User login
Clinical question: Does implementation of an electronic sepsis detection and response system improve patient outcomes?
Background: It is known that interventions such as goal-directed resuscitation and antibiotics can reduce sepsis mortality, but their effectiveness depends on early administration. This fact has increased interest in developing an effective, automated system to improve the timeliness of sepsis detection.
Study design: Pre-implementation/post-implementation with multivariable analysis.
Setting: Urban, academic, multi-hospital healthcare system.
Synopsis: Using the electronic health record (EHR) at the University of Pennsylvania Health System, an automated early warning and response system (EWRS) was developed and implemented to detect patients at risk of clinical deterioration and progression to severe sepsis. The EWRS monitored vital signs and key laboratory results in real time.
Multivariable analysis compared a pre-implementation cohort of adult non-ICU acute care patients admitted from June 6, 2012, to September 4, 2012, to a post-implementation cohort of patients admitted from June 6, 2013, to September 4, 2013.
Hospital and ICU length of stay were similar in both cohorts, and no difference was seen in the proportion of patients transferred to the ICU following the alert; however, transfer to the ICU within six hours became statistically significant after adjustment. All mortality measures were lower in the post-implementation period, but none reached statistical significance. Discharge to home and sepsis documentation were statistically higher in the post-implementation period, but discharge to home lost statistical significance after adjustment.
Although these data are encouraging, the findings are limited, because none of the mortality measures reached statistical significance. Further studies are required before large-scale implementation of such a system can be considered.
Bottom line: An automated prediction tool identified at-risk patients and prompted bedside evaluation resulting in more timely sepsis care, improved documentation, and a trend toward reduced mortality.
Clinical question: Does implementation of an electronic sepsis detection and response system improve patient outcomes?
Background: It is known that interventions such as goal-directed resuscitation and antibiotics can reduce sepsis mortality, but their effectiveness depends on early administration. This fact has increased interest in developing an effective, automated system to improve the timeliness of sepsis detection.
Study design: Pre-implementation/post-implementation with multivariable analysis.
Setting: Urban, academic, multi-hospital healthcare system.
Synopsis: Using the electronic health record (EHR) at the University of Pennsylvania Health System, an automated early warning and response system (EWRS) was developed and implemented to detect patients at risk of clinical deterioration and progression to severe sepsis. The EWRS monitored vital signs and key laboratory results in real time.
Multivariable analysis compared a pre-implementation cohort of adult non-ICU acute care patients admitted from June 6, 2012, to September 4, 2012, to a post-implementation cohort of patients admitted from June 6, 2013, to September 4, 2013.
Hospital and ICU length of stay were similar in both cohorts, and no difference was seen in the proportion of patients transferred to the ICU following the alert; however, transfer to the ICU within six hours became statistically significant after adjustment. All mortality measures were lower in the post-implementation period, but none reached statistical significance. Discharge to home and sepsis documentation were statistically higher in the post-implementation period, but discharge to home lost statistical significance after adjustment.
Although these data are encouraging, the findings are limited, because none of the mortality measures reached statistical significance. Further studies are required before large-scale implementation of such a system can be considered.
Bottom line: An automated prediction tool identified at-risk patients and prompted bedside evaluation resulting in more timely sepsis care, improved documentation, and a trend toward reduced mortality.
Clinical question: Does implementation of an electronic sepsis detection and response system improve patient outcomes?
Background: It is known that interventions such as goal-directed resuscitation and antibiotics can reduce sepsis mortality, but their effectiveness depends on early administration. This fact has increased interest in developing an effective, automated system to improve the timeliness of sepsis detection.
Study design: Pre-implementation/post-implementation with multivariable analysis.
Setting: Urban, academic, multi-hospital healthcare system.
Synopsis: Using the electronic health record (EHR) at the University of Pennsylvania Health System, an automated early warning and response system (EWRS) was developed and implemented to detect patients at risk of clinical deterioration and progression to severe sepsis. The EWRS monitored vital signs and key laboratory results in real time.
Multivariable analysis compared a pre-implementation cohort of adult non-ICU acute care patients admitted from June 6, 2012, to September 4, 2012, to a post-implementation cohort of patients admitted from June 6, 2013, to September 4, 2013.
Hospital and ICU length of stay were similar in both cohorts, and no difference was seen in the proportion of patients transferred to the ICU following the alert; however, transfer to the ICU within six hours became statistically significant after adjustment. All mortality measures were lower in the post-implementation period, but none reached statistical significance. Discharge to home and sepsis documentation were statistically higher in the post-implementation period, but discharge to home lost statistical significance after adjustment.
Although these data are encouraging, the findings are limited, because none of the mortality measures reached statistical significance. Further studies are required before large-scale implementation of such a system can be considered.
Bottom line: An automated prediction tool identified at-risk patients and prompted bedside evaluation resulting in more timely sepsis care, improved documentation, and a trend toward reduced mortality.