General Electric Company

Ge Precision Healthcare LLC

Andrew Day

Christopher Donald Johnson

Michelle Mbeo

David Toledano

Jeffrey Richardson Terry

Kunter Seref Akbay

Bex George Thomas

Peter Henry Tu

Tamas Fixler

Siyun Hur

Peter Katlic

Ting Yu

Mohammed Uddin

Savanoor Pradeep Rai

Brandon Stephen Good

GianFranco Doretto

Rajesh Tyagi

Yang Zhao

Onur Ilkin Dulgeroglu

Nitish Umang

Hong Yang

Ryan Mancl

Rulin Chen

Marcia Peterson

Gabriella Devine

Leonardo Dias

Aristotelis Emmanouil Thanos Filis

Younan Fakhouri

Mark Grum

Gaurav Sahariya

Sean Lister

Display screen or portion thereof with icon

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.

Integrated systems and methods for evolving state protocols and decision support

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.

Systems and methods for resource availability management

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.

Machine-learning and combinatorial optimization framework for managing tasks of a dynamic system with limited resources

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. The method can further include providing discharge information identifying the discharge destinations predicted for the respective patients to one or more care providers to facilitate managing and coordinating inpatient and post-discharge care for the respective patients.

System and method for identifying complex patients, forecasting outcomes and planning for post discharge care

Display screen with animated icon

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. In the present application, a probabilistic and conditional workflow reasoning system enabling complex team-based decisions that improve capacity, satisfaction, and safety is provided. A means to consume user(s) judgment, implement control on specific resource assignments and tasks in a clinical workflow is enabled, as is the dynamical and optimal control of the other care delivery assets being managed by the system so as to more probably achieve operating criteria such as throughput, waiting and schedule risk.

System-wide probabilistic alerting and activation

Display screen with graphical user interface

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.

Monitoring, predicting and alerting for census periods in medical inpatient units

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.

Methods and systems to sense situational awareness with a dual doppler and control for optimized operations

Display screen with icon

Growing community of inventors

Newtown, PA, United States of America

Andrew Day