Abstract: A computer-implemented method, computer program product and computing system for: acoustically monitoring a medical environment to generate an acoustic signal indicative of audio within the medical environment; processing the acoustic signal to identify one or more audible alarms within the medical environment; categorizing the one or more audible alarms, thus defining categorized alarms; and processing the categorized alarms.

Abstract: A computer-implemented method, computer program product and computing system for: gathering information from a datasource concerning one or more medical professionals within one or more medical institutions, thus defining gathered information; enabling a user to select a viewing lens from a plurality of available viewing lenses through which to display the gathered information, thus defining a selected viewing lens; and rendering at least a portion of the gathered information based, at least in part, upon the selected viewing lens.

Abstract: A computer-implemented method, computer program product and computing system for: gathering information from a datasource concerning one or more organizations, thus defining gathered information; enabling a user to select a viewing lens from a plurality of available viewing lenses through which to display the gathered information, thus defining a selected viewing lens; and rendering at least a portion of the gathered information based, at least in part, upon the selected viewing lens.

Abstract: A computer-implemented method, computer program product and computing system for: defining an incident as the occurrence of a plurality of required alarms; monitoring a plurality of devices to detect the occurrence of alarms, thus defining a plurality of detected alarms; and defining the incident as having occurred if the plurality of detected alarms includes the plurality of required alarms.

Abstract: A computer-implemented method, computer program product and computing system for: interfacing with a plurality of bedside monitoring devices to receive data signals, wherein the data signals have monitoring criteria; and enabling adjustment of one or more of the monitoring criteria.

Abstract: A computer-implemented method, computer program product and computing system for: monitoring a bedside monitoring device to detect the occurrence of alarms, thus defining detected alarms; processing the detected alarms to determine their authenticity; and if a detected alarm is determined to be non-authentic, adjusting one or more monitoring criteria that was instrumental in producing the non-authentic detected alarm.

Abstract: A computer-implemented method, computer program product and computing system for: gathering information from a datasource concerning one or more incidents within one or more medical institutions, thus defining gathered information; enabling a user to select a viewing lens from a plurality of available viewing lenses through which to display the gathered information, thus defining a selected viewing lens; and rendering at least a portion of the gathered information based, at least in part, upon the selected viewing lens.

Abstract: A computer-implemented method, computer program product and computing system for: interfacing with a bedside monitoring device to receive data signals; automatically obtaining patient information from a medical record associated with a patient assigned to the bedside monitoring device; and providing the patient information to the bedside monitoring device.

Abstract: A computer-implemented method, computer program product and computing system for: monitoring a device to receive data signals indicative of the device; and processing the data signals over a defined period of time to automatically define one or more defined signal norms for the data signals.

Abstract: A computer-implemented method, computer program product and computing system for: defining an incident as the occurrence of a plurality of required alarms; defining an event as the occurrence of a plurality of required incidents; monitoring a plurality of devices to detect the occurrence of alarms, thus defining a plurality of detected alarms; and defining the event as having occurred if the plurality of detected alarms includes the plurality of required alarms for each of the plurality of required incidents.

Abstract: Embodiments are directed towards identifying and decreasing operational pain and increasing system efficiency through health monitoring and management. This may be accomplished through measuring, monitoring, reducing meaningful incident behavior across an organization and using it to inform necessary changes in the organizational operations to improve efficiency, or the like. Ergonomic data or metrics collected by a resource management engine may be used to intelligently inform management decisions to increase human well-being across the organization's workforce to optimize overall system performance. Accordingly, resource management engines may identify areas in organizations that need improvement or repair. In some embodiments, resource management engines may be arranged to participate in a continuous feedback loop that may provide continuous overall system optimization.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

Abstract: Implementations of this disclosure are directed towards the computer-implemented determination of interrupt events from a plurality of notification events generated by one or more of a plurality of computer systems. Interrupt events are associated with notifications designed to immediately alert a responder. A health score is generated as a weighted sum of one or more health sub-scores that are generated using one or more health sub-score models that take as input more health metrics representative of data associated with one or more interrupt events. The health sub-scores are representative of an impact relating to the management of operations of the plurality of computer systems and the weights used in the weighted score are representative of an estimation of the relative impact of the generated health sub-scores. The health sub-score models may be automatically modified according to an automated analysis of historic operational outcome metrics and historic scores.

Abstract: Embodiments are directed towards to managing operations for an organization. An operation engine may be employed to provide operations metrics based on events and resolution actions associated with an incident. Update information may be provided to console modules included in operations command consoles (OCCs) each console module may include module views. The console modules may be employed to provide data objects to the module views. The module views may be employed to provide different visualizations of the incident based on a role that may be associated with the console modules. An OCC may be employed to provide other resolution actions to the operations engine and then that operations engine may provide additional update information to the console modules based on the other resolution actions.

Abstract: Embodiments are directed towards embodiments for monitoring or predicting user health with respect their use of connected computers, such as, mobile phones, tablets, or the like. Activities associated with a user and a computing device may be monitored to determine activity events. One or more sub-scores may be provided in real time based on metrics provided as input to sub-score models such that the metrics are associated with the activity events. A health score associated with a probability of an occurrence of adverse user outcomes may be provided based on a health model that uses the sub-scores. An analysis engine may compare the health score to other health scores to predict in real time the adverse outcomes. The analysis engine may recommend one or more actions to decrease the probability of the occurrence of the adverse outcomes based on the result.

Abstract: Embodiments are directed towards identifying and decreasing operational pain and increasing system efficiency through health monitoring and management. This may be accomplished through measuring, monitoring, reducing meaningful incident behavior across an organization and using it to inform necessary changes in the organizational operations to improve efficiency, or the like. Ergonomic data or metrics collected by a resource management engine may be used to intelligently inform management decisions to increase human well-being across the organization's workforce to optimize overall system performance. Accordingly, resource management engines may identify areas in organizations that need improvement or repair. In some embodiments, resource management engines may be arranged to participate in a continuous feedback loop that may provide continuous overall system optimization.

Abstract: Embodiments are directed towards embodiments for monitoring or predicting user health with respect their use of connected computers, such as, mobile phones, tablets, or the like. Activities associated with a user and a computing device may be monitored to determine activity events. One or more sub-scores may be provided in real time based on metrics provided as input to sub-score models such that the metrics are associated with the activity events. A health score associated with a probability of an occurrence of adverse user outcomes may be provided based on a health model that uses the sub-scores. An analysis engine may compare the health score to other health scores to predict in real time the adverse outcomes. The analysis engine may recommend one or more actions to decrease the probability of the occurrence of the adverse outcomes based on the result.

Abstract: Embodiments are directed to managing operations. If Operations events are provided, event clusters may be associated with one or more Operations events, such that the Operations events may be associated with the event clusters based on characteristics of the Operations events. Metrics including resolution metrics, root cause analysis, notes, and other remediation information may be associated with the event clusters. Then a modeling engine may be employed to train models based on the Operations events, the event clusters, and the resolution metrics, such that the trained model may be trained to correlate and predict the resolution metrics from real-time Operations events. If real-time Operations events may be provided, the trained models may be employed to predict the resolution metrics that are associated with the real-time Operations events. If model performance degrades beyond accuracy requirements, new observations may be added to the training set and the model re-trained.

Abstract: Embodiments are directed towards to managing operations for an organization. An operation engine may be employed to provide operations metrics based on events and resolution actions associated with an incident. Update information may be provided to console modules included in operations command consoles (OCCs) each console module may include module views. The console modules may be employed to provide data objects to the module views. The module views may be employed to provide different visualizations of the incident based on a role that may be associated with the console modules. An OCC may be employed to provide other resolution actions to the operations engine and then that operations engine may provide additional update information to the console modules based on the other resolution actions.