[Research on Patient Monitoring Alarms in ICU and NICU]

OBJECTIVE

The patient monitors were used to explore the alarm actuality in a ICU and NICU to investigate the awareness and reaction of medical staff to alarms.

METHODS

A series of surveys and interviews were taken to acquire clinicians' feelings and attitudes to monitoring alarms. The researchers were scheduled to track the alarms with annotations, and collect the alarm data of patient monitors using central monitoring system.

RESULTS

A total of 235 387 and 67 783 alarms occurred in ICU and NICU respectively. The average alarm rate was about 142 alarms/patient-day in ICU and 96 alarms/patient-day in NICU.

CONCLUSIONS

There remains alarm fatigue in ICU and NICU, the main reason is the large number of false alarms and clinically irrelevant alarms. In addition, patient monitor is still in the level of threshold alarms or combined alarms, the data integrity and intelligence level need to be improved in future.

Clinical alarms and alarm fatigue in a University Hospital Emergency Department-A retrospective data analysis

BACKGROUND

Alarm fatigue is hypothesized to be caused by vast amount of patient monitor alarms. Objectives were to study the frequency and types of patient monitor alarms, to evaluate alarm fatigue, and to find unit specific alarm threshold values in a university hospital emergency department.

METHODS

We retrospectively gathered alarm data from 9 September to 6 October 2019, in Jorvi Hospital Emergency department, Finland. The department treats surgical, internal and general medicine patients aged 16 and older. The number of patients is on average 4600 to 5000 per month. Eight out of 46 monitors were used for data gathering and the monitored modalities included electrocardiography, respiratory rate, blood pressure, and pulse oximetry.

RESULTS

Total number of alarms in the study monitors was 28 176. Number of acknowledged alarms (ie acknowledgement indicator pressed in the monitor) was 695 (2.5%). The most common alarm types were: Respiratory rate high, 9077 (32.2%), pulse oximetry low, 4572 (16.2%) and pulse oximetry probe off, 4036 (14.3%). Number of alarms with duration under 10 s was 14 936 (53%). Number of individual alarm sounds was 105 000, 469 per monitor per day. Of respiratory rate high alarms, 2846 (31.4%) had initial value below 30 breaths min^-1 . Of pulse oximetry low alarms, 2421 (53.0%) had initial value above 88%.

CONCLUSIONS

Alarm sound load, from individual alarm sounds, was nearly continuous in an emergency department observation room equipped with nine monitors. Intervention by the staff to the alarms was infrequent. More than half of the alarms were momentary.

[A Survey of Patient Monitoring Alarms in Cardiac Care Units]

OBJECTIVE

The patient monitors were used to explore the alarm fatigue in a cardiac care unit and to investigate the awareness and reaction of nurse to alarms.

METHODS

A semi-structured survey was taken to acquire nurses' feeling and knowledge about monitoring alarm. Three full-time researchers were scheduled to track the alarms with annotations, and analyze the alarm data of 12 patient monitors using central monitoring system.

RESULTS

A total of 72 310 unique alarms occurred in the 67-day study period. About 75.7% of them were physiological alarms and less than 10% of medium-low alarms were false positives. The average alarm rate was 128 alarms/patient-day.

CONCLUSIONS

There remains alarm fatigue in CCU, the alarm accuracy has improved than the past by applying new technologies. In some cases, clinicians will pay more attention to trend alarm and combination alarm.

A Framework to Assess Alarm Fatigue Indicators in Critical Care Staff

This article examines work-related and Personality personality factors that could influence health providers in experiencing alarm fatigue. The purpose of this study is to provide a basis to determine factors that may predict the potential of alarm fatigue in critical care staff.

DESIGN

A questionnaire-based survey and an observational study were conducted to assess factors that could contribute to indicators of alarm fatigue.

INTERVENTIONS

Factors included patient-to-staff ratio, criticality of the alarm, priority of different tasks, and personality traits.

SETTING

The study was conducted at an eight-bed ICU in a mid-size hospital in Montana.

SUBJECTS

Data were collected for six day shifts and six night shifts involving 24 critical care professionals. Within each 12-hour shift, six 15-minute intervals were randomly generated through work sampling for 6 days; a total of 1,080 observations were collected.

MEASUREMENTS

Alarm fatigue was assessed with the subjective workload assessment technique and Boredom, Apathy, and Distrust Affects, which were measured through validated questionnaires. The Big Five Personality model was used to assess personality traits.

MAIN RESULTS

Work factors including task prioritization, nurse-to-patient ratio, and length of shifts were associated with indicators of alarm fatigue. Personality traits of openness, conscientiousness, and neuroticism were also associated.

CONCLUSIONS

We recommend assessing personality traits for critical care staff to be aware of how their individualities can affect their behavior towards alarm fatigue. We also recommend an examination of alternative strategies to reduce alarm fatigue, including examining the use of breaks, work rotation, or shift reduction.

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions

BACKGROUND

As 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.

OBJECTIVE

This 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.

METHODS

This 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.

RESULTS

We 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).

CONCLUSIONS

Analyzing 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.

Prevalence, Proportionality, and Cause of Ventilator Alarms in a Pediatric Intensive Care Setting

BACKGROUND

Clinical alarms play an important role in monitoring physiological parameters, vital signs and medical device function in the hospital intensive care environment. Delays in staff response to alarms are well documented as health care providers become desensitized to increased rates of nuisance alarms. Patients can be at increased risk of harm due to alarm fatigue. Current literature suggests alarms from ventilators contribute significantly to nonactionable alarms. A greater understanding of which specific ventilator alarms are most common and the rates at which they occur is fundamental to improving alarm management.

METHODS

A retrospective review was performed on alarms that occurred on the Avea and Servo-i ventilators used in the pediatric ICU and pediatric cardiothoracic ICU at a major metropolitan children's hospital. High- and medium-priority alarms, as classified by the manufacturer, were studied between June 1, 2017, and November 31, 2017. Descriptive data analysis and a 2-proportion z-test were performed to identify proportionality, cause, and prevalence rates in the pediatric ICU and the cardiothoracic ICU.

RESULTS

Eleven distinct ventilator alarms were identified during 2,091 d of mechanical ventilation. The Inspiratory Flow Overrange alarm (42.4%) on the Servo-i, Low V_TE (20.4%; expiratory tidal volume) and Circuit Integrity alarm (20.0%) on the Avea were the most prevalent causes according to ventilator type. Medium-priority alarms comprised 68.7% of all Servo-i alarms, and high-priority alarms comprised 84% of all Avea alarms. The 2-sample test of proportions was significant for differences between both areas (P < .001). The overall alarm prevalence rate was 22.5 ventilator alarms per ventilator-day per patient.

CONCLUSIONS

The cause and proportion of alarms varied by ventilator and care unit. High-priority alarms were most common with the Avea and medium-priority alarms for the Servo-i. The overall combined ventilator alarm prevalence rate was 22.5 alarms per ventilator-day per patient.

[Research of Methods to Reduce Alarm Fatigue of Monitoring System]

OBJECTIVE

In order to solve alarm fatigue, the algorithm optimization strategies were researched to reduce false and worthless alarms.

METHODS

A four-lead arrhythmia analysis algorithm, a multiparameter fusion analysis algorithm, an intelligent threshold reminder, a refractory period delay technique were proposed and tested with collected 28 679 alarms in multi-center study.

RESULTS

The sampling survey indicate that the 80.8% of arrhythmia false alarms were reduced by the four-lead analysis, the 55.9% of arrhythmia and pulse false alarms were reduced by the multi-parameter fusion analysis, the 28.0% and 29.8% of clinical worthless alarms were reduced by the intelligent threshold and refractory period delay techniques respectively. Finally, the total quantity of alarms decreased to 12 724.

CONCLUSIONS

To increase the dimensionality of parametric analysis and control the alarm limits and delay time are conducive to reduce alarm fatigue in intensive care units.

The relationships among alarm fatigue, compassion fatigue, burnout and compassion satisfaction in critical care and step-down nurses

AIMS AND OBJECTIVES

The study purpose was to investigate if compassion fatigue, burnout, compassion satisfaction and personal characteristics are associated with alarm fatigue and predict alarm fatigue in critical care nurses.

BACKGROUND

The phenomena of alarm fatigue, compassion fatigue and burnout place nurses, patients and the healthcare environment in potentially harmful situations and represent the opposite of the foundation of caring and compassion satisfaction in nursing. It has been noted that healthcare organisations should address alarm fatigue as mandated by the Joint Commission based on the higher number of alarms sounding in the critical care environment and based on factors influencing nurses to respond to the alarm.

DESIGN

This was a correlational and predictive quantitative study.

METHODS

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for research reporting of observational studies was followed in this study. Nurses working in the step-down and intensive care units from three hospitals in a selected healthcare network in Pennsylvania were recruited using convenience sampling (n = 52). Observation, the ProQOL and demographic surveys were used to collect data on alarm fatigue, compassion fatigue, burnout, compassion satisfaction and personal characteristics of critical care nurses. Methods of data analyses included descriptive statistics, chi-square, Spearman's ρ and binary logistic regression.

RESULTS

The study results revealed that the participating critical care nurses showed alarm fatigue, were at risk for compassion fatigue and were near risk for burnout.

CONCLUSIONS

This study illuminated the significant relationships among alarm fatigue and the characteristics of gender, nursing unit, nurse-to-patient ratio and age in critical care nurses.

RELEVANCE TO CLINICAL PRACTICE

The study results can help critical care nurses take the initiative to not only help themselves prevent or overcome alarm fatigue, compassion fatigue and burnout, but also help their coworkers in this area.

Impact of Alarm Fatigue on the Work of Nurses in an Intensive Care Environment-A Systematic Review

Background: In conditions of intensive therapy, where the patients treated are in a critical condition, alarms are omnipresent. Nurses, as they spend most of their time with patients, monitoring their condition 24 h, are particularly exposed to so-called alarm fatigue. The purpose of this study is to review the literature available on the perception of clinical alarms by nursing personnel and its impact on work in the ICU environment. Methods: A systematic review of the literature was carried out according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol. The content of electronic databases was searched through, i.e., PubMed, OVID, EBSCO, ProQuest Nursery, and Cochrane Library. The keywords used in the search included: “intensive care unit,” “nurse,” “alarm fatigue,” “workload,” and “clinical alarm.” The review also covered studies carried out among nurses employed at an adult intensive care unit. Finally, seven publications were taken into consideration. Data were analyzed both descriptively and quantitatively, calculating a weighted average for specific synthetized data. Results: In the analyzed studies, 389 nurses were tested, working in different intensive care units. Two studies were based on a quality model, while the other five described the problem of alarms in terms of quantity, based on the HTF (Healthcare Technology Foundation) questionnaire. Intensive care nurses think that alarms are burdensome and too frequent, interfering with caring for patients and causing reduced trust in alarm systems. They feel overburdened with an excessive amount of duties and a continuous wave of alarms. Having to operate modern equipment, which is becoming more and more advanced, takes time that nurses would prefer to dedicate to their patients. There is no clear system for managing the alarms of monitoring devices. Conclusion: Alarm fatigue may have serious consequences, both for patients and for nursing personnel. It is necessary to introduce a strategy of alarm management and for measuring the alarm fatigue level.

Effects of monitor alarm management training on nurses' alarm fatigue: A randomised controlled trial

BACKGROUND

Chaotic monitor alarm management generates a large number of alarms, which result in alarm fatigue. Intensive care unit (ICU) nurses are caretakers of critically ill patients, the effect of alarm management affect patient safety directly.

OBJECTIVES

To evaluate the effect of monitor alarm management training based on the theory of planned behaviour for reducing alarm fatigue in intensive care unit nurses.

DESIGN

A randomised, single-blind trial. This article follows the requirements of CONSORT statement.

PARTICIPANTS

The study was conducted from February 2019-May 2019 in a tertiary A-level hospital. 93 ICU clinical nurses were included, and they were randomly assigned into two groups.

INTERVENTION

Nurses in the experimental group (n = 47) received a 12-week alarm management training course based on the theory of planned behaviour. Nurses in the control group (n = 46) received regular training. All nurses' alarm fatigue scores were measured with a questionnaire before and after the study period. Total number of alarms, nonactionable alarms and true crisis alarms were recorded continuously throughout the study period.

RESULTS

For baseline comparisons, no significant differences were found. By the analysis of independent samples one-way ANCOVAs, the nurses' adjusted alarm fatigue scores at the post-test in the experimental group were significantly lower than those in the control group (p < .001). After the study period, adjusted total number of alarms and nonactionable alarms recorded in the experimental group were both significantly lower than those recorded in the control group (p < .001). After the study period, no significant difference between the two groups was noted in the adjusted number of true crisis alarms (p > .05). The interventions did not cause adverse events in either group of patients and did not cause adverse events in patients.

CONCLUSION

Intensive care unit nurses' alarm fatigue was effectively decreased by the monitor alarm management training based on the theory of planned behaviour.

RELEVANCE TO CLINICAL PRACTICE

(1) Monitor alarm training based on the theory of planned behaviour is effective in reducing nonactionable alarms and lowering alarm fatigue in ICU nurses. (2) The intervention considering the social psychological aspects of behaviour is effective in rebuilding the nurses' awareness and behaviour of alarm management. (3) Nurses are the direct users of monitoring technology. Hospital administrators should attach importance to the role of nurses in the medical monitoring system. We suggest that nursing managers implement training programmes in more ICUs in the future to improve alarm management ability and lower alarm fatigue in ICU nurses.

Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation

Background

Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue.

Objective

This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next.

Methods

We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue.

Results

The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue.

Conclusions

Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.

Decreasing Pediatric PACU Noise Level and Alarm Fatigue: A Quality Improvement Initiative to Improve Safety and Satisfaction

PURPOSE

To describe how a pediatric postanesthesia care unit used a two-phased approach of bundled interventions to reduce unit noise levels and improve staff perceptions of their work environment.

DESIGN

Pre/post design.

METHODS

Postanesthesia care unit sound levels and monitor alarms were measured at baseline and after implementing both project phases. Nursing staff members were surveyed at baseline and after completion of the project.

FINDINGS

Monitor alarms were reduced by more than 50% after Phase I. However, noise measurement data did not reflect a difference in sound levels between baseline and intervention phases. Despite this, staff perceived the unit as much quieter.

CONCLUSIONS

The reduction in monitor alarms did not cause an appreciable change in sound levels as measured by noise dosimeters in either intervention phase. Despite these findings, nurses perceived a quieter and more pleasant workplace. These impressions might have resulted from subjective expectations versus actual volume levels, or they might owe to the reduction in incidence of alarms themselves, which they had viewed as nuisance sounds.

Respiratory Volume Monitoring in the Perioperative Setting Across Multiple Centers

BACKGROUND

The prevalence of nuisance (technical) alarms is the leading cause of alarm fatigue resulting in decreased awareness and a reduction in effective care. The Joint Commission identified in their National Patient Safety goals alarm fatigue as a major safety issue. The introduction of noninvasive respiratory volume monitoring (RVM) has implications for effective perioperative respiratory status management. We evaluated this within the Kaiser Permanente health system.

METHODS

This observational study was conducted at 4 hospitals in the Kaiser Permanente system. Standard data from RVM, pulse oximetry, and capnography were collected postoperatively in the post-anesthesia care unit (PACU) and/or on the general hospital floor. Device-specific alarm types, rates, and respective actions were recorded and analyzed by non-study staff.

RESULTS

RVM was applied to 247 subjects (143 females, body mass index 32.3 ± 8.7 kg/m², age 60.9 ± 13.9 y) providing 2,321 h. RVM alarms occurred 605 times (0.25 alarms/h); 64% were actionable and addressed, 17% were not addressed, 13% were self-resolved, and only 6% were nuisance. In a subgroup, RVM completed all 127 h of monitoring, whereas oximetry with capnography only completed 51 h with 12.9 alarms/h (73% nuisance). The overall RVM alarm rate was significantly lower than with either pulse oximeters or capnography monitors. We saw a nearly 1,000-fold reduction in nuisance alarms compared to capnography and a 20-50-fold reduction in nuisance alarms compared to pulse oximetry.

CONCLUSIONS

Our study indicates that alarm fatigue due to nuisance alarms continues to be a clinical challenge in perioperative settings. Among the 3 common technologies for respiratory function monitoring, RVM had the lowest rate of overall technical alarms and the highest rate of compliance. Furthermore, with early interventions, none of the subjects monitored with RVM suffered any negative outcomes.

Indication-specific event rates among hospitalized patients undergoing continuous cardiac monitoring

Background

Cardiac telemetry monitoring is widely utilized for a variety of clinical indications, yet indication‐specific event rates for monitored patients are seldomly reported.

Hypothesis

High‐risk hospitalized patients for clinical deterioration can be identified using standardized telemetry monitoring indications.

Methods

Adjudicated data from events triggering emergency response team (ERT) activation were systematically characterized at the Cleveland Clinic from among standardized telemetry indications ordered over a 13‐month period.

Results

Among 72 199 orders created for telemetry monitored patients, ERT activation occurred in 2677 patients (3.7%), of which 1326 (49.5%) were cardiac‐related. Patients with deep venous thrombosis or pulmonary embolism (DVT/PE) demonstrated the highest overall event rate (ERT: n = 41 of 593 pts [6.9%]; 25/41 cardiac related [61%]). Cardiac‐related events were proportionally highest among patients with coronary disease awaiting revascularization (ERT: n = 19 of 847 patients [2.2%]; 13/19 cardiac‐related [68.4%]). Arrhythmia‐specific events were highest among patients who underwent cardiac surgery (n = 78 of 193 cardiac‐related ERT [40.4%]), and patients with known or suspected tachyarrhythmias (n = 318 of 788 cardiac‐related ERT [40.4%]). Bubble plot analysis identified patients hospitalized with DVT/PE, drug or alcohol exposures, and acute coronary syndrome as among the highest overall and cardiac‐related events while identifying patients with respiratory disorder monitoring indications as carrying the highest noncardiac event rate.

Conclusion

High‐risk hospitalized patients can be identified by telemetry indication and prioritized according to concerns for cardiac, arrhythmia‐specific and noncardiac clinical deterioration. This is particularly useful when monitored bed resources are constrained.

Types and Frequency of Infusion Pump Alarms and Infusion-Interruption to Infusion-Recovery Times for Critical Short Half-Life Infusions: Retrospective Data Analysis

BACKGROUND

Alarm fatigue commonly leads to a reduced response to alarms. Appropriate and timely response to intravenous pump alarms is crucial to infusion continuity. The difficulty of filtering out critical short half-life infusion alarms from nonurgent alarms is a key challenge for risk management for clinicians. Critical care areas provide ample opportunities for intravenous medication error with the frequent administration of high-alert, critical short half-life infusions that require rigorous maintenance for continuity of delivery. Most serious medication errors in critical care occur during the execution of treatment, with performance-level failures outweighing rule-based or knowledge-based mistakes.

OBJECTIVE

One objective of this study was to establish baseline data for the types and frequency of alarms that critical care clinicians are exposed to from a variety of infusion devices, including both large volume pumps and syringe drivers. Another objective was to identify the volume of these alarms that specifically relate to critical short half-life infusions and to evaluate user response times to alarms from infusion devices delivering these particular infusions.

METHODS

The event logs of 1183 infusion pumps used in critical care environments and in general care areas within the European region were mined for a range of alarm states. The study then focused on a selection of infusion alarms from devices delivering critical short half-life infusions that would warrant rapid attention from clinicians in order to avoid potentially harmful prolonged infusion interruption. The reaction time of clinicians to infusion-interruption states and alarms for the selected critical short half-life infusions was then calculated.

RESULTS

Initial analysis showed a mean average of 4.50 alarms per infusion in the general critical care pump population as opposed to the whole hospital rate of 1.39. In the pediatric intensive care unit (PICU) group, the alarms per infusion value was significantly above the mean average for all critical care areas, with 8.61 alarms per infusion. Infusion-interruption of critical short half-life infusions was found to be a significant problem in all areas of the general critical care pump population, with a significant number of downstream (ie, vein and access) occlusion events noted. While the mean and median response times to critical short half-life infusion interruptions were generally within the half-lives of the selected medications, there was a high prevalence of outliers in terms of reaction times for all the critical short half-life infusions studied.

CONCLUSIONS

This study gives an indication of what might be expected in critical care environments in terms of the volume of general infusion alarms and critical short half-life infusion alarms, as well as for clinician reaction times to critical short half-life infusion-interruption events. This study also identifies potentially problematic areas of the hospital for alarm fatigue and for particular issues of infusion and infusion-line management. Application of the proposed protocols can help create benchmarks for pump alarm management and clinician reaction times. These protocols can be applied to studies on the impact of alarm fatigue and for the evaluation of protocols, infusion-monitoring strategies, and infusion pump-based medication safety software aimed at reducing alarm fatigue and ensuring the maintenance of critical short half-life infusions. Given the frequency of infusion alarms seen in this study, the risk of alarm fatigue due to the white noise of pump alarms present in critical care, to which clinicians are constantly exposed, is very high. Furthermore, the added difficulties of maintaining critical short half-life infusions, and other infusions in specialist areas, are made clear by the high ratio of downstream occlusion to infusion starts in the neonatal intensive care unit (NICU). The ability to quantitatively track the volume of alarms and clinician reaction times contributes to a greater understanding of the issues of alarm fatigue in intensive care units. This can be applied to clinical audit, can allow for targeted training to reduce nuisance alarms, and can aid in planning for improvement in the key area of maintenance of steady-state plasma levels of critical short half-life infusions. One clear conclusion is that the medication administration rights should be extended to include right maintenance and ensured delivery continuity of critical short half-life infusions.

Mechanical Ventilation Alarms and Alarm Fatigue

Mechanical ventilation alarms and alerts, both audible and visual, provide the clinician with vital information about the patient's physiologic condition and the status of the machine's function. Not all alarms generated by the mechanical ventilator provide actionable information. Over time, clinicians can become desensitized to audible alarms due to alarm fatigue and may potentially ignore an actionable situation that results in patient harm. Alarm fatigue has been recognized by multiple agencies as a major patient-safety issue. To date, mechanical ventilator alarm settings do not have standardized nomenclature. The aim of this review was to examine and report on the literature that pertains to mechanical ventilation alarms and alarm fatigue and to propose recommendations for future research that may lead to safer mechanical ventilation alarm practices.

The Impact of Head-Worn Displays on Strategic Alarm Management and Situation Awareness

OBJECTIVE

To investigate whether head-worn displays (HWDs) help mobile participants make better alarm management decisions and achieve better situation awareness than alarms alone.

BACKGROUND

Patient alarms occur frequently in hospitals but often do not require clinical intervention. Clinicians may become desensitized to alarms and fail to respond to clinically relevant alarms. HWDs could make patient information continuously accessible, support situation awareness, and help clinicians prioritize alarms.

METHOD

Experiment 1 ( n = 76) tested whether nonclinicians monitoring simulated patients benefited from vital sign information continuously displayed on an HWD while they performed a secondary calculation task. Experiment 2 ( n = 13) tested, across three separate experimental sessions, how effectively nursing trainees monitored simulated patients' vital signs under three different display conditions while they assessed a simulated patient.

RESULTS

In Experiment 1, participants who had access to continuous patient information on an HWD responded to clinically important alarms 25.9% faster and were 6.7 times less likely to miss alarms compared to participants who only heard alarms. In Experiment 2, participants using an HWD answered situation awareness questions 18.9% more accurately overall than when they used alarms only. However, the effect was significant in only two of the three experimental sessions.

CONCLUSION

HWDs may help users maintain continuous awareness of multiple remote processes without affecting their performance on ongoing tasks.

APPLICATION

The outcomes may apply to contexts where access to continuous streams of information from remote locations is useful, such as patient monitoring or clinical supervision.

[Improving the Surveillance Alarm Response Rate Among Nurses in the Internal Medicine Intensive Care Unit]

BACKGROUND & PROBLEMS

According to the Emergency Care Research Institute, "not responding to alarms" is a top-ten health-technology hazard that ranked first between 2008 and 2014. The failure of clinical nurses to respond to alarms in time due to lack of awareness, fatigue, or other cause represents a great threat to patient safety. Between August 2014 and August 2015, two patients in this unit died because the red alert on the physiological alarm surveillance system was not answered and dealt with promptly.

PURPOSE

To raise the 10-second response rate to red alerts from 22% to 100% in order to enhance inpatient safety.

METHODS

Establish standard operating procedures for alarms and for the handling of physiologic monitor devices when alarms sound; form a gatekeeper system; and arrange on-the-job training.

RESULTS

The 10-second response rate to red alerts increased from 22% to 100% between November 2016 and November 2017.

CONCLUSIONS

By following standard operating procedures, personnel now have a guide to respond to and handle red alerts comprehensively. Implementing the gatekeeper system also increased the team spirit of the unit and helped personnel appreciate the importance of cooperation in handling alarms. In addition, the functions of the physiologic monitor devices and the 10-second response rate for red alerts will be included in the annual quality control checklist of the unit for follow up, review, and further improvement.

Perceptions of Infusion Pump Alarms: Insights Gained From Critical Care Nurses

Between 1983 and 2011, equipment-related alarms in critical care have increased from 6 to 40 different alarm types. As nurses become overwhelmed, distracted, or desensitized by alarm noise, they may miss critical alarms that could result in patient harm. The findings of an infusion pump alarm survey indicated that nurses overwhelmingly agree that infusion pump nuisance alarms occur frequently and disrupt patient care. But nurses' perceptions of pump alarms are different from those previously reported for clinical alarms in general. It may not be appropriate to broadly apply general alarm management recommendations to infusion pump alarms at this time.

Types and Frequency of Infusion Pump Alarms: Protocol for a Retrospective Data Analysis

BACKGROUND

The variety of alarms from all types of medical devices has increased from 6 to 40 in the last three decades, with today's most critically ill patients experiencing as many as 45 alarms per hour. Alarm fatigue has been identified as a critical safety issue for clinical staff that can lead to potentially dangerous delays or nonresponse to actionable alarms, resulting in serious patient injury and death. To date, most research on medical device alarms has focused on the nonactionable alarms of physiological monitoring devices. While there have been some reports in the literature related to drug library alerts during the infusion pump programing sequence, research related to the types and frequencies of actionable infusion pump alarms remains largely unexplored.

OBJECTIVE

The objectives of this study protocol are to establish baseline data related to the types and frequency of infusion pump alarms from the B. Braun Outlook 400ES Safety Infusion System with the accompanying DoseTrac Infusion Management Software.

METHODS

The most recent consecutive 60-day period of backup hospital data received between April 2014 and February 2017 from 32 United States-based hospitals will be selected for analysis. Microsoft SQL Server (2012 - 11.0.5343.0 X64) will be used to manage the data with unique code written to sort data and perform descriptive analyses. A validated data management methodology will be utilized to clean and analyze the data. Data management procedures will include blinding, cleaning, and review of existing infusion data within the DoseTrac Infusion Management Software databases at each hospital. Patient-identifying data will be removed prior to merging into a dedicated and secure data repository. This pooled data will then be analyzed.

RESULTS

This exploratory study will analyze the aggregate alarm data for each hospital by care area, drug infused, time of day, and day of week, including: overall infusion pump alarm frequency (number of alarms per active infusion), duration of alarms (average, range, median), and type and frequency of alarms distributed by care area.

CONCLUSIONS

Infusion pump alarm data collected and analyzed in this study will be used to help establish a baseline of infusion pump alarm types and relative frequencies. Understanding the incidences and characteristics of infusion pump alarms will result in more informed quality improvement recommendations to decrease and/or modify infusion pump alarms, and potentially reduce clinical staff alarm fatigue and improve patient safety. .

REGISTERED REPORT IDENTIFIER

RR1-10.2196/10446.

[Research on the Clinical Alarm Management Mechanism Based on Closed-loop Control Theory]

This paper proposes a clinical alarm management system based on the theory of the closed loop control. The alarm management mechanism can be divided into the expected standard, improving execution rule, rule execution, medical devices with alarm functions, results analysis strategy and the output link. And, we make relevant application and discussion. Results showed that the mechanism can be operable and effective.

Addressing vital sign alarm fatigue using personalized alarm thresholds

Alarm fatigue, a condition in which clinical staff become desensitized to alarms due to the high frequency of unnecessary alarms, is a major patient safety concern. Alarm fatigue is particularly prevalent in the pediatric setting, due to the high level of variation in vital signs with patient age. Existing studies have shown that the current default pediatric vital sign alarm thresholds are inappropriate, and lead to a larger than necessary alarm load. This study leverages a large database containing over 190 patient-years of heart rate data to accurately identify the 1st and 99th percentiles of an individual's heart rate on their first day of vital sign monitoring. These percentiles are then used as personalized vital sign thresholds, which are evaluated by comparing to non-default alarm thresholds used in practice, and by using the presence of major clinical events to infer alarm labels. Using the proposed personalized thresholds would decrease low and high heart rate alarms by up to 50% and 44% respectively, while maintaining sensitivity of 62% and increasing specificity to 49%. The proposed personalized vital sign alarm thresholds will reduce alarm fatigue, thus contributing to improved patient outcomes, shorter hospital stays, and reduced hospital costs.

[Survey and Discussion on Ventilator Alarm Status in ICU]

OBJECTIVES

The article aims at survey and analysis on ventilator alarm status in pediatric hospital.

METHODS

First, we design the statistical table of ventilator, and analyze the reliability of statistical table. Then we evaluate the effectiveness of the alarm. Finally, we evaluate the alarm information synthetically through investigation and statistics.

RESULTS

The ventilator alarm is ineffective, a large number of nonsensical alarms, leading to lower alert sensitivity of medical staff.

CONCLUSIONS

According to the survey results, we should analyze the cause of the problem, and propose the corresponding alarm management method.

Spearcons for Patient Monitoring: Laboratory Investigation Comparing Earcons and Spearcons

Objective We compared the effectiveness of single-tone earcons versus spearcons in conveying information about two commonly monitored vital signs: oxygen saturation and heart rate. Background The uninformative nature of many medical alarms-and clinicians' lack of response to alarms-is a widespread problem that can compromise patient safety. Auditory displays, such as earcons and spearcons (speech-based earcons), may help clinicians maintain awareness of patients' well-being and reduce their reliance on alarms. Earcons are short abstract sounds whose properties represent different types and levels of information, whereas spearcons are time-compressed spoken phrases that directly state their meaning. Listeners might identify patient vital signs more accurately with spearcons than with earcons. Method In Experiment 1 we compared how accurately 40 nonclinician participants using either (a) single-tone earcons differentiated by timbre and tremolo or (b) Cantonese spearcons recorded using a female Cantonese voice could identify both oxygen saturation and heart rate levels. In Experiment 2 we tested the identification performance of six further nonclinician participants with spearcons recorded using a male Cantonese voice. Results In Experiment 1, participants using spearcons identified both vital signs together more accurately than did participants using earcons. Participants using Cantonese spearcons also learned faster, completed trials faster, identified individual vital signs more accurately, and felt greater ease and more confident when identifying oxygen saturation levels. Experiment 2 verified the previous findings with male-voice Cantonese spearcons. Conclusion Participants identified vital signs more accurately using spearcons than with the single-tone earcons. Application Spearcons may be useful for patient monitoring in situations in which intermittently presented information is desirable.

Patient characteristics associated with false arrhythmia alarms in intensive care

INTRODUCTION

A high rate of false arrhythmia alarms in the intensive care unit (ICU) leads to alarm fatigue, the condition of desensitization and potentially inappropriate silencing of alarms due to frequent invalid and nonactionable alarms, often referred to as false alarms.

OBJECTIVE

The aim of this study was to identify patient characteristics, such as gender, age, body mass index, and diagnosis associated with frequent false arrhythmia alarms in the ICU.

METHODS

This descriptive, observational study prospectively enrolled patients who were consecutively admitted to one of five adult ICUs (77 beds) at an urban medical center over a period of 31 days in 2013. All monitor alarms and continuous waveforms were stored on a secure server. Nurse scientists with expertise in cardiac monitoring used a standardized protocol to annotate six clinically important types of arrhythmia alarms (asystole, pause, ventricular fibrillation, ventricular tachycardia, accelerated ventricular rhythm, and ventricular bradycardia) as true or false. Total monitoring time for each patient was measured, and the number of false alarms per hour was calculated for these six alarm types. Medical records were examined to acquire data on patient characteristics.

RESULTS

A total of 461 unique patients (mean age =60±17 years) were enrolled, generating a total of 2,558,760 alarms, including all levels of arrhythmia, parameter, and technical alarms. There were 48,404 hours of patient monitoring time, and an average overall alarm rate of 52 alarms/hour. Investigators annotated 12,671 arrhythmia alarms; 11,345 (89.5%) were determined to be false. Two hundred and fifty patients (54%) generated at least one of the six annotated alarm types. Two patients generated 6,940 arrhythmia alarms (55%). The number of false alarms per monitored hour for patients' annotated arrhythmia alarms ranged from 0.0 to 7.7, and the duration of these false alarms per hour ranged from 0.0 to 158.8 seconds. Patient characteristics were compared in relation to 1) the number and 2) the duration of false arrhythmia alarms per 24-hour period, using nonparametric statistics to minimize the influence of outliers. Among the significant associations were the following: age ≥60 years (P=0.013; P=0.034), confused mental status (P<0.001 for both comparisons), cardiovascular diagnoses (P<0.001 for both comparisons), electrocardiographic (ECG) features, such as wide ECG waveforms that correspond to ventricular depolarization known as QRS complex due to bundle branch block (BBB) (P=0.003; P=0.004) or ventricular paced rhythm (P=0.002 for both comparisons), respiratory diagnoses (P=0.004 for both comparisons), and support with mechanical ventilation, including those with primary diagnoses other than respiratory ones (P<0.001 for both comparisons).

CONCLUSION

Patients likely to trigger a higher number of false arrhythmia alarms may be those with older age, confusion, cardiovascular diagnoses, and ECG features that indicate BBB or ventricular pacing, respiratory diagnoses, and mechanical ventilatory support. Algorithm improvements could focus on better noise reduction (eg, motion artifact with confused state) and distinguishing BBB and paced rhythms from ventricular arrhythmias. Increasing awareness of patient conditions that apparently trigger a higher rate of false arrhythmia alarms may be useful for reducing unnecessary noise and improving alarm management.