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Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency
Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units.
Sepsis management is a challenge for hospitals nationwide, as severe sepsis carries high mortality rates and costs the US healthcare system billions of dollars each year. It has been shown that early intervention for patients with severe sepsis and septic shock is associated with higher rates of survival. The Cape Regional Medical Center (CRMC) aimed to improve sepsis-related patient outcomes through a revised sepsis management approach.
In collaboration with Dascena, CRMC formed a quality improvement team to implement a machine learning-based sepsis prediction algorithm to identify patients with sepsis earlier. Previously, CRMC assessed all patients for sepsis using twice-daily systemic inflammatory response syndrome screenings, but desired improvements. The quality improvement team worked to implement a machine learning-based algorithm, collect and incorporate feedback, and tailor the system to current hospital workflow.
Relative to the pre-implementation period, the post-implementation period sepsis-related in-hospital mortality rate decreased by 60.24%, sepsis-related hospital length of stay decreased by 9.55% and sepsis-related 30-day readmission rate decreased by 50.14%.
The machine learning-based sepsis prediction algorithm improved patient outcomes at CRMC.
(McCoy A, Das R Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Qual 2017;6:e000158. doi: 10.1136/bmjoq-2017-000158 )
Source: bmjopenquality.bmj.com
sepsis learning-based crmc sepsis-related prediction machine decreased length
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