Abstract: Systems and methods for predicting changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may receive, from a first source, first information and receive, from a second source, second information. The computing device may generate patient information by linking the first information and the second information using a linkage, corresponding to a patient. The computing device may generate using one or more trained machine learning models, a risk prediction for the patient and a change in risk prediction for the patient corresponding to an intervention. The computing device may output the risk prediction and the change in risk prediction for the patient.

Abstract: Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.

Abstract: Member outreach services to be offered to members of a health plan based on predictions generated by a prediction engine using data associated with the members. The prediction engine can include an ensemble of different base predictive models, and the prediction engine can use a weighted combination of base predictions produced by the different base predictive models to generate a final prediction associated with a member. A representative can attempt to contact the member based on an outreach ticket associated with the final prediction. The representative can also provide ticket feedback associated with the outreach, and the prediction engine can use the ticket feedback to adjust the weights associated with the different base predictive models.

Abstract: Member outreach services to be offered to members of a health plan based on predictions generated by a prediction engine using data associated with the members. The prediction engine can include an ensemble of different base predictive models, and the prediction engine can use a weighted combination of base predictions produced by the different base predictive models to generate a final prediction associated with a member. A representative can attempt to contact the member based on an outreach ticket associated with the final prediction. The representative can also provide ticket feedback associated with the outreach, and the prediction engine can use the ticket feedback to adjust the weights associated with the different base predictive models.

Abstract: Member outreach services to be offered to members of a health plan based on predictions generated by a prediction engine using data associated with the members. The prediction engine can include an ensemble of different base predictive models, and the prediction engine can use a weighted combination of base predictions produced by the different base predictive models to generate a final prediction associated with a member. A representative can attempt to contact the member based on an outreach ticket associated with the final prediction. The representative can also provide ticket feedback associated with the outreach, and the prediction engine can use the ticket feedback to adjust the weights associated with the different base predictive models.

Abstract: A compliance-based application deployment system for a cloud computing environment includes a computing system that executes a service to obtain a computing node compliance level for each of multiple computing nodes configured in the cloud computing environment. When a request is received to provision an application, the service provisions the application on one of the computing nodes having a specified compliance level included in the request.

Abstract: A method for determining policies for cloud orchestrators is provided. The method includes sending from a server to a client device, availability and pricing information pertaining to a plurality of constraints relating to a plurality of resources as managed by a plurality of cloud orchestrators, for selection. The method includes receiving at the server, via the network, the selection from the client device and generating, at the server, a policy for at least one of the plurality of cloud orchestrators, based on the selection.

Abstract: A tenant-based distributed computing environment management system includes a service that is executed on a computing system to allocate a subset of the resources for use by each of a plurality of tenants, and generate a plurality of tags for each resource in the subset in which each tag includes tenant information associated with the tenant to whom the subset of resources are allocated. The service may then identify, for at least one of the tenants, the subset of resources allocated to the tenant, and transmit information associated with the identified subset of resources to a computing device of the tenant.

Abstract: A tenant-based distributed computing environment management system includes a service that is executed on a computing system to allocate a subset of the resources for use by each of a plurality of tenants, and generate a plurality of tags for each resource in the subset in which each tag includes tenant information associated with the tenant to whom the subset of resources are allocated. The service may then identify, for at least one of the tenants, the subset of resources allocated to the tenant, and transmit information associated with the identified subset of resources to a computing device of the tenant.

Abstract: A device may receive a request for messaging from a client device, and may determine a set of available data centers, of multiple of data centers, based on data received on a set of networking ports of the device. The device may configure a set of local queue managers to provide messaging to a set of external queue managers based on determining the set of available data centers. The device may assign the request to a particular local queue manager associated with providing messaging to a particular external queue manager that is associated with a particular data center. The device may provide the request to the particular data center via the particular local queue manager and a particular external queue manager associated with the particular data center, and may determine a reference identifier based on providing the request.

Abstract: A device may receive a request for messaging from a client device, and may determine a set of available data centers, of multiple of data centers, based on data received on a set of networking ports of the device. The device may configure a set of local queue managers to provide messaging to a set of external queue managers based on determining the set of available data centers. The device may assign the request to a particular local queue manager associated with providing messaging to a particular external queue manager that is associated with a particular data center. The device may provide the request to the particular data center via the particular local queue manager and a particular external queue manager associated with the particular data center, and may determine a reference identifier based on providing the request.

Abstract: An approach for providing enhanced data retrieval with improved response time is described. An enhanced data retrieval platform may retrieve data associated with multiple accounts from multiple database records within multiple databases, wherein each database record of the multiple database records is associated with a different type of information. The enhanced data retrieval platform may further create a single database entry for each account of the multiple accounts based on the retrieved data. The enhanced data retrieval platform may also compress the single database entry. Additionally, the enhanced data retrieval platform may store the compressed single database entry in a cache.

Abstract: An approach for providing intelligent load balancing between data centers is described. A load balancing platform monitors a replication delay associated with the replication of data between a first data center and a second data center, and determines to halt access to the data at the second data center if the delay satisfies a threshold delay value. The platform determines to allow access to the data at a second data center if the replication delay does not satisfy the threshold delay value.