Abstract: A system comprises a toolset to render a protocol, such as an industry clinical treatment guideline, into a process map. The process map includes a workflow having a time-based series of steps determined from the protocol. The system performs recursive matching to match events in electronic medical records (EMRs) to nodes in threads in the protocol map to determine protocol compliance. The EMRs include unstructured data, and specialized query objects may be generated to retrieve relevant data for the process map from the unstructured EMR data.

Abstract: A system for anonymizing and aggregating protected information (PI) from a plurality of data sources includes a master index server coupled to a data repository. The master index server receives an anonymized records associated with an individual from a plurality of data hashing appliances. The system includes a cluster matching engine that applies a plurality of rules to hashed data elements of the received record for comparing hashed data elements of the record with hashed data elements of a plurality of clusters of anonymized records associated with different individuals stored in the data repository to determine whether the individual associated with the received record corresponds to an individual associated with one of the clusters of anonymized records. When a match is found, the cluster matching engine adds the received record to the cluster of anonymized records associated with that individual.

Abstract: A system comprises a toolset to render a protocol, such as an industry clinical treatment guideline, into a process map. The process map includes a workflow having a time-based series of steps determined from the protocol. The system performs recursive matching to match events in electronic medical records (EMRs) to nodes in threads in the protocol map to determine protocol compliance. The EMRs include unstructured data, and specialized query objects may be generated to retrieve relevant data for the process map from the unstructured EMR data.

Abstract: A system for anonymizing and aggregating protected information (PI) from a plurality of data sources includes a master index server coupled to a data repository. The master index server receives an anonymized records associated with an individual from a plurality of data hashing appliances. The system includes a cluster matching engine that applies a plurality of rules to hashed data elements of the received record for comparing hashed data elements of the record with hashed data elements of a plurality of clusters of anonymized records associated with different individuals stored in the data repository to determine whether the individual associated with the received record corresponds to an individual associated with one of the clusters of anonymized records. When a match is found, the cluster matching engine adds the received record to the cluster of anonymized records associated with that individual.

Abstract: A clinical quality analytics system may include a data storage to store electronic medical record (EMR) data. The system may map events from the EMR data to a process map through a recursive matching process. The mapping may include recursively matching the events to nodes in threads in a map based on event times and thread times. One of the recursions may be selected as a best fit based on metrics determined for the recursions.

Abstract: A system for anonymizing and aggregating protected information (PI) from a plurality of data sources includes a master index server coupled to a data repository. The master index server receives an anonymized records associated with an individual from a plurality of data hashing appliances. The system includes a cluster matching engine that applies a plurality of rules to hashed data elements of the received record for comparing hashed data elements of the record with hashed data elements of a plurality of clusters of anonymized records associated with different individuals stored in the data repository to determine whether the individual associated with the received record corresponds to an individual associated with one of the clusters of anonymized records. When a match is found, the cluster matching engine adds the received record to the cluster of anonymized records associated with that individual.

Abstract: A system for anonymizing and aggregating protected information (PI) from a plurality of data sources includes a master index server coupled to a data repository. The master index server receives an anonymized records associated with an individual from a plurality of data hashing appliances. The system includes a cluster matching engine that applies a plurality of rules to hashed data elements of the received record for comparing hashed data elements of the record with hashed data elements of a plurality of clusters of anonymized records associated with different individuals stored in the data repository to determine whether the individual associated with the received record corresponds to an n) individual associated with one of the clusters of anonymized records. When a match is found, the cluster matching engine adds the received record to the cluster of anonymized records associated with that individual.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing a data model, a data file and user input to provide a populated data model, the populated data model including: a data content table having one or more navigation columns and one or more content columns, at least one navigation column including one or more key values of a key node, and at least one content column including data values of a plurality of data values, and a foreign key table having one or more navigation columns and one or more relationship columns, at least one navigation column including one or more key values of a foreign key node, and the one or more relationship columns associating at least one key node of the data content table to a respective foreign key node.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, can be implemented to perform actions including receiving input data defining a predictive model, the predictive model including multiple features. The actions further include weighting the predictive model iteratively for each feature, using actual data including values for each feature for multiple entities within a population, by iteratively adjusting a current weight for the feature by a momentum until the momentum equals zero, the momentum being iteratively adjusted by a momentum factor based on whether a model score improves, the model score being calculated based on the actual data. The actions further include calculating a value score for the entity using the weighted predictive model.

Abstract: A clinical quality analytics system may include a data storage to store electronic medical record (EMR) data. The system may map events from the EMR data to a process map through a recursive matching process. The mapping may include recursively matching the events to nodes in threads in a map based on event times and thread times. One of the recursions may be selected as a best fit based on metrics determined for the recursions.