The estimated annual cost of treating patients with sepsis is approximately $20 billion in the United States, making sepsis the costliest medical condition in this country. Each year, an estimated 750,000 people die from sepsis in the U.S. But according to an article in the Journal of the American Medical Informatics Association, a hospital in Huntsville, Alabama, has been able to reduce sepsis-related deaths by 53% through a program focused on staff education and an electronic surveillance system.
The authors created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and to deliver specific decision support to the point of care using a mobile application.
All patients admitted to study units in Huntsville Hospital were screened according to the Institute for Healthcare Improvement’s Surviving Sepsis Guidelines using real-time electronic surveillance. Discrete data elements were added to patients’ electronic health records (EHRs) to achieve high sensitivity and specificity for automated sepsis screening. Over a period of 10 months, nurses received mobile alerts for all positive sepsis screenings as well as severe sepsis and shock alerts through their mobile devices or desktop computers, and advice was given for early goal-directed therapy.
The training and implementation phase of the study was from October 1, 2013, to February 28, 2014. The implementation for the computerized sepsis surveillance and mobile alerting system was complete on March 6, 2014. Data from patients admitted to the study units were collected from March 1 to December 31, 2014. In addition, hospital data on the baseline incidence of sepsis and other health-quality indicators were gathered from a control period (January 1, 2011, to September 30, 2013).
The study’s primary outcome, sepsis mortality, decreased by 53% (P = 0.03). In addition, the 30-day readmission rate was reduced from 19% during the control period to 13% during the study period (P = 0.05). No significant changes in length of hospital stay were noted.
Last year, in similar research, investigators at Johns Hopkins University developed an early-warning system for septic shock. The computer-based method correctly predicted septic shock in 85% of cases, without increasing the false positive rates associated with current screening methods. The research was published in Science Translational Medicine.
The study drew on EHRs of 16,234 patients admitted to intensive care units—including medical, surgical, and cardiac units—at Boston’s Beth Israel Deaconess Medical Center from 2001 to 2007. The researchers created an algorithm that combined 27 factors into a Targeted Real-Time Early Warning Score (TREWScore) measuring the risk of septic shock.
The researchers said that the algorithm could be programmed into EHR systems to alert doctors and nurses about a patient at risk of septic shock. Moreover, EHR systems could be set up to convey alerts to clinicians via pagers or cell phones at regular intervals.
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