Abstract: A data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.

Abstract: Techniques are described for managing tasks of a dynamic system with limited resources using a machine-learning and combinatorial optimization framework. In one embodiment, a computer-implemented method is provided that comprises employing, by a system operatively coupled to a processor, one or more first machine learning models to determine a total demand for tasks of a dynamic system within a defined time frame based on state information regarding a current state of the dynamic system, wherein the state information comprises task information regarding currently pending tasks of the tasks. The method further comprises, employing, by the system, one or more second machine learning models to determine turnaround times for completing the tasks based on the state information, and determining, by the system, a prioritization order for performing the currently pending tasks based on the total demand and the turnaround times.

Abstract: Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients.

Abstract: A spraying apparatus having an improved system for monitoring the flow of a spray nozzle or spray device and sensing malfunctions to the spray device is provided. The spraying apparatus includes sensors to monitor an input or instruction signal and a spray signal. The timing of the signals are analyzed to verify whether the spraying apparatus is opening and closing properly for each spray instruction signal.

Abstract: Systems and methods are provided for collecting, aggregating, and visualizing resource availability information including patient disease load for one or more medical facilities. In one example, a method includes determining, based on one or more test results of one or more tests for a disease included in a data stream from a medical facility and further based on an amount of time since the one or more tests were conducted, whether a patient suspected of having the disease is positive, negative, or under investigation for the disease; and updating one or more resource availability graphical user interfaces (GUIs) based on the determination.

Abstract: Systems and methods are provided for collecting, aggregating, and visualizing resource availability information including medical device availability for one or more medical facilities. In one example, a method includes receiving real-time data associated with a plurality of ventilators; determining, for each ventilator of the plurality of ventilators, a ventilator status based on one or more of a ventilation start time, a ventilation stop time, a ventilator type, and a ventilator location, the one or more of the ventilation start time, the ventilation stop time, the ventilator type, and the ventilator location determined from the real-time data; and updating one or more resource availability graphical user interfaces (GUIs) based on the ventilator status.

Abstract: Systems and methods are provided for collecting, aggregating, and visualizing resource availability information for one or more medical facilities. In one example, a method includes receiving first data from a first medical facility, the first data received via a first data streaming process and in a first format; receiving second data from a second medical facility, the second data received via a second data streaming process and in a second format; processing the first data and the second data to generate one or more resource availability graphical user interfaces (GUIs); and outputting the one or more resource availability GUIs for display on a display device.

Abstract: Systems, methods, and computer program products that enable system-wide probabilistic forecasting, alerting, optimizing and activating resources in the delivery of care to address both immediate (near real-time) conditions as well as probabilistic forecasted operational states of the system over an interval that is selectable from the current time to minutes, hours and coming days or weeks ahead are provided. There are multiple probabilistic future states that are implemented in these different time intervals and these may be implemented concurrently for an instant in time control, near term, and long term. Those forecasts along with their optimized control of hospital capacity may be independently calculated and optimized, such as for a dynamic workflow direction over the next hour and also a patient's stay over a period of days.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage care pathways and associated resources. An example apparatus includes a system monitor to coordinate a patient data analyzer and a care pathway processor to at least: identify a first patient record associated with a first care pathway; and identify a second patient record that is not on the first care pathway but should be on the first care pathway. The example apparatus includes a graphical user interface including information regarding, in a first area, the first patient record associated with the first care pathway and, in a second area, the second patient record that should be associated with the first care pathway.

Abstract: Techniques are described for optimizing patient placement and sequencing in dynamic medical environment. In various embodiments, a method includes receiving current state information regarding a current state of a medical facility system in real-time, including operating conditions data regarding current operating conditions of the medical facility system and patient case data regarding active patient cases and pending patient cases of the medical facility system. The method further includes forecasting future state information for the medical facility system based on the current state information using a machine learning framework, including forecasted timeline information regarding future timing of workflow events of the active patient cases and pending patient cases.

Abstract: A spraying apparatus having an improved system for monitoring the flow of a spray nozzle or spray device and sensing malfunctions to the spray device is provided. The spraying apparatus includes sensors to monitor an input or instruction signal and a spray signal. The timing of the signals are analyzed to verify whether the spraying apparatus is opening and closing properly for each spray instruction signal.

Abstract: Systems and techniques for monitoring, predicting and/or alerting for census periods in medical inpatient units are presented. A system can perform a first machine learning process to learn patterns in patient flow data related to a set of patient identities and a set of operations associated with a set of medical inpatient units. The system can also perform a second machine learning process to detect abnormalities associated with the patterns in the patient flow data. Furthermore, the system can determine patient census data associated with a prediction for a total number of patient identities in the set of medical inpatient units during a period of time based on the patterns and the abnormalities. The system can also generate an alert for a user interface in response to a determination that the patient census data satisfies a defined criterion.

Abstract: Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage care pathways and associated resources. An example apparatus includes a system monitor to coordinate a patient data analyzer and a care pathway processor to at least: identify a first patient record associated with a first care pathway; and identify a second patient record that is not on the first care pathway but should be on the first care pathway. The example apparatus includes a graphical user interface including information regarding, in a first area, the first patient record associated with the first care pathway and, in a second area, the second patient record that should be associated with the first care pathway.

Abstract: Techniques are described for managing tasks of a dynamic system with limited resources using a machine-learning and combinatorial optimization framework. In one embodiment, a computer-implemented method is provided that comprises employing, by a system operatively coupled to a processor, one or more first machine learning models to determine a total demand for tasks of a dynamic system within a defined time frame based on state information regarding a current state of the dynamic system, wherein the state information comprises task information regarding currently pending tasks of the tasks. The method further comprises, employing, by the system, one or more second machine learning models to determine turnaround times for completing the tasks based on the state information, and determining, by the system, a prioritization order for performing the currently pending tasks based on the total demand and the turnaround times.