Abstract: A method and system for assessing the risk of a cardiac event in a patient which utilizes real-time and historical data from Electronic Medical Record (EMR) systems is described. A risk of a cardiac event is estimated, in real-time or near-real-time, for a patient who is currently in a hospital emergency department. Batch data for one or more past patients is extracted from EMRs into a machine learning model. Using the machine learning model, a risk level for one or more past patients is calculated. A real-time database is constructed from streams of real-time Health Level 7 (HL7) clinical data, wherein at least one stream of real-time HL7 clinical data is associated with the current patient, and a risk prediction is estimated by joining the calculated risk level for the patient in the machine learning model with the real-time HL7 clinical data from the patient.

Abstract: The present system pertains to event tracking, involving the acquisition of event information, generation of compound hashes, and formation of a search data structure. Event information includes time-and-geolocation data and topic data for each event. Compound hashes, comprising a spacetime hash and a topic hash for each event, are generated based on this information. A search data structure is formed using these compound hashes, enabling the grouping of spacetime hashes and structuring of topic hashes. Upon receiving a query, the system executes a search based on the query's compound hash or parameters, returning matching event content. The system can also generate semantic descriptions of relevant events using a narrative generation model and predict event relevance using a preferential learning model.

Abstract: A user interface for a physician or other health care provider to follow up with high-risk patients recently discharged from the emergency department (ED) and update the patient's health care record using the physician or provider's mobile or end user device is described. The user interface is generated after extracting patient detail batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database or other patient information database into a medical activity tracking batch database, and constructing a real-time database from streams of real-time Health Level 7 (HL7) clinical administrative data, wherein at least one stream of real-time HL7 clinical administrative data is associated with at least one high-risk patient in a recent medical encounter and at least one stream of real-time HL7 data comprises daily schedule information associating at least one clinician with the at least one high-risk patient.

Abstract: A method and system for assessing the risk of a cardiac event in a patient which utilizes real-time and historical data from Electronic Medical Record (EMR) systems is described. A risk of a cardiac event is estimated, in real-time or near-real-time, for a patient who is currently in a hospital emergency department. Batch data for one or more past patients is extracted from EMRs into a machine learning model. Using the machine learning model, a risk level for one or more past patients is calculated. A real-time database is constructed from streams of real-time Health Level 7 (HL7) clinical data, wherein at least one stream of real-time HL7 data is associated with the current patient, and a risk prediction is estimated by joining the calculated risk level for the patient in the machine learning model with the real-time HL7 clinical data from the patient.

Abstract: A user interface for a physician or other health care provider to follow up with high-risk patients recently discharged from the emergency department (ED) and update the patient's health care record using the physician or provider's mobile or end user device is described. The user interface is generated after extracting patient detail batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database or other patient information database into a medical activity tracking batch database, and constructing a real-time database from streams of real-time Health Level 7 (HL7) clinical administrative data, wherein at least one stream of real-time HL7 clinical administrative data is associated with at least one high-risk patient in a recent medical encounter and at least one stream of real-time HL7 data comprises daily schedule information associating at least one clinician with the at least one high-risk patient.

Abstract: A user interface for a physician or other health care provider to follow up with high-risk patients recently discharged from the emergency department (ED) and update the patient's health care record using the physician or provider's mobile or end user device is described. The user interface is generated after extracting patient detail batch information at a pre-defined time interval for one or more past patients from an electronic medical records (EMR) database or other patient information database into a medical activity tracking batch database, and constructing a real-time database from streams of real-time Health Level 7 (HL7) clinical administrative data, wherein at least one stream of real-time HL7 clinical administrative data is associated with at least one high-risk patient in a recent medical encounter and at least one stream of real-time HL7 data comprises daily schedule information associating at least one clinician with the at least one high-risk patient.

Abstract: A method and system for assessing the risk of a cardiac event in a patient which utilizes real-time and historical data from Electronic Medical Record (EMR) systems is described. A risk of a cardiac event is estimated, in real-time or near-real-time, for a patient who is currently in a hospital emergency department. Batch data for one or more past patients is extracted from EMRs into a machine learning model. Using the machine learning model, a risk level for one or more past patients is calculated. A real-time database is constructed from streams of real-time Health Level 7 (HL7) clinical data, wherein at least one stream of real-time HL7 clinical data is associated with the current patient, and a risk prediction is estimated by joining the calculated risk level for the patient in the machine learning model with the real-time HL7 clinical data from the patient.