The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden (JHEOR).
Background: Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties.
However, the economic consequences of forecasting the onset of sepsis are unknown.
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The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare
sector) resulting in a cost saving of €1009 per ICU patient.
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Conclusions: A sepsis prediction algorithm such as NAVOY® Sepsis reduces the
cost per ICU patient and will potentially have a substantial cost-saving and
life-saving impact for ICU departments and the healthcare system.
Quelle: JHEOR, 27.05.2022
