Last year, Cabell Huntington Hospital faced sepsis head-on and came out on top. Implementing machine learning technology specifically designed to fight sepsis in part through clinician alerts, the organization saw the sepsis-related in-hospital mortality rate was 33.5 percent lower during the post-implementation period and the average sepsis-related hospital length of stay was 17.1 percent lower during the same period. Analyses included 2,298 adult patients in the emergency department and intensive care unit.
Through an ongoing review of internal data, it appears that InSight clinical alerts, from machine learning vendor Dascena, and clinical documentation/coding of sepsis are showing an increased correlation, said Hoyt J. Burdick, MD, chief medical officer at Cabell Huntington Hospital.
"Of course, this phenomenon is not just dependent on the machine logic alerting, but is also subject to clinician education, documentation, coding and billing variables," he explained. "But since we only recently began to adjust some of the machine logic parameters, it seems more likely that the clinicians are more confident in making diagnoses and decisions based upon the improved alerts."
"The software requires a vital signs feed, but also analyzes certain laboratory results, when available," Burdick said. "When InSight determines that a patient's data profile is similar to the reference population with sepsis, the software notifies the charge nurse on duty through an automated call to a dedicated phone line per unit. Following the alert, the charge nurse can then begin the sepsis assessment and inform the physician."
Machine learning also has allowed for improved predictive power in sepsis detection by warning Cabell clinicians of sepsis trends prior to onset of organ dysfunction. This time factor is important since early antibiotic administration is key to reducing sepsis mortality.
Source: Healthcare IT News (View full article)
Posted by Dan Corcoran on March 20, 2018 07:48 AM
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