The disclosed technology provides computer-to-wireless-voice integration methods and systems. In some implementations, the methods and systems deliver real-time voice instructions to users of required time-sensitive actions and ensure that such directives are received and a recipient effectively acts on the directives. The systems and methods include receiving a notification of an event from a terminal in a wireless active voice engine (WAVE) system, determining an active voice directive corresponding to the event with a WAVE module, converting the active voice directive into a voice event via a directive converter, and notifying a targeted recipient of the active voice directive corresponding to the event with a communications module. In some implementations, the systems and methods include sending a confirmation event via the receiver to the communications module that the active voice directive was received by the targeted recipient and communicating the active voice directive has been completed.

The disclosed technology provides computer-to-wireless-voice integration methods and systems. In some implementations, the methods and systems deliver real-time voice instructions to users of required time-sensitive actions and ensure that such directives are received and a recipient effectively acts on the directives. The systems and methods include receiving a notification of an event from a terminal in a wireless active voice engine (WAVE) system, determining an active voice directive corresponding to the event with a WAVE module, converting the active voice directive into a voice event via a directive converter, and notifying a targeted recipient of the active voice directive corresponding to the event with a communications module. In some implementations, the systems and methods include sending a confirmation event via the receiver to the communications module that the active voice directive was received by the targeted recipient and communicating the active voice directive has been completed.

One embodiment provides a method, including: receiving a process flow diagram element of a process flow diagram; identifying a context of the process flow diagram element, wherein the identifying a context comprises identifying (i) categories of elements connected to the process flow diagram element, (ii) swimlanes within the process flow diagram, and (iii) text included in the process flow diagram; encoding features of the process flow diagram element into a semantic vector, wherein the features are identified from the context of the process flow diagram element; and predicting, utilizing a process flow diagram model, a process flow diagram element for the process flow diagram based upon the at least one process flow diagram element, wherein the process flow diagram model receives and analyzes the features of the at least one process flow diagram and outputs the predicted process flow diagram element.

One embodiment provides a method, including: receiving a process flow diagram element of a process flow diagram; identifying a context of the process flow diagram element, wherein the identifying a context comprises identifying (i) categories of elements connected to the process flow diagram element, (ii) swimlanes within the process flow diagram, and (iii) text included in the process flow diagram; encoding features of the process flow diagram element into a semantic vector, wherein the features are identified from the context of the process flow diagram element; and predicting, utilizing a process flow diagram model, a process flow diagram element for the process flow diagram based upon the at least one process flow diagram element, wherein the process flow diagram model receives and analyzes the features of the at least one process flow diagram and outputs the predicted process flow diagram element.

In operational methodology and a software package or other computer enabled business method, which enables users to apply the method and practice. A wide variety of means for identifying, evaluating and mitigating risk and performance factors within an organization are also provided.

In operational methodology and a software package or other computer enabled business method, which enables users to apply the method and practice. A wide variety of means for identifying, evaluating and mitigating risk and performance factors within an organization are also provided.

A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

A method for generating at least one installation operation for at least one on-going installation process of at an installation site includes obtaining site information of the at least one ongoing installation process at the installation site from a site control unit arranged at the installation site, obtaining site information gathered from one or more previous installation processes at one or more other installation sites from one or more external databases, defining at least one installation operation for the at least one ongoing installation process based on the site information of the at least one ongoing installation process and the site information gathered from the one or more previous installation processes, and generating at least one signal including an instruction to perform the at least one installation operation for the at least one ongoing installation process to the site control unit. A computing unit, an installation support system, and a computer program performing at least partly the method are disclosed.

An apparatus (10) for generating benchmarking metrics of current operational workflow performance of a hospital department includes at least one electronic processor (20) programmed to: generate a department profile (34) identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile; retrieve current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show, and emergency department (ED) arrival statistics; compute values of one or more key performance indicator (KPI) metrics (40) for the current statistics; generate an executable workflow model (44) for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; simulate a best case scenario (50) by executing the workflow model on inputs including the department profile and best case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated best case scenario; simulate a worst case scenario (52) by executing the workflow model on inputs including the department profile and worst case values for the variables of the workflow model and compute values of the one or more KPI metrics for the simulated worst case scenario; and output, on at least one display device (24), the values of the one or more KPI metrics computed for the simulated best case scenario, the values of the one or more KPI metrics computed for the simulated worst case scenario, and the values of the one or more KPI metrics computed for the current statistics.