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 patient anonymizing system includes a plurality of hashing appliances and data sources. Each appliance receives medical records containing at least confidential protected health information (PHI). A salt value is appended to each confidential PHI, and a hash is generated, which replaces the confidential PHI to generate an anonymized record. A master patient index server aggregates the anonymized records. A vector and cluster matching engine determines if the anonymized record matches a unique patient identifier corresponding to a second anonymized record. A comparison vector is generated by comparing hash values of the confidential PHI with hash values in the second anonymized record, and is crossed with a confidence vector having weights based on match conditions. This produces a match confidence level, which is compared to a threshold. If the threshold is met, the anonymized record is mapped to the unique patient identifier associated with the second record.

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 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 patient anonymizing system includes a plurality of hashing appliances and data sources. Each appliance receives medical records containing at least confidential protected health information (PHI). A salt value is appended to each confidential PHI, and a hash is generated, which replaces the confidential PHI to generate an anonymized record. A master patient index server aggregates the anonymized records. A vector and cluster matching engine determines if the anonymized record matches a unique patient identifier corresponding to a second anonymized record. A comparison vector is generated by comparing hash values of the confidential PHI with hash values in the second anonymized record, and is crossed with a confidence vector having weights based on match conditions. This produces a match confidence level, which is compared to a threshold. If the threshold is met, the anonymized record is mapped to the unique patient identifier associated with the second record.

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.