Abstract: A parallel network architecture connects among providers, subscribers, and storage networks to pull clinical records in an automated and verifiable electronic context. Messages, alerts, or data updates from a healthcare event such as a discharge are received, and example embodiment networks calculate patient clinical data target from the same and query the target for the patient to solicit records for the patient of the event, proximate to the event. The received records may be clinical, with diagnostic and treatment records for the patient, to be automatically delivered back to a subscribing user. Example embodiment networks may improve record quality and data match by verifying a target stores records for the patient, timing queries and requested data to those found most successful for a target, and/or not delivering records that contain errors, no clinical data, are duplicative or unreadable, and/or are not for the patient or healthcare event.

Abstract: Network architectures interface with several real-time streams that control network operation. The network can analyze stream data by storing incoming raw stream data, processing the data, and controlling network operations based thereon. Specific packets, bits, flags, fields, or messages may be targeted, including HL7 ADTs. Data is queued for processing in a FIFO manner to avoid timeline mistakes. The network sets a configuration value based on the data analyzed in this time ordering, and a relational database can be updated in real-time with these values as stream data is incoming. Separate clean-up functions separately manage the database by adjusting the values as they become old. Timeline information is generated and selectively broadcast based on the operations value, potentially with additional information from the targeted data. The network selectively provides this information to users interfaces and displays as dictated by the operating value and information calculated therefrom.

Abstract: A parallel network architecture connects among providers, subscribers, and storage networks to pull clinical records in an automated and verifiable electronic context. Messages, alerts, or data updates from a healthcare event such as a discharge are received, and example embodiment networks calculate patient clinical data target from the same and query the target for the patient to solicit records for the patient of the event, proximate to the event. The received records may be clinical, with diagnostic and treatment records for the patient, to be automatically delivered back to a subscribing user. Example embodiment networks may improve record quality and data match by verifying a target stores records for the patient, timing queries and requested data to those found most successful for a target, and/or not delivering records that contain errors, no clinical data, are duplicative or unreadable, and/or are not for the patient or healthcare event.

Abstract: Network architectures interface with several real-time streams that control network operation. The network can analyze stream data by storing incoming raw stream data, processing the data, and controlling network operations based thereon. Specific packets, bits, flags, fields, or messages may be targeted, including HL7 ADTs. Data is queued for processing in a FIFO manner to avoid timeline mistakes. The network sets a configuration value based on the data analyzed in this time ordering, and a relational database can be updated in real-time with these values as stream data is incoming. Separate clean-up functions separately manage the database by adjusting the values as they become old. Timeline information is generated and selectively broadcast based on the operations value, potentially with additional information from the targeted data. The network selectively provides this information to users interfaces and displays as dictated by the operating value and information calculated therefrom.

Abstract: Network architectures interface with several real-time streams that control network operation. The network can analyze stream data by storing incoming raw stream data, processing the data, and controlling network operations based thereon. Specific packets, bits, flags, fields, or messages may be targeted, including HL7 ADTs. Data is queued for processing in a FIFO manner to avoid timeline mistakes. The network sets a configuration value based on the data analyzed in this time ordering, and a relational database can be updated in real-time with these values as stream data is incoming. Separate clean-up functions separately manage the database by adjusting the values as they become old. Timeline information is generated and selectively broadcast based on the operations value, potentially with additional information from the targeted data. The network selectively provides this information to users interfaces and displays as dictated by the operating value and information calculated therefrom.