Abstract: A system and method for creating and presenting a rolling severity of illness acuity score are comprised of two phases. The first phase analyzes historical physiologic and clinical data from a hospital's EMR system and discovers data patterns that presage a deleterious outcome. These data patterns are stored in a first database. Once these patterns have been revealed, the second phase involves embedding these patterns into a hospital's EMR, collecting data on new patients in near-real time, matching up this data against the patterns uncovered in Phase One, and producing a score based on how many of these deleterious patterns a patient is exhibiting. This score is updated continuously, weighting the current value higher than previous scores. If this rolling score surpasses a specific value, an alert is generated. The alert, in one example a color-coded signal, is then sent to a mobile device or tablet of the clinicians caring for the patient.

Abstract: The system and method described herein represent a totally new paradigm for assessing risk in critically ill patients by identifying physiologic patterns associated with a poor outcome. It then uses this information to detect these patterns on future patients, sending alerts to clinicians caring for patients with one or more of them. Moreover, the system and method can be extended to outcomes other than mortality, and can be adapted for use in a specific hospital system's EMR. The “code footprint” for embedding the patterns that indicate a trigger is small and thus favorably suited for embedding into an existing clinical decision support system.

Abstract: A system and method for creating and presenting a rolling severity of illness acuity score are comprised of two phases. The first phase analyzes historical physiologic and clinical data from a hospital's EMR system and discovers data patterns that presage a deleterious outcome. These data patterns are stored in a first database. Once these patterns have been revealed, the second phase involves embedding these patterns into a hospital's EMR, collecting data on new patients in near-real time, matching up this data against the patterns uncovered in Phase One, and producing a score based on how many of these deleterious patterns a patient is exhibiting. This score is updated continuously, weighting the current value higher than previous scores. If this rolling score surpasses a specific value, an alert is generated. The alert, in one example a color-coded signal, is then sent to a mobile device or tablet of the clinicians caring for the patient.

Abstract: The system and method described herein represent a totally new paradigm for assessing risk in critically ill patients by identifying physiologic patterns associated with a poor outcome. It then uses this information to detect these patterns on future patients, sending alerts to clinicians caring for patients with one or more of them. Moreover, the system and method can be extended to outcomes other than mortality, and can be adapted for use in a specific hospital system's EMR. The “code footprint” for embedding the patterns that indicate a trigger is small and thus favorably suited for embedding into an existing clinical decision support system.