Abstract: Systems, methods, and computer storage media are provided for determining a patient's severity of illness score (pSIS) for a patient admitted to an acute care facility. Data corresponding to physiologic components is received from an electronic medical record associated with a patient admitted to an acute care healthcare facility. The physiologic components include vital sign measurements and laboratory tests. Weights are assigned to a minimum, median, and maximum measured values for each vital sign. Weights are assigned to minimum and maximum values for each laboratory test. The weights are derived based on a deviation from normal within a time period. A pSIS is determined by summing the weights. Additional data corresponding to physiologic components may be received from the electronic medical record. The additional data may be utilized to update the weights and determine a patient's updated pSIS that may be utilized to track a progress of the patient.

Abstract: Systems, methods, and computer storage media are provided for determining a patient's severity of illness score (pSIS) for a patient admitted to an acute care facility. Data corresponding to physiologic components is received from an electronic medical record associated with a patient admitted to an acute care healthcare facility. The physiologic components include vital sign measurements and laboratory tests. Weights are assigned to a minimum, median, and maximum measured values for each vital sign. Weights are assigned to minimum and maximum values for each laboratory test. The weights are derived based on a deviation from normal within a time period. A pSIS is determined by summing the weights. Additional data corresponding to physiologic components may be received from the electronic medical record. The additional data may be utilized to update the weights and determine a patient's updated pSIS that may be utilized to track a progress of the patient.

Abstract: Systems, methods, and computer storage media are provided for determining a patient's severity of illness score (pSIS) for a patient admitted to an acute care facility. Data corresponding to physiologic components is received from an electronic medical record associated with a patient admitted to an acute care healthcare facility. The physiologic components include vital sign measurements and laboratory tests. Weights are assigned to a minimum, median, and maximum measured values for each vital sign. Weights are assigned to minimum and maximum values for each laboratory test. The weights are derived based on a deviation from normal within a time period. A pSIS is determined by summing the weights. Additional data corresponding to physiologic components may be received from the electronic medical record. The additional data may be utilized to update the weights and determine a patient's updated pSIS that may be utilized to track a progress of the patient.

Abstract: Systems, methods, and computer storage media are provided for determining a patient's severity of illness score (pSIS) for a patient admitted to an acute care facility. Data corresponding to physiologic components is received from an electronic medical record associated with a patient admitted to an acute care healthcare facility. The physiologic components include vital sign measurements and laboratory tests. Weights are assigned to a minimum, median, and maximum measured values for each vital sign. Weights are assigned to minimum and maximum values for each laboratory test. The weights are derived based on a deviation from normal within a time period. A pSIS is determined by summing the weights. Additional data corresponding to physiologic components may be received from the electronic medical record. The additional data may be utilized to update the weights and determine a patient's updated pSIS that may be utilized to track a progress of the patient.

Abstract: Systems, methods, and computer storage media are provided for determining a patient's severity of illness score (pSIS) for a patient admitted to an acute care facility. Data is received corresponding to medical support a patient is receiving from an electronic medical record associated with the patient admitted to an acute care healthcare facility. The data is associated with support variables present in a patient upon admission or administered to the patient within an initial time period from admission. Support variables include both pharmaceutical-type variables and medical device-type variables. Weights are assigned to each support variable associated with the patient. A patient's pSIS is determined by summing the weights. The pSIS accounts for effects that medical support a patient is receiving has on the patient's physiology.

Abstract: Systems, methods, and computer storage media are provided for determining a physiologic severity of illness score (pSIS) for a patient admitted to an acute care healthcare facility. Data corresponding to physiologic components is received from an electronic medical record associated with a patient admitted to an acute care healthcare facility. The data is not required to correspond to physiologic components collected in or associated with an intensive care unit. Weights are assigned to each physiologic component. The weights are derived based on a deviation from normal. A physiologic severity of illness score (pSIS) is for the patient is determined by summing the weights. Additional data corresponding to the physiologic components may be received from the electronic medical record. The additional data may be utilized to update the weights and determine an updated pSIS for the patient which may be utilized to track a progress of the patient.

Abstract: Systems and methods for rendering a graphic that describes multiple dimensions of performance for a subject intensive care unit (ICU) over a predefined period of time are provided. One method involves measuring the performance of the subject ICU against various metrics and displaying the measured performance as a graphic constructed of data strips placed adjacent to a 2-dimensional plot. The lengths of the data strips visually represent a percentage of patients that did not receive active treatment in the subject ICU, a rate of readmission to an ICU, and a median of the acute physiological scores (APS's) extracted from patients admitted to the subject ICU, respectively. The 2-dimensional plot includes an X-axis that represents a ratio of a hospital predicted mortality rate value over the measured mortality rate, while a Y-axis represents a difference between a predicted mean length of stay value and the measured mean length of stay value.

Abstract: Computerized methods in a clinical computing environment for predicting mortality in critically ill patients, that is, patients admitted to Intensive Care Units, are provided. In accordance with embodiments hereof, at least two distinctly different mortality prediction models (e.g., the Acute Physiology and Chronic Health Evaluation (APACHE®) model and the Mortality Probability Model at Admission (MPM0) are utilized in a multi-stage fashion to determine, with better accuracy than may be provided by either mortality prediction model alone, the probability of mortality for critically ill adult patients.

Abstract: Systems and methods for rendering a graphic that describes multiple dimensions of performance for a subject intensive care unit (ICU) over a predefined period of time are provided. One method involves measuring the performance of the subject ICU against various metrics and displaying the measured performance as a graphic constructed of data strips placed adjacent to a 2-dimensional plot. The lengths of the data strips visually represent a percentage of patients that did not receive active treatment in the subject ICU, a rate of readmission to an ICU, and a median of the acute physiological scores (APS's) extracted from patients admitted to the subject ICU, respectively. The 2-dimensional plot includes an X-axis that represents a ratio of a hospital predicted mortality rate value over the measured mortality rate, while a Y-axis represents a difference between a predicted mean length of stay value and the measured mean length of stay value.