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The heuristics of nurse responsiveness to critical patient monitor and ventilator alarms in a private room neonatal intensive care unit.

ABSTRACT: Alarm fatigue is a well-recognized patient safety concern in intensive care settings. Decreased nurse responsiveness and slow response times to alarms are the potentially dangerous consequences of alarm fatigue. The aim of this study was to determine the factors that modulate nurse responsiveness to critical patient monitor and ventilator alarms in the context of a private room neonatal intensive care setting.The study design comprised of both a questionnaire and video monitoring of nurse-responsiveness to critical alarms. The Likert scale questionnaire, comprising of 50 questions across thematic clusters (critical alarms, yellow alarms, perception, design, nursing action, and context) was administered to 56 nurses (90% response rate). Nearly 6000 critical alarms were recorded from 10 infants in approximately 2400 hours of video monitoring. Logistic regression was used to identify patient and alarm-level factors that modulate nurse-responsiveness to critical alarms, with a response being defined as a nurse entering the patient's room within the 90s of the alarm being generated.Based on the questionnaire, the majority of nurses found critical alarms to be clinically relevant even though the alarms did not always mandate clinical action. Based on video observations, for a median of 34% (IQR, 20-52) of critical alarms, the nurse was already present in the room. For the remaining alarms, the response rate within 90s was 26%. The median response time was 55s (IQR, 37-70s). Desaturation alarms were the most prevalent and accounted for more than 50% of all alarms. The odds of responding to bradycardia alarms, compared to desaturation alarms, were 1.47 (95% CI = 1.21-1.78; <0.001) while that of responding to a ventilator alarm was lower at 0.35 (95% CI = 0.27-0.46; p <0.001). For every 20s increase in the duration of an alarm, the odds of responding to the alarm (within 90s) increased to 1.15 (95% CI = 1.1-1.2; p <0.001). The random effect per infant improved the fit of the model to the data with the response times being slower for infants suffering from chronic illnesses while being faster for infants who were clinically unstable.Even though nurses respond to only a fraction of all critical alarms, they consider the vast majority of critical and yellow alarms as useful and relevant. When notified of a critical alarm, they seek waveform information and employ heuristics in determining whether or not to respond to the alarm.Amongst other factors, the category and duration of critical alarms along with the clinical status of the patient determine nurse-responsiveness to alarms.

SUBMITTER: Joshi R

PROVIDER: S-EPMC5628801 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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The heuristics of nurse responsiveness to critical patient monitor and ventilator alarms in a private room neonatal intensive care unit.

Joshi Rohan R Mortel Heidi van de HV Feijs Loe L Andriessen Peter P Pul Carola van CV

PloS one 20171005 10

<h4>Aim</h4>Alarm fatigue is a well-recognized patient safety concern in intensive care settings. Decreased nurse responsiveness and slow response times to alarms are the potentially dangerous consequences of alarm fatigue. The aim of this study was to determine the factors that modulate nurse responsiveness to critical patient monitor and ventilator alarms in the context of a private room neonatal intensive care setting.<h4>Methods</h4>The study design comprised of both a questionnaire and vide ...[more]

PMID: 28981515

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Project description:BackgroundClinicians in intensive care units experience alarm fatigue related to frequent false and non-actionable alarms produced by physiologic monitors. To reduce non-actionable alarms, alarm settings may need to be customized for individual patients; however, nurses may not customize alarms because of competing demands and alarm fatigue.ObjectiveTo examine the effectiveness and acceptance of physiologic monitor software to support customization of alarms.MethodsThis pre/post intervention study was conducted in a 56-bed medical intensive care unit. IntelliVue® Alarm Advisor customization support software for alarm limit violations was installed on all monitors and education on its use provided. For 2 months before and after implementation of the software, data were collected on patient characteristics from the electronic health record, alarm counts and duration from the monitoring system, and nurses' experience of alarms from a survey.ResultsMedium-priority heart rate, respiratory rate, and arterial pressure alarms were significantly reduced after software implementation (9.3%, 11.8%, and 15.9% reduction respectively; p<0.001 for all). The duration of these alarms was also significantly shorter (7.8%, 13.3%, and 9.3% reduction respectively; p<0.05 for all). The number and duration of SpO2 alarms did not decrease (p>0.05 for both). Patients post-intervention had worse Glasgow Coma Scale scores (p = 0.014), but otherwise were comparable to those pre-intervention. Nurses reported less time spent on non-actionable alarms post-intervention than pre-intervention (p = 0.026). Also lower post-intervention were the proportions of nurses who reported that alarms disturbed their workflow (p = 0.027) and who encountered a situation where an important alarm was ignored (p = 0.043). The majority (>50%) agreed that the software supported setting appropriate alarm limits and was easy to use.ConclusionAlarm customization software was associated with a reduction in alarms. Use of software to support nurses' recognition of trends in patients' alarms and facilitate changes to alarm settings may add value to alarm reduction initiatives.

Project description:BackgroundAs one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.ObjectiveThis study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies.MethodsThis observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation.ResultsWe developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed).ConclusionsAnalyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.

Project description:Critically ill pediatric patients frequently require hemoglobin monitoring. Accurate noninvasive Hb (SpHb) would allow practitioners to decrease anemia from repeated blood draws, traumatic blood draws, and a decreased number of laboratory Hb (LabHb) medical tests. The Food and Drug Administration has approved the Masimo Pronto SpHb and associated Rainbow probes; however, its use in the pediatric intensive care unit (PICU) is controversial. In this study, we define the degree of agreement between LabHb and SpHb using the Masimo Pronto SpHb Monitor and identify clinical and demographic conditions associated with decreased accuracy.We performed a prospective, observational study in a large PICU at an academic medical center. Fifty-three pediatric patients (30-d and 18-y-old), weighing >3 kg, admitted to the PICU from January-April 2013 were examined. SpHb levels measured at the time of LabHb blood draw were compared and analyzed.Only 83 SpHb readings were obtained in 118 attempts (70.3%) and 35 readings provided a result of "unable to obtain." The mean LabHb and SpHb were 11.1 g/dL and 11.2 g/dL, respectively. Bland-Altman analysis showed a mean difference of 0.07 g/dL with a standard deviation of ±2.59 g/dL. Pearson correlation is 0.55, with a 95% confidence interval between 0.38 and 0.68. Logistic regression showed that extreme LabHb values, increasing skin pigmentation, and increasing body mass index were predictors of poor agreement between SpHb and LabHb (P < 0.05). Separately, increasing body mass index, hypoxia, and hypothermia were predictors for undetectable readings (P < 0.05).The Masimo Pronto SpHb Monitor provides adequate agreement for the trending of hemoglobin levels in critically ill pediatric patients. However, the degree of agreement is insufficient to be used as the sole indicator for transfusion decisions and should be used in context of other clinical parameters to determine the need for LabHb in critically ill pediatric patients.

Project description:OBJECTIVES:To study the incidence, etiology and risk factors associated with ventilator associated pneumonia (VAP) in children. METHODS:This prospective cohort study was conducted on patients admitted to the Pediatric Intensive Care Unit (PICU) of a tertiary care institute of North India, from June 2012 through March 2014, who received mechanical ventilation for more than 24 h. All enrolled children were assessed daily for development of ventilator associated pneumonia (VAP) using the case definition given by Centers for Disease Control and Prevention (CDC). Chest radiograph and microbiologic samplings were performed in children suspected to have VAP. Risk factors associated with VAP were calculated by doing bivariate and multivariate analysis. RESULTS:A total of 128 patients were screened and 86 were enrolled (median age 30 mo 95% CI 4.0-84.0; 72% boys). The most common admitting diagnosis was sepsis (16%) followed by acyanotic congenital heart disease with pneumonia (14%) and the most common indication for ventilation was respiratory failure (45.3%). The incidence of VAP according to CDC criteria was 38.4%, while the incidence of microbiologically confirmed VAP was 24.4%. The incidence of ventilator associated tracheobronchitis (VAT) was found to be 11.6%. Acinetobacter was the most frequently isolated organism (47%) followed by Pseudomonas (28%), Klebsiella (15%), E. coli (5%) and Enterobacter (5%). Risk factors for VAP on bivariate analysis were use of proton pump inhibitor (PPI) (p?=?0.027, OR 5.2, 95% CI 1.1-24.3), enteral feeding (p?<?0.001, OR 6.5, 95% CI 2.1-19.4) and re-intubation (p?=?0.024, OR 3.3 and 95% CI 1.1-9.6). On multivariate analysis, use of PPI (p?=?0.03, OR 8.47, 95% CI 1.19-60.33) and enteral feeding (p?<?0.001, OR 12.2, 95% CI 2.58-57.78) were identified as independent risk factors for VAP. CONCLUSIONS:Ventilator associated pneumonia is an important complication in children receiving mechanical ventilation in PICU and Gram negative bacilli (Acinetobacter and Pseudomonas) being the important causative agents. Ventilator associated tracheobronchitis is an emerging entity; recognition and treatment of same might prevent the development of VAP.

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The heuristics of nurse responsiveness to critical patient monitor and ventilator alarms in a private room neonatal intensive care unit.

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The heuristics of nurse responsiveness to critical patient monitor and ventilator alarms in a private room neonatal intensive care unit.

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