Abstract: A health-state detection system configured to receive medical measurement data for one or more patients; a care manager interface for communicating with care managers; a user interface for receiving rules related to each type of medical measurement data; processors for parametrize the rules to generate a set of parameterized rules that define shifts, drifts and trends in the medical measurement data, determine a baseline for each type of medical measurement data, execute the parameterized rules for newly received medical measurement data against the baseline to detect shifts, drifts or trends in the medical measurement data, determine the magnitudes and directions of the detected shifts, drifts or trends, correlate the magnitudes and directions of the shifts, drifts, or trends detected with changes in the health state of one or more patients, and provide customizable notifications, alerts to care managers based at least in the past on the detected health state changes.

Abstract: Current patients in an emergency department of a hospital are described according to their quantity, their triage classification levels, and their wait times to calculate a current patient backlog. A sum of weight-adjusted triage classification levels of all of the current patients is calculated. Current patient arrival rates in the emergency department are tracked to calculate a current change in patient arrival rates, which are compared with historical changes in patient arrival rates. A size of an imminent influx of arriving patients into the emergency department is then predicted based on the current patient backlog, the sum of weight-adjusted triage classification levels of patients currently in the emergency department, and the current change in patient arrival rates.

Abstract: A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.

Abstract: Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.

Abstract: An initial triage level classification for a latest patient to arrive at an emergency department (ED) is received. Availability levels of resources needed to treat the latest patient are electronically collected, along with triage level classifications for all other patients currently in the ED. The initial triage level classification of the latest patient is adjusted upward or downward based on the availability levels of resources needed to treat the latest patient and based on the triage level classifications for the patients in the ED. The triage level classifications for all patients currently in the ED are summed up. If a sum of all triage level classifications exceeds a first predetermined threshold, other resources are reallocated in order to provide the resources needed to treat the latest patient to arrive at the ED. If the sum of all triage level classifications exceeds a second predetermined threshold, then a disaster plan is implemented.

Abstract: Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.

Abstract: A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.

Abstract: An initial triage level classification for a latest patient to arrive at an emergency department (ED) is received. Availability levels of resources needed to treat the latest patient are electronically collected, along with triage level classifications for all other patients currently in the ED. The initial triage level classification of the latest patient is adjusted upward or downward based on the availability levels of resources needed to treat the latest patient and based on the triage level classifications for the patients in the ED. The triage level classifications for all patients currently in the ED are summed up. If a sum of all triage level classifications exceeds a first predetermined threshold, other resources are reallocated in order to provide the resources needed to treat the latest patient to arrive at the ED. If the sum of all triage level classifications exceeds a second predetermined threshold, then a disaster plan is implemented.

Abstract: Current patients in an emergency department of a hospital are described according to their quantity, their triage classification levels, and their wait times to calculate a current patient backlog. A sum of weight-adjusted triage classification levels of all of the current patients is calculated. Current patient arrival rates in the emergency department are tracked to calculate a current change in patient arrival rates, which are compared with historical changes in patient arrival rates. A size of an imminent influx of arriving patients into the emergency department is then predicted based on the current patient backlog, the sum of weight-adjusted triage classification levels of patients currently in the emergency department, and the current change in patient arrival rates.