Abstract: Methods, systems, and apparatuses to improve the handling of exceptions during the retrieval and processing of health records from various data sources are provided. During the retrieval and processing of health records, exceptions to typical behavior are recorded with context at the data extraction protocol level, at the health record level and at the level of elements with the document. Accordingly, insights may be developed and configurations, rules, or coding changes, based on the detected exceptions may be proposed. In some instances, an operator may be notified about the exceptions such that the operator may act on the insight. In some instances, the processing of extracted records (documents, messages) may be deferred until the operator has made appropriate changes to configuration, rules, or code. In some instances, the system may supplement and/or replace the operator with machine learning engines that act on the developed insights.

Abstract: Methods, systems, and apparatuses to improve the handling of exceptions during the retrieval and processing of health records from various data sources are provided. During the retrieval and processing of health records, exceptions to typical behavior are recorded with context at the data extraction protocol level, at the health record level and at the level of elements with the document. Accordingly, insights may be developed and configurations, rules, or coding changes, based on the detected exceptions may be proposed. In some instances, an operator may be notified about the exceptions such that the operator may act on the insight. In some instances, the processing of extracted records (documents, messages) may be deferred until the operator has made appropriate changes to configuration, rules, or code. In some instances, the system may supplement and/or replace the operator with machine learning engines that act on the developed insights.

Abstract: The present disclosure is directed to systems and methods for medical health document retrieval and network expansion. In a method, a computing system receives a request for a health record, obtains contextual information associated with the request, determines a source of health record information for the health record based on the request and contextual information, retrieves the health record information from the source, and assembles the health record using the retrieved health record information. In some embodiments, the method may be recursive and analyze the retrieved health record information for references to additional health record information that may not be present in the health record.

Abstract: A system, method, and computer-readable medium for dynamically partitioning a directory list to limit recipients to an appropriate context. More specifically, in certain embodiments the dynamic partitioning includes a first component which describes a priori categories. In certain embodiments, the a priori categories could include “inner circle” or “trusted advisors” categories. Other categories could include “friends of the DL” who only want occasional but relevant messages. In certain embodiments, these categories are created by a DL owner. However, in certain embodiments, members (e.g., recipients) could also change their own category. For example, a DL could have categories: All, Required participants, FYI participants, Managers.

Abstract: A system, method, and computer-readable medium for dynamically partitioning a directory list to limit recipients to an appropriate context. More specifically, in certain embodiments the dynamic partitioning includes a first component which describes a priori categories. In certain embodiments, the a priori categories could include “inner circle” or “trusted advisors” categories. Other categories could include “friends of the DL” who only want occasional but relevant messages. In certain embodiments, these categories are created by a DL owner. However, in certain embodiments, members (e.g., recipients) could also change their own category. For example, a DL could have categories: All, Required participants, FYI participants, Managers.