C/hca, Inc.

Hca Holdings, Inc.

Edmund Stephen Jackson

William Michael Gregg

Jim Najib Jirjis

Jonathan Perlin

Thomas Andrew Doyle

Paul Martin Paslick

Paul Brient

Christian Krayer

Sarah Hume Buta

Umesh P Phirke

Erin S Jospe

Karl Bradley Kehler

Deborah Reiner

Annabaker Garber

Victoria Weaver

Christopher Wobensmith

Ravi S Chari

Wesley Boles

Steven V Manoukian

Aaron Montlary

Vincent Doyle, Iii

Cody Hall

Brian Freeman

Victoria Samples

Gabriel O Perez

Jeffrey Cuthbert

Janet McCallister

Cae Swanger

Timothy Beacham

Warren Sadler

Louis Davis

Erica Williams

Chigger Bynum

Troy Gifford

George Tucker

Heather Fuller

Dan Nguyen

Randy Scott Fagin

Scott Murray

Thomas Neal Payne

Daniel Hefley

Tara Haines

Sarah Dhane

James E Hicks

Christopher Anthony

William Rice

Igor Ges

J R Allen

Tyler Forehand

Brian Weldy

Nathan Watkins

Megan McGee

Brooke Hamilton

Hannah Marshall

Stephanie Fulbright

Jane Englebright

Nan Chen

Martin Tobias

Sammie Mosier

Bryan Sisk

Todd Shafer

Clarence Robert Foster

David Wedemeyer

Jeffrey Scott Smith

Robert Rochford

Betty A Chodkowski

Electronic communications received via a network from a plurality of electronic devices may include signals of device interactions or data changes that correspond to process performances by process-performing resources, signals of conditions of loads, or signals of processes associated with the process-performing resources and the loads. Data composites may be formed from the electronic communications, with data portions collected and mapped to resource profile records and load profile records that may be updated with the collected data portions. For each load, at least one of the one or more resource profile records and/or the one or more load profile records may be used to map the process-performing resources to the load. Content nodes may be linked in a network of content nodes, including respective linked content, resource specifications or load specifications. Access to the network of content nodes may be allowed via a control interface.

Multi-tier resource, subsystem, and load orchestration

Systems, methods, and machine-readable storage media for processing data streams in accordance with protocols to selectively transmit content to endpoint devices to facilitate conferencing are disclosed. Electronic communications may be received, via one or more networks, from endpoint devices, segregated, and routed to facilitate conferencing environments. Listening for data changes in data streams may be performed, each data stream corresponding to a particular source. Data changes that are generated based on events may be detected and mapped to a particular load identifier and a particular conferencing environment. A conferencing protocol may be identified and mapped to the particular conferencing environment. Consequent to determining that the data changes correspond to the one or more, the particular conferencing environment may be updated. a content composite may be created based at least in part on the data changes and may be transmitted endpoint devices mapped to a particular population of resource identifiers.

Data stream processing to facilitate conferencing based on protocols

In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.

Contextual assessment of current conditions

Approaches are provided for a prediction model determining a prediction indicative of the authorized user being associated with at least one anomaly event. In one example, a computer system may receive data from different sources. For example, the system may receive data associated with use of one or more monitored units of an automated storage and retrieval location. The system may also receive request data associated with a request for execution of the monitored controlled unit by an authorized user to a target user. The prediction model of the system may utilize the received data to determine the prediction that the authorized user is associated with at least one anomaly event associated with a diversion of a monitored unit away from the target user. The system may then provide the prediction for presentation.

Utilizing predictive modeling to identify anomaly events

In some examples, a prediction is generated by a prediction system. A machine learning model of the prediction system receives as input one or more data elements of a user service record, and outputs a prediction, the prediction corresponding to one or more probability scores. The one or more probability scores correspond to a prediction that a user will exhibit a certain behavioral pattern (e.g., probability of readmission to a service unit within a predefined post-release time period) and/or be classified into a certain service category (e.g. probability of association with a particular service group, the service group being included within a set of qualifying service groups). The prediction system may then provide the prediction to a user device of a user coordinator for subsequent presentation on the user device to assist the user coordinator in coordinating user service.

Predictive modeling for enhanced decision making

In some examples, unstructured data is evaluated using a natural language processing model to output a set of subjective indicators. These subjective indicators are scored using a predictive model to determine whether a dependent user has or is likely to develop a particular condition such as a cellular abnormality.

Signal processing for making predictive determinations

The techniques may include receiving patient data of patients of a service facility, each patient assigned to a service unit. In addition, the techniques may include inputting patient data into a classification model trained to output classifications. The techniques may include determining a classification for each patient. Moreover, the techniques may include assigning each patient to a patient group based at least in part on a classification common with members of the patient group. The techniques may include determining a recommendation associated with at least one of: (1) servicing a first service resource associated with a third service unit, or (2) procuring a second service resource associated with a fourth service unit. This recommendation can be based at least in part on the classifications. Further, the techniques may include presenting on a user device of a patient care provider for coordinating patient care among service units of the service facility.

Predictive resource management

Techniques are disclosed for coordinating service for a user assigned to a service unit of a service facility. The techniques include receiving user data associated with the user, or resource status data associated with a status of a resource. The techniques further include determining a classification of the user based on the user data, the classification corresponding to a condition or service status of the user. The user may then be assigned to a user group based on the classification of the user. The user group may be associated with a recommendation for treating the user. The resource may also be assigned to a resource group based on the resource status data. The techniques further include generating a visual representation of at least a portion of the service unit based on the assigned user group or assigned resource group, and providing the visual representation to a user device for presentation.

Systems, methods, and machine-readable media to orchestrate process-performing resources are disclosed. Collected data items may correspond to assignments of process-performing resources, and device interactions or data changes that correspond to process or operation performances, conditions of loads. Data items may be processed to identify and map data portions to process-performing resources. Content nodes may be created and linked in a network of content nodes configured according to a computational model that comprises hierarchical orderings of the content nodes using a network data structure. A graphical representation may be formatted to represent the network data structure of the content nodes linked in the network. Metrics of resource performance corresponding to content nodes may be determined. The computational model may be trained using the metrics to create an adapted computational model. Adapted content nodes may be created according to the adapted computational model.

Systems and methods for multi-tier network adaptation and resource orchestration

Growing community of inventors

Nashville, TN, United States of America

Edmund Stephen Jackson