Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise.