Abstract: Various methods and systems are provided for automatically generating a recommendation to downgrade a patient from an intensive care unit (ICU) to a different medical unit. In one embodiment, a method for a patient downgrade recommendation system comprises, for each patient of a plurality of selected patients of the ICU, selecting a set of downgrade criteria for the patient; from a first, larger set of patient data of the patient, automatically compiling a second, smaller set of patient data based on the set of downgrade criteria; applying a set of rules associated with the set of downgrade criteria to the second, smaller set of patient data to generate a downgrade recommendation for the patient; and arranging the graphical downgrade recommendation elements of the plurality of selected patients in a single integrated view of a user interface a display device of the patient downgrade recommendation system.

Abstract: Various methods and systems are provided for automatically generating downgrade recommendations. In one embodiment, a method for a patient downgrade recommendation system includes displaying a menu listing one or more options for retrieving data of one or more patients from a plurality of databases and systems of a hospital, the plurality of databases and systems including one or more electronic health record (EHR) systems, and displaying a recommendation graphical user interface (GUI) that displays, for each patient, a downgrade recommendation recommending whether or not to downgrade the patient, the downgrade recommendation being selectable to add additional display elements to the recommendation GUI display panel identifying parameters from the retrieved data from the one or more EHR systems that triggered a displayed downgrade recommendation, wherein the recommendation GUI is displayed while the one or more EHR systems are in an un-launched state.

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 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 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: Certain examples provide systems and methods to monitor and control hospital operational systems based on occupancy data and medical orders. An example healthcare workflow management and reasoning system includes a workflow engine including a first particularly programmed processor to monitor one or more medical orders from one or more hospital information systems to identify a condition indicating that a first patient in a first room is ready for a clinical activity such as discharge. The example healthcare workflow management and reasoning system includes a sensing component including a second processor to gather occupancy data regarding the first patient in the first room and transmit the occupancy data to the workflow engine. The example workflow engine controls one or more hospital operational systems to trigger cleaning of the first room, lighting settings for the first room, and transportation of a second patient to the first room based on occupancy data from the sensing component.

Abstract: An example method includes: classifying lung function risk based on patient attributes and a clinical protocol; generating alarms and incentives for compliance with the clinical protocol based on patient attributes, clinical protocol, and patient lung function risk; determining an orientation and position of a clinical device based on tagged feature(s) of the clinical device compared to identified patient feature(s); monitoring patient interaction with the clinical device; identifying a deviation from the clinical protocol based on the monitored patient interaction, a patient biometric indicator, and a desired setpoint state in the protocol; when a deviation is identified, providing feedback proportional to the deviation, the feedback including an adjustment with respect to the clinical protocol and/or the clinical device; and triggering at least one alarm and/or incentive based on deviation and feedback, wherein the alarm/incentives differs based on whether and to what extent deviation is identified and feed

Abstract: An example method includes: classifying lung function risk based on patient attributes and a clinical protocol; generating alarms and incentives for compliance with the clinical protocol based on patient attributes, clinical protocol, and patient lung function risk; determining an orientation and position of a clinical device based on tagged feature(s) of the clinical device compared to identified patient feature(s); monitoring patient interaction with the clinical device; identifying a deviation from the clinical protocol based on the monitored patient interaction, a patient biometric indicator, and a desired setpoint state in the protocol; when a deviation is identified, providing feedback proportional to the deviation, the feedback including an adjustment with respect to the clinical protocol and/or the clinical device; and triggering at least one alarm and/or incentive based on deviation and feedback, wherein the alarm/incentives differs based on whether and to what extent deviation is identified and feed

Abstract: An example method includes: classifying lung function risk based on patient attributes and a clinical protocol; generating alarms and incentives for compliance with the clinical protocol based on patient attributes, clinical protocol, and patient lung function risk; determining an orientation and position of a clinical device based on tagged feature(s) of the clinical device compared to identified patient feature(s); monitoring patient interaction with the clinical device; identifying a deviation from the clinical protocol based on the monitored patient interaction, a patient biometric indicator, and a desired setpoint state in the protocol; when a deviation is identified, providing feedback proportional to the deviation, the feedback including an adjustment with respect to the clinical protocol and/or the clinical device; and triggering at least one alarm and/or incentive based on deviation and feedback, wherein the alarm/incentives differs based on whether and to what extent deviation is identified and feed

Abstract: Certain examples provide systems and methods to monitor and control hospital operational systems based on occupancy data and medical orders. An example healthcare workflow management and reasoning system includes a workflow engine including a first particularly programmed processor to monitor one or more medical orders from one or more hospital information systems to identify a condition indicating that a first patient in a first room is ready for a clinical activity such as discharge. The example healthcare workflow management and reasoning system includes a sensing component including a second processor to gather occupancy data regarding the first patient in the first room and transmit the occupancy data to the workflow engine. The example workflow engine controls one or more hospital operational systems to trigger cleaning of the first room, lighting settings for the first room, and transportation of a second patient to the first room based on occupancy data from the sensing component.

Abstract: An example method includes: classifying lung function risk based on patient attributes and a clinical protocol; generating alarms and incentives for compliance with the clinical protocol based on patient attributes, clinical protocol, and patient lung function risk; determining an orientation and position of a clinical device based on tagged feature(s) of the clinical device compared to identified patient feature(s); monitoring patient interaction with the clinical device; identifying a deviation from the clinical protocol based on the monitored patient interaction, a patient biometric indicator, and a desired setpoint state in the protocol; when a deviation is identified, providing feedback proportional to the deviation, the feedback including an adjustment with respect to the clinical protocol and/or the clinical device; and triggering at least one alarm and/or incentive based on deviation and feedback, wherein the alarm/incentives differs based on whether and to what extent deviation is identified and feed