow state for the surgical case.

A virtual care system can include a location module configured to receive location information associated with a device from the device, and a graphical user interface (GUI) module configured to generate a medical provider user interface accessible via the device. The medical provider user interface can be contextually generated based on the location information and includes interface characteristics associated with the location information.

A computer-implemented method for employing a surgical instrument educational platform to train medical personnel to identify, characterize, and organize surgical instrumentation and surgical instrumentation trays includes selecting a level from one or more levels on a display of an electronic device, selecting a surgical category from a plurality of surgical categories, in response to selecting the surgical category, an image of a surgical instrument is displayed on the display of the electronic device and a query is made to identify the image of the surgical instrument, loading surgical instrument options to allow a user to associate the image of the surgical instrument to one of the surgical instrument options, prompting the user to select an option from the surgical instrument options, and in response to the selected option by the user, outputting a result.

An eye test administered to a remote subject over a computer network, where sensors provide feedback through the computer network so that light source position, light source brightness and/or a display configuration and/or parameter setting related to a visual display that is used to implement the remote eye test.

A system and method are provided for performing operations comprising: receiving one or more images from an image capture device of a medical treatment location; applying a trained machine learning model to the one or more images to detect presence of a patient in the medical treatment location, the trained machine learning model being trained to establish a relationship between one or more features of images of the medical treatment location and patient presence; generating context assessment for the medical treatment location based on the detected presence of the patient; and transmitting, over a network, the context assessment for presentation on a user interface of a client device.

A system and method are provided for performing operations comprising: receiving one or more images from an image capture device of a medical treatment location; applying a trained machine learning model to the one or more images to detect presence of a patient in the medical treatment location, the trained machine learning model being trained to establish a relationship between one or more features of images of the medical treatment location and patient presence; generating context assessment for the medical treatment location based on the detected presence of the patient; and transmitting, over a network, the context assessment for presentation on a user interface of a client device.

A network device (100) detects a network problem in a medical system (105). A reception module (110) receives current medical system process data. A monitoring module (120) detects predefined events (124) based on the process data and triggers a detection signal (132) output in the presence of a predefined event. A sending module (130) sends the detection signal to a predefined device address (134) via a network (140). The predefined events include: a predefined plurality of unsuccessful password entry attempts within a predefined first time period; an unsuccessful encryption within an encryption protocol framework; a predefined plurality of outputs via the network triggered via the network within a predefined second time period; an output of a signal, which is to be carried out, has been unsuccessful; and a predefined number of messages have been received within the framework of a service discovery within a predefined third time period.

A network device (100) detects a network problem in a medical system (105). A reception module (110) receives current medical system process data. A monitoring module (120) detects predefined events (124) based on the process data and triggers a detection signal (132) output in the presence of a predefined event. A sending module (130) sends the detection signal to a predefined device address (134) via a network (140). The predefined events include: a predefined plurality of unsuccessful password entry attempts within a predefined first time period; an unsuccessful encryption within an encryption protocol framework; a predefined plurality of outputs via the network triggered via the network within a predefined second time period; an output of a signal, which is to be carried out, has been unsuccessful; and a predefined number of messages have been received within the framework of a service discovery within a predefined third time period.