Abstract: Techniques and architectures for clustering health care providers based on data regarding services that have previously been provided by health care providers are described herein. For example, the techniques and architectures may receive data from devices of health care providers regarding services that have been provided by the health care providers to patients. The techniques and architectures may perform various machine learning techniques, including clustering, to categorize health care providers into multiple clusters based on the services that were provided. Health care providers that lie within a cluster may be associated with a category of the cluster. While health care providers that lie outside a cluster, and have designated themselves as being associated with or are otherwise associated with a practice area that is associated with the cluster, may be flagged as potentially practicing outside an area that is designated by or otherwise associated with the health care providers.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for complex care tools are disclosed. For example, user data associated with users that have benefited from enrollment in a complex care program may be utilized to predict additional users that are also likely to benefit from enrollment. The presently disclosed systems and methods may determine reductions in cost profiles associated with enrolled users that exceed a reduction value, where the reduction value indicates the intended benefit of the complex care program has been achieved. The system may then associate user data of such users with the reduction value. As such, the system may utilize this data to predict additional users that are likely to benefit from enrollment. In this way, the system may engage such users to promote enrollment so that care costs may be reduced.

Abstract: Techniques and architectures for clustering health care providers based on data regarding services that have previously been provided by health care providers are described herein. For example, the techniques and architectures may receive data from devices of health care providers regarding services that have been provided by the health care providers to patients. The techniques and architectures may perform various machine learning techniques, including clustering, to categorize health care providers into multiple clusters based on the services that were provided. Health care providers that lie within a cluster may be associated with a category of the cluster. While health care providers that lie outside a cluster, and have designated themselves as being associated with or are otherwise associated with a practice area that is associated with the cluster, may be flagged as potentially practicing outside an area that is designated by or otherwise associated with the health care providers.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.

Abstract: Systems and methods for models utilizing siloed data are disclosed. For example, data stored with and/or available to one or more systems may be siloed such that it may not be aggregated and/or shared with other systems. The presently-disclosed systems and methods generate and utilize predictive layers and models to allow each system to predict outcomes using its own data and then models are shared between systems to allow each associated system to gain the benefits of the data of other systems without aggregating such data or otherwise sharing the data.