ABSTRACT
The problem of high rates of false alarms in patient monitoring in anesthesiology and intensive care medicine is well known but remains unsolved. False alarms desensitize the medical staff, leading to ignored true alarms and reduced quality of patient care. A database of intra-operative monitoring data was analyzed to find characteristic alarm patterns. The original data were re-evaluated to find relevant events and to rate the severity of these events. Based on this analysis an adaptive time delay was developed that individually delays the alarms depending on the grade of threshold deviation. The conventional threshold algorithm led to 4893 alarms. 3515 (71.84 %) of these alarms were annotated as clinically irrelevant. In total 81.0 % of all clinically irrelevant alarms were caused by only mild and/or brief threshold violations. We implemented the new algorithm for selected parameters. These parameters equipped with adaptive validation delays led to 1729 alarms. 931 (53.85 %) alarms were annotated as clinically irrelevant. 632 alarms indicated the 645 clinically relevant events. The positive predictive value of occurring alarms improved from 28.16 % (conventional algorithm) to 46.15 % (new algorithm). 13 events were missed. The false positive alarm reduction rate of the algorithm ranged from 33 to 86.75 %. The overall reduction was 73.51 %. The implementation of this algorithm may be able to suppress a large percentage of false alarms. The effect of this approach has not been demonstrated but shows promise for reducing alarm fatigue. Its safety needs to be proven in a prospective study.
