Physiologic Monitor Alarm Rates at 5 Children's Hospitals

Schondelmeyer, Amanda C.; Brady, Patrick; Goel, Vaibhava; Cvach, Maria; Blake, Nancy; Mangeot, Colleen; Bonafide, Christopher P.

doi:10.12788/jhm.2918

Cited by 33 publications

(27 citation statements)

References 15 publications

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“…Initial studies identified alarm fatigue to be directly related to the number of alarms per patient per day, with some patients experiencing up to 350 physiological monitor alarms daily 7. On a paediatric ward, up to 99% of alarms are non-actionable, either not accurately reflecting the clinical status of the patient or not requiring intervention 1 8 9. Furthermore, nursing response time to alarms increases as exposure to non-actionable alarms increases 10.…”

Section: Introductionmentioning

confidence: 99%

Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital

et al. 2020

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Background10The Joint Commission identified inpatient alarm reduction as an opportunity to improve patient safety; enhance patient, family and nursing satisfaction; and optimise workflow. We used quality improvement (QI) methods to safely decrease non-actionable alarm notifications to bedside providers.MethodsIn a paediatric tertiary care centre, we convened a multidisciplinary team to address alarm notifications in our acute care cardiology unit. Alarm notification was defined as any alert to bedside providers for each patient-triggered monitor alarm. Our aim was to decrease alarm notifications per monitored bed per day by 60%. Plan-Do-Study-Act testing cycles included updating notification technology, establishing alarm logic and modifying bedside workflow processes, including silencing the volume on all bedside monitors. Our secondary outcome measure was nursing satisfaction. Balancing safety measures included floor to intensive care unit transfers and patient acuity level.ResultsAt baseline, there was an average of 71 initial alarm notifications per monitored bed per day. Over a 3.5-year improvement period (2014–2017), the rate decreased by 68% to 22 initial alarm notifications per monitored bed per day. The proportion of initial to total alarm notifications remained stable, decreasing slightly from 51% to 40%. There was a significant improvement in subjective nursing satisfaction. At baseline, 32% of nurses agreed they were able to respond to alarms appropriately and quickly. Following interventions, agreement increased to 76% (p<0.001). We sustained these improvements over a year without a change in monitored balancing measures.ConclusionWe successfully reduced alarm notifications while preserving patient safety over a 4-year period in a complex paediatric patient population using technological advances and QI methodology. Continued efforts are needed to further optimise monitor use across paediatric hospital units.

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Section: Introductionmentioning

confidence: 99%

Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital

et al. 2020

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“…In the neonatal population, these limitations are particularly apparent and produce several unique adverse outcomes. The frequency of false alarms secondary to the frequent movement of newborns is associated with provider alarm fatigue, infant hearing loss, and a disruptive environment for development [ 8 , 9 , 10 , 11 , 12 ]. Additionally, the humid environment of neonatal incubators and the infant’s thin, underdeveloped skin cause the adhesive in electric leads to fail and require frequent changing.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Video-Based Neonatal Respiratory Monitoring

et al. 2020

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Respiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system that could improve patient care. This iterative design study developed a novel algorithm that produced RR from footage analyzed from stable NICU patients in open cribs with corrected gestational ages ranging from 33 to 40 weeks. The final algorithm used a proprietary technique of micromotion and stationarity detection (MSD) to model background noise to be able to amplify and record respiratory motions. We found significant correlation—r equals 0.948 (p value of 0.001)—between MSD and the current hospital standard, electrocardiogram impedance pneumography. Our video-based system showed a bias of negative 1.3 breaths and root mean square error of 6.36 breaths per minute compared to standard continuous monitoring. Further work is needed to evaluate the ability of video-based monitors to observe clinical changes in a larger population of patients over extended periods of time.

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“…[5][6][7] Despite these concerns, CPMs are routinely used: up to 48% of pediatric patients in nonintensive care units at children's hospitals are monitored. 2 Although the low number of alarms that receive responses has been well-described, 8,9 the reasons why clinicians do or do not respond to alarms are unclear. A study conducted in an adult perioperative unit noted prolonged nurse response times for patients with high alarm rates.…”

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confidence: 99%

Nurse Responses to Physiologic Monitor Alarms on a General Pediatric Unit

Schondelmeyer

Daraiseh

Allison

et al. 2019

Journal of Hospital Medicine

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BACKGROUND Hospitalized children generate up to 152 alarms per patient per day outside of the intensive care unit. In that setting, as few as 1% of alarms are clinically important. How nurses make decisions about responding to alarms, given an alarm's low specificity for detecting clinical deterioration, remains unclear. OBJECTIVE Our objective was to describe how bedside nurses think about and act upon monitor alarms for hospitalized children. DESIGN, SETTING, PARTICIPANTS This was a qualitative study that involved the direct observation of nurses working on a general pediatric unit at a large children's hospital. MEASUREMENTS We used a structured tool that included predetermined categories to assess nurse responses to monitor alarms. Data on alarm frequency and type were pulled from bedside monitors. RESULTS We conducted 61.3 patient‐hours of observation with nine nurses, in which we documented 207 nurse responses to patient alarms. For 67% of alarms heard outside of the room, the nurse decided not to respond without further assessment. Nurses most commonly cited reassuring clinical context (eg, medical team in room), as the rationale for alarm nonresponse. The nurse deemed clinical intervention necessary in only 14 (7%) of the observed responses. CONCLUSION Nurses rely on clinical and contextual details to determine how to respond to alarms. Few of the alarm responses in our study resulted in a clinical intervention. These findings suggest that multiple system‐level and educational interventions may be necessary to improve the efficacy and safety of continuous monitoring.

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Physiologic Monitor Alarm Rates at 5 Children's Hospitals

Cited by 33 publications

References 15 publications

Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital

Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital

Non-Contact Video-Based Neonatal Respiratory Monitoring

Nurse Responses to Physiologic Monitor Alarms on a General Pediatric Unit

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