Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer.

Abstract: A mechanism is provided to implement a healthcare analytics management system. A healthcare analytics development sub-system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. A model deployment module of a healthcare analytics operation sub-system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer. Responsive to detecting a performance deviation of the deployed healthcare analytics model from the set of business needs, a model feedback module determines improvement needs for the healthcare analytics model. The model feedback module feeds the improvement needs back to the healthcare analytics development sub-system. The healthcare analytics development sub-system customizes the healthcare analytics model based on the improvement needs.

Abstract: A mechanism is provided to implement a healthcare analytics management system. A healthcare analytics development sub-system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. A model deployment module of a healthcare analytics operation sub-system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer. Responsive to detecting a performance deviation of the deployed healthcare analytics model from the set of business needs, a model feedback module determines improvement needs for the healthcare analytics model. The model feedback module feeds the improvement needs back to the healthcare analytics development sub-system. The healthcare analytics development sub-system customizes the healthcare analytics model based on the improvement needs.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer.

Abstract: A method for estimating a risk associated with a project includes preparing a plurality of data models, where each of the plurality of data models examines a different dimension of the project, classifying each of the plurality of data models to produce a plurality of prediction models, where each of the plurality of prediction models is defined by a plurality of quality metrics, and where the plurality of quality metrics includes a preliminary estimate of the risk and a measure of confidence in the preliminary estimate, and computing a refined estimate of the risk based on a quality of the plurality of quality metrics.

Abstract: A method for estimating a risk associated with a project includes preparing a plurality of data models, where each of the plurality of data models examines a different dimension of the project, classifying each of the plurality of data models to produce a plurality of prediction models, where each of the plurality of prediction models is defined by a plurality of quality metrics, and where the plurality of quality metrics includes a preliminary estimate of the risk and a measure of confidence in the preliminary estimate, and computing a refined estimate of the risk based on a quality of the plurality of quality metrics.

Abstract: Ranking a plurality of objects includes obtaining an initial set of data relating to the objects, generating an initial set of estimates based on the initial set of data, wherein the initial set of estimates includes, for each of the objects, an initial estimated change in performance and an initial estimated likelihood of decline in the performance, incrementally and dynamically refining the initial set of estimates in accordance with a new set of data from new data sources and relating to the objects to produce a refined set of estimates, wherein the refined set of estimates includes, for each of the objects, a refined estimated change in performance and a refined estimated likelihood of decline in the performance, without modifying or replacing a system used to generate the initial set of estimates, and generating a list that ranks the objects according to the refined set of estimates.

Abstract: A method, system, and/or computer program product predicts trouble during a transition phase of a current project. Past operational data and past trouble data for past healthy projects and past troubled projects before transition phases is collected. A trouble correlation between the past operational data and the past trouble data is then determined. A set of key metrics that describes a current health of a current project before transitioning from a current phase to a future phase is defined, and values of the set of key metrics for the current project are identified. Identified values of the set of key metrics for the current project are compared to the past operational data for past healthy and troubled projects in order to generate a pattern comparison. This pattern comparison, along with the trouble correlation, is used to generate a predicted quality of transition for the current project.

Abstract: A computer implemented method, system, and/or computer program product predicts a success of a currently proposed project. Responsive answers to a survey questionnaire for a currently proposed project are received and used to create a predicted success score for the currently proposed project, based on a pattern of responsive answers to a predetermined combination of questions from the survey questionnaire. The predicted success score for the currently proposed project is compared with and matched to actual success scores from prior projects. If the percentage of prior projects that retained their success scores through completion exceeds a predetermined accuracy threshold, then the currently proposed project is approved for implementation.

Abstract: A computer implemented method, system, and/or computer program product predicts a success of a currently proposed project. Responsive answers to a survey questionnaire for a currently proposed project are received and used to create a predicted success score for the currently proposed project, based on a pattern of responsive answers to a predetermined combination of questions from the survey questionnaire. The predicted success score for the currently proposed project is compared with and matched to actual success scores from prior projects. If the percentage of prior projects that retained their success scores through completion exceeds a predetermined accuracy threshold, then the currently proposed project is approved for implementation.

Abstract: A computer implemented method, system, and/or computer program product predicts a success of a currently proposed project. Responsive answers to a survey questionnaire for a currently proposed project are received and used to create a predicted success score for the currently proposed project, based on a pattern of responsive answers to a predetermined combination of questions from the survey questionnaire. The predicted success score for the currently proposed project is compared with and matched to actual success scores from prior projects. If the percentage of prior projects that retained their success scores through completion exceeds a predetermined accuracy threshold, then the currently proposed project is approved for implementation.

Abstract: A method, system, and/or computer program product predicts trouble during a transition phase of a current project. Past operational data and past trouble data for past healthy projects and past troubled projects before transition phases is collected. A trouble correlation between the past operational data and the past trouble data is then determined. A set of key metrics that describes a current health of a current project before transitioning from a current phase to a future phase is defined, and values of the set of key metrics for the current project are identified. Identified values of the set of key metrics for the current project are compared to the past operational data for past healthy and troubled projects in order to generate a pattern comparison. This pattern comparison, along with the trouble correlation, is used to generate a predicted quality of transition for the current project.

Abstract: A computer implemented method, system, and/or computer program product predicts the profitability of a current project. Historical data from the current project represents profit data points at predetermined intervals of time during the current project. If a linear regression model of the historical data does not adequately describe the historical data, then a polynomial regression model to describe the historical data is generated. If the polynomial model does not adequately describe the historical data, then another project that is similar to the current project is located. This other project has its own set of historical data. If the historical data from the other project fits the polynomial regression model created from the historical data of the current project, then the polynomial regression model is trusted to accurately predict the profitability of the current project, even though the current project's historical data did not adequately fit the polynomial regression model.

Abstract: A computer implemented method, system, and/or computer program product predicts a success of a currently proposed project. Responsive answers to a survey questionnaire for a currently proposed project are received and used to create a predicted success score for the currently proposed project, based on a pattern of responsive answers to a predetermined combination of questions from the survey questionnaire. The predicted success score for the currently proposed project is compared with and matched to actual success scores from prior projects. If the percentage of prior projects that retained their success scores through completion exceeds a predetermined accuracy threshold, then the currently proposed project is approved for implementation.