Abstract: The described implementations relate to patient identification. One implementation can acquire binary biometric data and structured data from a patient. This implementation can compare the acquired patient binary biometric data and structured data to binary biometric data and structured data associated with patient files in an electronic master patient index.

Abstract: One or more techniques and/or systems are disclosed that provide for document retrieval where a user can identify key attributes of potential target documents that are desirable (e.g., have a particular semantic content for the user). Further, relevant documents that comprise the desired semantic content can be retrieved. Additionally, the user can provide feedback on the retrieved documents, for example, based on key semantic concepts found in the documents, and the input can be used to update the classification. For example, this process can be iterated to improve the retrieval and precision of documents found through machine learning techniques.

Abstract: The described implementations relate to patient identification. One implementation can acquire binary biometric data and structured data from a patient. This implementation can compare the acquired patient binary biometric data and structured data to binary biometric data and structured data associated with patient files in an electronic master patient index.

Abstract: One or more techniques and/or systems are disclosed that provide for document retrieval where a user can identify key attributes of potential target documents that are desirable (e.g., have a particular semantic content for the user). Further, relevant documents that comprise the desired semantic content can be retrieved. Additionally, the user can provide feedback on the retrieved documents, for example, based on key semantic concepts found in the documents, and the input can be used to update the classification. For example, this process can be iterated to improve the retrieval and precision of documents found through machine learning techniques.

Abstract: A dynamically adaptable data connector is provided. The connector can dynamically adapt to connect disparate systems regardless of system initiation protocol, data type, data format, transfer protocol, content encoding, etc. Accordingly, data can be converted or otherwise configured to effectuate communication between disparate systems that were not originally designed for such communication. As well, the connector can cause the raw data (e.g., unmodified data) to be maintained for subsequent use if necessary or desired.

Abstract: Systems and methods for providing automated access to embedded decision support are disclosed. More specifically, the subject innovation provides a method for encoding decision support tools and for using those tools to adaptively provide decision support. Data can be provided to an engine in real-time and, if needed or desired, can be combined with additional existing data (e.g., from a store or entered by a user). The engine can process that data using its library of decision support tools and take action based on the results of that processing. Additionally, contextual awareness of decision support for a particular user and a specific working environment can be established and employed to trigger and/or select appropriate action.

Abstract: A system and/or method that can maintain a master patient index in a health care context is disclosed. The index enables a health-care professional or entity to collect, access and/or retrieve a cohort of health-related data elements (e.g., patient information) from a variety of sources and of differing formats. The cohort of data elements can be displayed such that a health care entity can easily toggle through the patient-related data without a need to access format-specific applications.