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Safety Huddle Intervention for Reducing Physiologic Monitor Alarms: A Hybrid Effectiveness-Implementation Cluster Randomized Trial
Physiologic monitor alarms occur frequently in the hospital environment, with average rates on pediatric wards between 42 and 155 alarms per monitored patient-day.1 However, average rates do not depict the full story, because only 9%–25% of patients are responsible for most alarms on inpatient wards.1,2 In addition, only 0.5%–1% of alarms on pediatric wards warrant action.3,4 Downstream consequences of high alarm rates include interruptions5,6 and alarm fatigue.3,4,7
Alarm customization, the process of reviewing individual patients’ alarm data and using that data to implement patient-specific alarm reduction interventions, has emerged as a potential approach to unit-wide alarm management.8-11 Potential customizations include broadening alarm thresholds, instituting delays between the time the alarm condition is met and the time the alarm sounds, and changing electrodes.8-11 However, the workflows within which to identify the patients who will benefit from customization, make decisions about how to customize, and implement customizations have not been delineated.
Safety huddles are brief structured discussions among physicians, nurses, and other staff aiming to identify and mitigate threats to patient safety.11-13 In this study, we aimed to evaluate the influence of a safety huddle-based alarm intervention strategy targeting high alarm pediatric ward patients on (a) unit-level alarm rates and (b) patient-level alarm rates, as well as to (c) evaluate implementation outcomes. We hypothesized that patients discussed in huddles would have greater reductions in alarm rates in the 24 hours following their huddle than patients who were not discussed. Given that most alarms are generated by a small fraction of patients,1,2 we hypothesized that patient-level reductions would translate to unit-level reductions.
METHODS
Human Subject Protection
The Institutional Review Board of Children’s Hospital of Philadelphia approved this study with a waiver of informed consent. We registered the study at ClinicalTrials.gov (identifier NCT02458872). The original protocol is available as an Online Supplement.
Design and Framework
For our secondary effectiveness outcome evaluating the effect of the intervention on the alarm rates of the individual patients discussed in huddles, we used a cohort design embedded within the trial to analyze patient-specific alarm data collected only on randomly selected “intensive data collection days,” described below and in Figure 1.
Setting and Subjects
All patients hospitalized on 8 units that admit general pediatric and medical subspecialty patients at Children’s Hospital of Philadelphia between June 15, 2015 and May 8, 2016 were included in the primary (unit-level) analysis. Every patient’s bedside included a General Electric Dash 3000 physiologic monitor. Decisions to monitor patients were made by physicians and required orders. Default alarm settings are available in Supplementary Table 1; these settings required orders to change.
All 8 units were already convening scheduled safety huddles led by the charge nurse each day. All nurses and at least one resident were expected to attend; attending physicians and fellows were welcome but not expected to attend. Huddles focused on discussing safety concerns and patient flow. None of the preexisting huddles included alarm discussion.
Intervention
For each nonholiday weekday, we generated customized paper-based alarm huddle data “dashboards” (Supplementary Figure 1) displaying data from the patients (up to a maximum of 4) on each intervention unit with the highest numbers of high-acuity alarms (“crisis” and “warning” audible alarms, see Supplementary Table 2 for detailed listing of alarm types) in the preceding 4 hours by reviewing data from the monitor network using BedMasterEx v4.2 (Excel Medical Electronics). Dashboards listed the most frequent types of alarms, alarm settings, and included a script for discussing the alarms with checkboxes to indicate changes agreed upon by the team during the huddle. Patients with fewer than 20 alarms in the preceding 4h were not included; thus, sometimes fewer than 4 patients’ data were available for discussion. We hand-delivered dashboards to the charge nurses leading huddles, and they facilitated the multidisciplinary alarm discussions focused on reviewing alarm data and customizing settings to reduce unnecessary alarms.
Study Periods
The study had 3 periods as shown in Supplementary Figure 2: (1) 16-week baseline data collection, (2) phased intervention implementation during which we serially spent 2-8 weeks on each of the 4 intervention units implementing the intervention, and (3) 16-week postimplementation data collection.
Outcomes
The primary effectiveness outcome was the change in unit-level alarms per patient-day between the baseline and postimplementation periods in intervention versus control units, with all patients on the units included. The secondary effectiveness outcome (analyzed using the embedded cohort design) was the change in individual patient-level alarms between the 24 hours leading up to a huddle and the 24 hours following huddles in patients who were versus patients who were not discussed in huddles.
Implementation outcomes included adoption and fidelity measures. To measure adoption (defined as “intention to try” the intervention),16 we measured the frequency of discussions attended by patients’ nurses and physicians. We evaluated 3 elements of fidelity: adherence, dose, and quality of delivery.17 We measured adherence as the incorporation of alarm discussion into huddles when there were eligible patients to discuss. We measured dose as the average number of patients discussed on each unit per calendar day during the postimplementation period. We measured quality of delivery as the extent to which changes to monitoring that were agreed upon in the huddles were made at the bedside.
Safety Measures
To surveil for unintended consequences of reduced monitoring, we screened the hospital’s rapid response and code blue team database weekly for any events in patients previously discussed in huddles that occurred between huddle and hospital discharge. We reviewed charts to determine if the events were related to the intervention.
Randomization
Prior to randomization, the 8 units were divided into pairs based on participation in hospital-wide Joint Commission alarm management activities, use of alarm middleware that relayed detailed alarm information to nurses’ mobile phones, and baseline alarm rates. One unit in each pair was randomized to intervention and the other to control by coin flip.
Data Collection
We used Research Electronic Data Capture (REDCap)18 database tools.
Data for Unit-Level Analyses
We captured all alarms occurring on the study units during the study period using data from BedMasterEx. We obtained census data accurate to the hour from the Clinical Data Warehouse.
Data Captured in All Huddles
During each huddle, we collected the number of patients whose alarms were discussed, patient characteristics, presence of nurses and physicians, and monitoring changes agreed upon. We then followed up 4 hours later to determine if changes were made at the bedside by examining monitor settings.
Data Captured Only During Intensive Data Collection Days
We randomly selected 1 day during each of the 16 weeks of the postimplementation period to obtain additional patient-level data. On each intensive data collection day, the 4 monitored patients on each intervention and control unit with the most high-acuity alarms in the 4 hours prior to huddles occurring — regardless of whether or not these patients were later discussed in huddles — were identified for data collection. On these dates, a member of the research team reviewed each patient’s alarm counts in 4-hour blocks during the 24 hours before and after the huddle. Given that the huddles were not always at the same time every day (ranging between 10:00 and 13:00), we operationally set the huddle time as 12:00 for all units.
Data Analysis
We used Stata/SE 14.2 for all analyses.
Unit-Level Alarm Rates
To compare unit-level rates, we performed an interrupted time series analysis using segmented (piecewise) regression to evaluate the impact of the intervention.19,20 We used a multivariable generalized estimating equation model with the negative binomial distribution21 and clustering by unit. We bootstrapped the model and generated percentile-based 95% confidence intervals. We then used the model to estimate the alarm rate difference in differences between the baseline data collection period and the postimplementation data collection period for intervention versus control units.
Patient-Level Alarm Rates
In contrast to unit-level analysis, we used an embedded cohort design to model the change in individual patients’ alarms between the 24 hours leading up to huddles and the 24 hours following huddles in patients who were versus patients who were not discussed in huddles. The analysis was restricted to the patients included in intensive data collection days. We performed bootstrapped linear regression and generated percentile-based 95% confidence intervals using the difference in 4-hour block alarm rate between pre- and posthuddle as the outcome. We clustered within patients. We stratified by unit and preceding alarm rate. We modeled the alarm rate difference between the 24-hour prehuddle and the 24-hour posthuddle for huddled and nonhuddled patients and the difference in differences between exposure groups.
Implementation Outcomes
We summarized adoption and fidelity using proportions.
RESULTS
Alarm dashboards informed 580 structured alarm discussions during 353 safety huddles (huddles often included discussion of more than one patient).
Unit-Level Alarm Rates
Visually, alarm rates over time on each individual unit appeared flat despite the intervention (Supplementary Figure 3). Using piecewise regression, we found that intervention and control units had small increases in alarm rates between the baseline and postimplementation periods with a nonsignificant difference in these differences between the control and intervention groups (Table 1).
Patient-Level Alarm Rates
We then restricted the analysis to the patients whose data were collected during intensive data collection days. We obtained data from 1974 pre-post pairs of 4-hour time periods.
Implementation Outcomes
Adoption
The patient’s nurse attended 482 of the 580 huddle discussions (83.1%), and at least one of the patient’s physicians (resident, fellow, or attending) attended 394 (67.9%).
Fidelity: Adherence
In addition to the 353 huddles that included alarm discussion, 123 instances had no patients with ≥20 high acuity alarms in the preceding 4 hours therefore, no data were brought to the huddle. There were an additional 30 instances when a huddle did not occur or there was no alarm discussion in the huddle despite data being available. Thus, adherence occurred in 353 of 383 huddles (92.2%).
Fidelity: Dose
During the 112 calendar day postimplementation period, 379 patients’ alarms were discussed in huddles for an average intervention dose of 0.85 discussions per unit per calendar day.
Fidelity: Quality of Delivery
In 362 of the 580 huddle discussions (62.4%), changes were agreed upon. The most frequently agreed upon changes were discontinuing monitoring (32.0%), monitoring only when asleep or unsupervised (23.8%), widening heart rate parameters (12.7%), changing electrocardiographic leads/wires (8.6%), changing the pulse oximetry probe (8.0%), and increasing the delay time between when oxygen desaturation was detected and when the alarm was generated (4.7%). Of the huddle discussions with changes agreed upon, 346 (95.6%) changes were enacted at the bedside.
Safety Measures
There were 0 code blue events and 26 rapid response team activations for patients discussed in huddles. None were related to the intervention.
Discussion
Our main finding was that the huddle strategy was effective in safely reducing the burden of alarms for the high alarm pediatric ward patients whose alarms were discussed, but it did not reduce unit-level alarm rates. Implementation outcomes explained this finding. Although adoption and adherence were high, the overall dose of the intervention was low.
We also found that 36% of alarms had technical causes, the majority of which were related to the pulse oximetry probe detecting that it was off the patient or searching for a pulse. Although these alarms are likely perceived differently by clinical staff (most monitors generate different sounds for technical alarms), they still represent a substantial contribution to the alarm environment. Minimizing them in patients who must remain continuously monitored requires more intensive effort to implement other types of interventions than the main focus of this study, such as changing pulse oximetry probes and electrocardiographic leads/wires.
In one-third of huddles, monitoring was simply discontinued. We observed in many cases that, while these patients may have had legitimate indications for monitoring upon admission, their conditions had improved; after brief multidisciplinary discussion, the team concluded that monitoring was no longer indicated. This observation may suggest interventions at the ordering phase, such as prespecifying a monitoring duration.22,23
This study’s findings were consistent with a quasi-experimental study of safety huddle-based alarm discussions in a pediatric intensive care unit that showed a patient-level reduction of 116 alarms per patient-day in those discussed in huddles relative to controls.11 A smaller quasi-experimental study of implementing a nighttime alarm “ward round” in an adult intensive care unit showed a significant reduction in unit-level alarms/patient-day from 168 to 84.9 In a quality improvement report, a monitoring care process bundle that included discussion of alarm settings showed a reduction in unit-level alarms/patient-day from 180 to 40.10 Our study strengthens the body of literature using a cluster-randomized design, measuring patient- and unit-level outcomes, and including implementation outcomes that explain effectiveness findings.
On a hypothetical unit similar to the ones we studied with 20 occupied beds and 60 alarms/patient-day, an average of 1200 alarms would occur each day. We delivered the intervention to 0.85 patients per day. Changes were made at the bedside in 60% of those with the intervention delivered, and those patients had a difference in differences of 119 fewer alarms compared with the comparison patients on control units. In this scenario, we could expect a relative reduction of 0.85 x 0.60 x 119 = 61 fewer alarms/day total on the unit or a 5% reduction. However, that estimated reduction did not account for the arrival of new patients with high alarm rates, which certainly occurred in this study and explained the lack of effect at the unit level.
As described above, the intervention dose was low, which translated into a lack of effect at the unit level despite a strong effect at the patient level. This result was partly due to the manual process required to produce the alarm dashboards that restricted their availability to nonholiday weekdays. The study was performed at one hospital, which limited generalizability. The study hospital was already convening daily safety huddles that were well attended by nurses and physicians. Other hospitals without existing huddle structures may face challenges in implementing similar multidisciplinary alarm discussions. In addition, the study design was randomized at the unit (rather than patient) level, which limited our ability to balance potential confounders at the patient level.
Conclusion
A safety huddle intervention strategy to drive alarm customization was effective in safely reducing alarms for individual children discussed. However, unit-level alarm rates were not affected by the intervention due to a low dose. Leaders of efforts to reduce alarms should consider beginning with passive interventions (such as changes to default settings and alarm delays) and use huddle-based discussion as a second-line intervention to address remaining patients with high alarm rates.
Acknowledgments
We thank Matthew MacMurchy, BA, for his assistance with data collection.
Funding/Support
This study was supported by a Young Investigator Award (Bonafide, PI) from the Academic Pediatric Association.
Role of the Funder/Sponsor
The Academic Pediatric Association had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit for publication.
Disclosures
No relevant financial activities, aside from the grant funding from the Academic Pediatric Association listed above, are reported.
1. Schondelmeyer AC, Brady PW, Goel VV, et al. Physiologic monitor alarm rates at 5 children’s hospitals. J Hosp Med. 2018;In press. PubMed
2. Cvach M, Kitchens M, Smith K, Harris P, Flack MN. Customizing alarm limits based on specific needs of patients. Biomed Instrum Technol. 2017;51(3):227-234. PubMed
3. Bonafide CP, Lin R, Zander M, et al. Association between exposure to nonactionable physiologic monitor alarms and response time in a children’s hospital. J Hosp Med. 2015;10(6):345-351. PubMed
4. Bonafide CP, Localio AR, Holmes JH, et al. Video analysis of factors associated with response time to physiologic monitor alarms in a children’s hospital. JAMA Pediatr. 2017;171(6):524-531. PubMed
5. Lange K, Nowak M, Zoller R, Lauer W. Boundary conditions for safe detection of clinical alarms: An observational study to identify the cognitive and perceptual demands on an Intensive Care Unit. In: In: D. de Waard, K.A. Brookhuis, A. Toffetti, A. Stuiver, C. Weikert, D. Coelho, D. Manzey, A.B. Ünal, S. Röttger, and N. Merat (Eds.) Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2015 Annual Conference. Groningen, Netherlands; 2016.
6. Westbrook JI, Li L, Hooper TD, Raban MZ, Middleton S, Lehnbom EC. Effectiveness of a ‘Do not interrupt’ bundled intervention to reduce interruptions during medication administration: a cluster randomised controlled feasibility study. BMJ Qual Saf. 2017;26:734-742. PubMed
7. Chopra V, McMahon LF Jr. Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199-1200. PubMed
8. Turmell JW, Coke L, Catinella R, Hosford T, Majeski A. Alarm fatigue: use of an evidence-based alarm management strategy. J Nurs Care Qual. 2017;32(1):47-54. PubMed
9. Koerber JP, Walker J, Worsley M, Thorpe CM. An alarm ward round reduces the frequency of false alarms on the ICU at night. J Intensive Care Soc. 2011;12(1):75-76.
10. Dandoy CE, Davies SM, Flesch L, et al. A team-based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686-1694. PubMed
11. Dewan M, Wolfe H, Lin R, et al. Impact of a safety huddle–based intervention on monitor alarm rates in low-acuity pediatric intensive care unit patients. J Hosp Med. 2017;12(8):652-657. PubMed
12. Goldenhar LM, Brady PW, Sutcliffe KM, Muething SE. Huddling for high reliability and situation awareness. BMJ Qual Saf. 2013;22(11):899-906. PubMed
13. Brady PW, Muething S, Kotagal U, et al. Improving situation awareness to reduce unrecognized clinical deterioration and serious safety events. Pediatrics. 2013;131:e298-308. PubMed
14. Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217-226. PubMed
15. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4(1):50. PubMed
16. Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65-76. PubMed
17. Allen JD, Linnan LA, Emmons KM. Fidelity and its relationship to implementation effectiveness, adaptation, and dissemination. In: Dissemination and Implementation Research in Health: Translating Science to Practice (Brownson RC, Proctor EK, Colditz GA Eds.). Oxford University Press; 2012:281-304.
18. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42:377-381. PubMed
19. Singer JD, Willett JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press; 2003.
20. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27:299-309. PubMed
21. Gardner W, Mulvey EP, Shaw EC. Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychol Bull. 1995;118:392-404. PubMed
22. Dressler R, Dryer MM, Coletti C, Mahoney D, Doorey AJ. Altering overuse of cardiac telemetry in non-intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174(11):1852-1854. PubMed
23. Boggan JC, Navar-Boggan AM, Patel V, Schulteis RD, Simel DL. Reductions in telemetry order duration do not reduce telemetry utilization. J Hosp Med. 2014;9(12):795-796. PubMed
Physiologic monitor alarms occur frequently in the hospital environment, with average rates on pediatric wards between 42 and 155 alarms per monitored patient-day.1 However, average rates do not depict the full story, because only 9%–25% of patients are responsible for most alarms on inpatient wards.1,2 In addition, only 0.5%–1% of alarms on pediatric wards warrant action.3,4 Downstream consequences of high alarm rates include interruptions5,6 and alarm fatigue.3,4,7
Alarm customization, the process of reviewing individual patients’ alarm data and using that data to implement patient-specific alarm reduction interventions, has emerged as a potential approach to unit-wide alarm management.8-11 Potential customizations include broadening alarm thresholds, instituting delays between the time the alarm condition is met and the time the alarm sounds, and changing electrodes.8-11 However, the workflows within which to identify the patients who will benefit from customization, make decisions about how to customize, and implement customizations have not been delineated.
Safety huddles are brief structured discussions among physicians, nurses, and other staff aiming to identify and mitigate threats to patient safety.11-13 In this study, we aimed to evaluate the influence of a safety huddle-based alarm intervention strategy targeting high alarm pediatric ward patients on (a) unit-level alarm rates and (b) patient-level alarm rates, as well as to (c) evaluate implementation outcomes. We hypothesized that patients discussed in huddles would have greater reductions in alarm rates in the 24 hours following their huddle than patients who were not discussed. Given that most alarms are generated by a small fraction of patients,1,2 we hypothesized that patient-level reductions would translate to unit-level reductions.
METHODS
Human Subject Protection
The Institutional Review Board of Children’s Hospital of Philadelphia approved this study with a waiver of informed consent. We registered the study at ClinicalTrials.gov (identifier NCT02458872). The original protocol is available as an Online Supplement.
Design and Framework
For our secondary effectiveness outcome evaluating the effect of the intervention on the alarm rates of the individual patients discussed in huddles, we used a cohort design embedded within the trial to analyze patient-specific alarm data collected only on randomly selected “intensive data collection days,” described below and in Figure 1.
Setting and Subjects
All patients hospitalized on 8 units that admit general pediatric and medical subspecialty patients at Children’s Hospital of Philadelphia between June 15, 2015 and May 8, 2016 were included in the primary (unit-level) analysis. Every patient’s bedside included a General Electric Dash 3000 physiologic monitor. Decisions to monitor patients were made by physicians and required orders. Default alarm settings are available in Supplementary Table 1; these settings required orders to change.
All 8 units were already convening scheduled safety huddles led by the charge nurse each day. All nurses and at least one resident were expected to attend; attending physicians and fellows were welcome but not expected to attend. Huddles focused on discussing safety concerns and patient flow. None of the preexisting huddles included alarm discussion.
Intervention
For each nonholiday weekday, we generated customized paper-based alarm huddle data “dashboards” (Supplementary Figure 1) displaying data from the patients (up to a maximum of 4) on each intervention unit with the highest numbers of high-acuity alarms (“crisis” and “warning” audible alarms, see Supplementary Table 2 for detailed listing of alarm types) in the preceding 4 hours by reviewing data from the monitor network using BedMasterEx v4.2 (Excel Medical Electronics). Dashboards listed the most frequent types of alarms, alarm settings, and included a script for discussing the alarms with checkboxes to indicate changes agreed upon by the team during the huddle. Patients with fewer than 20 alarms in the preceding 4h were not included; thus, sometimes fewer than 4 patients’ data were available for discussion. We hand-delivered dashboards to the charge nurses leading huddles, and they facilitated the multidisciplinary alarm discussions focused on reviewing alarm data and customizing settings to reduce unnecessary alarms.
Study Periods
The study had 3 periods as shown in Supplementary Figure 2: (1) 16-week baseline data collection, (2) phased intervention implementation during which we serially spent 2-8 weeks on each of the 4 intervention units implementing the intervention, and (3) 16-week postimplementation data collection.
Outcomes
The primary effectiveness outcome was the change in unit-level alarms per patient-day between the baseline and postimplementation periods in intervention versus control units, with all patients on the units included. The secondary effectiveness outcome (analyzed using the embedded cohort design) was the change in individual patient-level alarms between the 24 hours leading up to a huddle and the 24 hours following huddles in patients who were versus patients who were not discussed in huddles.
Implementation outcomes included adoption and fidelity measures. To measure adoption (defined as “intention to try” the intervention),16 we measured the frequency of discussions attended by patients’ nurses and physicians. We evaluated 3 elements of fidelity: adherence, dose, and quality of delivery.17 We measured adherence as the incorporation of alarm discussion into huddles when there were eligible patients to discuss. We measured dose as the average number of patients discussed on each unit per calendar day during the postimplementation period. We measured quality of delivery as the extent to which changes to monitoring that were agreed upon in the huddles were made at the bedside.
Safety Measures
To surveil for unintended consequences of reduced monitoring, we screened the hospital’s rapid response and code blue team database weekly for any events in patients previously discussed in huddles that occurred between huddle and hospital discharge. We reviewed charts to determine if the events were related to the intervention.
Randomization
Prior to randomization, the 8 units were divided into pairs based on participation in hospital-wide Joint Commission alarm management activities, use of alarm middleware that relayed detailed alarm information to nurses’ mobile phones, and baseline alarm rates. One unit in each pair was randomized to intervention and the other to control by coin flip.
Data Collection
We used Research Electronic Data Capture (REDCap)18 database tools.
Data for Unit-Level Analyses
We captured all alarms occurring on the study units during the study period using data from BedMasterEx. We obtained census data accurate to the hour from the Clinical Data Warehouse.
Data Captured in All Huddles
During each huddle, we collected the number of patients whose alarms were discussed, patient characteristics, presence of nurses and physicians, and monitoring changes agreed upon. We then followed up 4 hours later to determine if changes were made at the bedside by examining monitor settings.
Data Captured Only During Intensive Data Collection Days
We randomly selected 1 day during each of the 16 weeks of the postimplementation period to obtain additional patient-level data. On each intensive data collection day, the 4 monitored patients on each intervention and control unit with the most high-acuity alarms in the 4 hours prior to huddles occurring — regardless of whether or not these patients were later discussed in huddles — were identified for data collection. On these dates, a member of the research team reviewed each patient’s alarm counts in 4-hour blocks during the 24 hours before and after the huddle. Given that the huddles were not always at the same time every day (ranging between 10:00 and 13:00), we operationally set the huddle time as 12:00 for all units.
Data Analysis
We used Stata/SE 14.2 for all analyses.
Unit-Level Alarm Rates
To compare unit-level rates, we performed an interrupted time series analysis using segmented (piecewise) regression to evaluate the impact of the intervention.19,20 We used a multivariable generalized estimating equation model with the negative binomial distribution21 and clustering by unit. We bootstrapped the model and generated percentile-based 95% confidence intervals. We then used the model to estimate the alarm rate difference in differences between the baseline data collection period and the postimplementation data collection period for intervention versus control units.
Patient-Level Alarm Rates
In contrast to unit-level analysis, we used an embedded cohort design to model the change in individual patients’ alarms between the 24 hours leading up to huddles and the 24 hours following huddles in patients who were versus patients who were not discussed in huddles. The analysis was restricted to the patients included in intensive data collection days. We performed bootstrapped linear regression and generated percentile-based 95% confidence intervals using the difference in 4-hour block alarm rate between pre- and posthuddle as the outcome. We clustered within patients. We stratified by unit and preceding alarm rate. We modeled the alarm rate difference between the 24-hour prehuddle and the 24-hour posthuddle for huddled and nonhuddled patients and the difference in differences between exposure groups.
Implementation Outcomes
We summarized adoption and fidelity using proportions.
RESULTS
Alarm dashboards informed 580 structured alarm discussions during 353 safety huddles (huddles often included discussion of more than one patient).
Unit-Level Alarm Rates
Visually, alarm rates over time on each individual unit appeared flat despite the intervention (Supplementary Figure 3). Using piecewise regression, we found that intervention and control units had small increases in alarm rates between the baseline and postimplementation periods with a nonsignificant difference in these differences between the control and intervention groups (Table 1).
Patient-Level Alarm Rates
We then restricted the analysis to the patients whose data were collected during intensive data collection days. We obtained data from 1974 pre-post pairs of 4-hour time periods.
Implementation Outcomes
Adoption
The patient’s nurse attended 482 of the 580 huddle discussions (83.1%), and at least one of the patient’s physicians (resident, fellow, or attending) attended 394 (67.9%).
Fidelity: Adherence
In addition to the 353 huddles that included alarm discussion, 123 instances had no patients with ≥20 high acuity alarms in the preceding 4 hours therefore, no data were brought to the huddle. There were an additional 30 instances when a huddle did not occur or there was no alarm discussion in the huddle despite data being available. Thus, adherence occurred in 353 of 383 huddles (92.2%).
Fidelity: Dose
During the 112 calendar day postimplementation period, 379 patients’ alarms were discussed in huddles for an average intervention dose of 0.85 discussions per unit per calendar day.
Fidelity: Quality of Delivery
In 362 of the 580 huddle discussions (62.4%), changes were agreed upon. The most frequently agreed upon changes were discontinuing monitoring (32.0%), monitoring only when asleep or unsupervised (23.8%), widening heart rate parameters (12.7%), changing electrocardiographic leads/wires (8.6%), changing the pulse oximetry probe (8.0%), and increasing the delay time between when oxygen desaturation was detected and when the alarm was generated (4.7%). Of the huddle discussions with changes agreed upon, 346 (95.6%) changes were enacted at the bedside.
Safety Measures
There were 0 code blue events and 26 rapid response team activations for patients discussed in huddles. None were related to the intervention.
Discussion
Our main finding was that the huddle strategy was effective in safely reducing the burden of alarms for the high alarm pediatric ward patients whose alarms were discussed, but it did not reduce unit-level alarm rates. Implementation outcomes explained this finding. Although adoption and adherence were high, the overall dose of the intervention was low.
We also found that 36% of alarms had technical causes, the majority of which were related to the pulse oximetry probe detecting that it was off the patient or searching for a pulse. Although these alarms are likely perceived differently by clinical staff (most monitors generate different sounds for technical alarms), they still represent a substantial contribution to the alarm environment. Minimizing them in patients who must remain continuously monitored requires more intensive effort to implement other types of interventions than the main focus of this study, such as changing pulse oximetry probes and electrocardiographic leads/wires.
In one-third of huddles, monitoring was simply discontinued. We observed in many cases that, while these patients may have had legitimate indications for monitoring upon admission, their conditions had improved; after brief multidisciplinary discussion, the team concluded that monitoring was no longer indicated. This observation may suggest interventions at the ordering phase, such as prespecifying a monitoring duration.22,23
This study’s findings were consistent with a quasi-experimental study of safety huddle-based alarm discussions in a pediatric intensive care unit that showed a patient-level reduction of 116 alarms per patient-day in those discussed in huddles relative to controls.11 A smaller quasi-experimental study of implementing a nighttime alarm “ward round” in an adult intensive care unit showed a significant reduction in unit-level alarms/patient-day from 168 to 84.9 In a quality improvement report, a monitoring care process bundle that included discussion of alarm settings showed a reduction in unit-level alarms/patient-day from 180 to 40.10 Our study strengthens the body of literature using a cluster-randomized design, measuring patient- and unit-level outcomes, and including implementation outcomes that explain effectiveness findings.
On a hypothetical unit similar to the ones we studied with 20 occupied beds and 60 alarms/patient-day, an average of 1200 alarms would occur each day. We delivered the intervention to 0.85 patients per day. Changes were made at the bedside in 60% of those with the intervention delivered, and those patients had a difference in differences of 119 fewer alarms compared with the comparison patients on control units. In this scenario, we could expect a relative reduction of 0.85 x 0.60 x 119 = 61 fewer alarms/day total on the unit or a 5% reduction. However, that estimated reduction did not account for the arrival of new patients with high alarm rates, which certainly occurred in this study and explained the lack of effect at the unit level.
As described above, the intervention dose was low, which translated into a lack of effect at the unit level despite a strong effect at the patient level. This result was partly due to the manual process required to produce the alarm dashboards that restricted their availability to nonholiday weekdays. The study was performed at one hospital, which limited generalizability. The study hospital was already convening daily safety huddles that were well attended by nurses and physicians. Other hospitals without existing huddle structures may face challenges in implementing similar multidisciplinary alarm discussions. In addition, the study design was randomized at the unit (rather than patient) level, which limited our ability to balance potential confounders at the patient level.
Conclusion
A safety huddle intervention strategy to drive alarm customization was effective in safely reducing alarms for individual children discussed. However, unit-level alarm rates were not affected by the intervention due to a low dose. Leaders of efforts to reduce alarms should consider beginning with passive interventions (such as changes to default settings and alarm delays) and use huddle-based discussion as a second-line intervention to address remaining patients with high alarm rates.
Acknowledgments
We thank Matthew MacMurchy, BA, for his assistance with data collection.
Funding/Support
This study was supported by a Young Investigator Award (Bonafide, PI) from the Academic Pediatric Association.
Role of the Funder/Sponsor
The Academic Pediatric Association had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit for publication.
Disclosures
No relevant financial activities, aside from the grant funding from the Academic Pediatric Association listed above, are reported.
Physiologic monitor alarms occur frequently in the hospital environment, with average rates on pediatric wards between 42 and 155 alarms per monitored patient-day.1 However, average rates do not depict the full story, because only 9%–25% of patients are responsible for most alarms on inpatient wards.1,2 In addition, only 0.5%–1% of alarms on pediatric wards warrant action.3,4 Downstream consequences of high alarm rates include interruptions5,6 and alarm fatigue.3,4,7
Alarm customization, the process of reviewing individual patients’ alarm data and using that data to implement patient-specific alarm reduction interventions, has emerged as a potential approach to unit-wide alarm management.8-11 Potential customizations include broadening alarm thresholds, instituting delays between the time the alarm condition is met and the time the alarm sounds, and changing electrodes.8-11 However, the workflows within which to identify the patients who will benefit from customization, make decisions about how to customize, and implement customizations have not been delineated.
Safety huddles are brief structured discussions among physicians, nurses, and other staff aiming to identify and mitigate threats to patient safety.11-13 In this study, we aimed to evaluate the influence of a safety huddle-based alarm intervention strategy targeting high alarm pediatric ward patients on (a) unit-level alarm rates and (b) patient-level alarm rates, as well as to (c) evaluate implementation outcomes. We hypothesized that patients discussed in huddles would have greater reductions in alarm rates in the 24 hours following their huddle than patients who were not discussed. Given that most alarms are generated by a small fraction of patients,1,2 we hypothesized that patient-level reductions would translate to unit-level reductions.
METHODS
Human Subject Protection
The Institutional Review Board of Children’s Hospital of Philadelphia approved this study with a waiver of informed consent. We registered the study at ClinicalTrials.gov (identifier NCT02458872). The original protocol is available as an Online Supplement.
Design and Framework
For our secondary effectiveness outcome evaluating the effect of the intervention on the alarm rates of the individual patients discussed in huddles, we used a cohort design embedded within the trial to analyze patient-specific alarm data collected only on randomly selected “intensive data collection days,” described below and in Figure 1.
Setting and Subjects
All patients hospitalized on 8 units that admit general pediatric and medical subspecialty patients at Children’s Hospital of Philadelphia between June 15, 2015 and May 8, 2016 were included in the primary (unit-level) analysis. Every patient’s bedside included a General Electric Dash 3000 physiologic monitor. Decisions to monitor patients were made by physicians and required orders. Default alarm settings are available in Supplementary Table 1; these settings required orders to change.
All 8 units were already convening scheduled safety huddles led by the charge nurse each day. All nurses and at least one resident were expected to attend; attending physicians and fellows were welcome but not expected to attend. Huddles focused on discussing safety concerns and patient flow. None of the preexisting huddles included alarm discussion.
Intervention
For each nonholiday weekday, we generated customized paper-based alarm huddle data “dashboards” (Supplementary Figure 1) displaying data from the patients (up to a maximum of 4) on each intervention unit with the highest numbers of high-acuity alarms (“crisis” and “warning” audible alarms, see Supplementary Table 2 for detailed listing of alarm types) in the preceding 4 hours by reviewing data from the monitor network using BedMasterEx v4.2 (Excel Medical Electronics). Dashboards listed the most frequent types of alarms, alarm settings, and included a script for discussing the alarms with checkboxes to indicate changes agreed upon by the team during the huddle. Patients with fewer than 20 alarms in the preceding 4h were not included; thus, sometimes fewer than 4 patients’ data were available for discussion. We hand-delivered dashboards to the charge nurses leading huddles, and they facilitated the multidisciplinary alarm discussions focused on reviewing alarm data and customizing settings to reduce unnecessary alarms.
Study Periods
The study had 3 periods as shown in Supplementary Figure 2: (1) 16-week baseline data collection, (2) phased intervention implementation during which we serially spent 2-8 weeks on each of the 4 intervention units implementing the intervention, and (3) 16-week postimplementation data collection.
Outcomes
The primary effectiveness outcome was the change in unit-level alarms per patient-day between the baseline and postimplementation periods in intervention versus control units, with all patients on the units included. The secondary effectiveness outcome (analyzed using the embedded cohort design) was the change in individual patient-level alarms between the 24 hours leading up to a huddle and the 24 hours following huddles in patients who were versus patients who were not discussed in huddles.
Implementation outcomes included adoption and fidelity measures. To measure adoption (defined as “intention to try” the intervention),16 we measured the frequency of discussions attended by patients’ nurses and physicians. We evaluated 3 elements of fidelity: adherence, dose, and quality of delivery.17 We measured adherence as the incorporation of alarm discussion into huddles when there were eligible patients to discuss. We measured dose as the average number of patients discussed on each unit per calendar day during the postimplementation period. We measured quality of delivery as the extent to which changes to monitoring that were agreed upon in the huddles were made at the bedside.
Safety Measures
To surveil for unintended consequences of reduced monitoring, we screened the hospital’s rapid response and code blue team database weekly for any events in patients previously discussed in huddles that occurred between huddle and hospital discharge. We reviewed charts to determine if the events were related to the intervention.
Randomization
Prior to randomization, the 8 units were divided into pairs based on participation in hospital-wide Joint Commission alarm management activities, use of alarm middleware that relayed detailed alarm information to nurses’ mobile phones, and baseline alarm rates. One unit in each pair was randomized to intervention and the other to control by coin flip.
Data Collection
We used Research Electronic Data Capture (REDCap)18 database tools.
Data for Unit-Level Analyses
We captured all alarms occurring on the study units during the study period using data from BedMasterEx. We obtained census data accurate to the hour from the Clinical Data Warehouse.
Data Captured in All Huddles
During each huddle, we collected the number of patients whose alarms were discussed, patient characteristics, presence of nurses and physicians, and monitoring changes agreed upon. We then followed up 4 hours later to determine if changes were made at the bedside by examining monitor settings.
Data Captured Only During Intensive Data Collection Days
We randomly selected 1 day during each of the 16 weeks of the postimplementation period to obtain additional patient-level data. On each intensive data collection day, the 4 monitored patients on each intervention and control unit with the most high-acuity alarms in the 4 hours prior to huddles occurring — regardless of whether or not these patients were later discussed in huddles — were identified for data collection. On these dates, a member of the research team reviewed each patient’s alarm counts in 4-hour blocks during the 24 hours before and after the huddle. Given that the huddles were not always at the same time every day (ranging between 10:00 and 13:00), we operationally set the huddle time as 12:00 for all units.
Data Analysis
We used Stata/SE 14.2 for all analyses.
Unit-Level Alarm Rates
To compare unit-level rates, we performed an interrupted time series analysis using segmented (piecewise) regression to evaluate the impact of the intervention.19,20 We used a multivariable generalized estimating equation model with the negative binomial distribution21 and clustering by unit. We bootstrapped the model and generated percentile-based 95% confidence intervals. We then used the model to estimate the alarm rate difference in differences between the baseline data collection period and the postimplementation data collection period for intervention versus control units.
Patient-Level Alarm Rates
In contrast to unit-level analysis, we used an embedded cohort design to model the change in individual patients’ alarms between the 24 hours leading up to huddles and the 24 hours following huddles in patients who were versus patients who were not discussed in huddles. The analysis was restricted to the patients included in intensive data collection days. We performed bootstrapped linear regression and generated percentile-based 95% confidence intervals using the difference in 4-hour block alarm rate between pre- and posthuddle as the outcome. We clustered within patients. We stratified by unit and preceding alarm rate. We modeled the alarm rate difference between the 24-hour prehuddle and the 24-hour posthuddle for huddled and nonhuddled patients and the difference in differences between exposure groups.
Implementation Outcomes
We summarized adoption and fidelity using proportions.
RESULTS
Alarm dashboards informed 580 structured alarm discussions during 353 safety huddles (huddles often included discussion of more than one patient).
Unit-Level Alarm Rates
Visually, alarm rates over time on each individual unit appeared flat despite the intervention (Supplementary Figure 3). Using piecewise regression, we found that intervention and control units had small increases in alarm rates between the baseline and postimplementation periods with a nonsignificant difference in these differences between the control and intervention groups (Table 1).
Patient-Level Alarm Rates
We then restricted the analysis to the patients whose data were collected during intensive data collection days. We obtained data from 1974 pre-post pairs of 4-hour time periods.
Implementation Outcomes
Adoption
The patient’s nurse attended 482 of the 580 huddle discussions (83.1%), and at least one of the patient’s physicians (resident, fellow, or attending) attended 394 (67.9%).
Fidelity: Adherence
In addition to the 353 huddles that included alarm discussion, 123 instances had no patients with ≥20 high acuity alarms in the preceding 4 hours therefore, no data were brought to the huddle. There were an additional 30 instances when a huddle did not occur or there was no alarm discussion in the huddle despite data being available. Thus, adherence occurred in 353 of 383 huddles (92.2%).
Fidelity: Dose
During the 112 calendar day postimplementation period, 379 patients’ alarms were discussed in huddles for an average intervention dose of 0.85 discussions per unit per calendar day.
Fidelity: Quality of Delivery
In 362 of the 580 huddle discussions (62.4%), changes were agreed upon. The most frequently agreed upon changes were discontinuing monitoring (32.0%), monitoring only when asleep or unsupervised (23.8%), widening heart rate parameters (12.7%), changing electrocardiographic leads/wires (8.6%), changing the pulse oximetry probe (8.0%), and increasing the delay time between when oxygen desaturation was detected and when the alarm was generated (4.7%). Of the huddle discussions with changes agreed upon, 346 (95.6%) changes were enacted at the bedside.
Safety Measures
There were 0 code blue events and 26 rapid response team activations for patients discussed in huddles. None were related to the intervention.
Discussion
Our main finding was that the huddle strategy was effective in safely reducing the burden of alarms for the high alarm pediatric ward patients whose alarms were discussed, but it did not reduce unit-level alarm rates. Implementation outcomes explained this finding. Although adoption and adherence were high, the overall dose of the intervention was low.
We also found that 36% of alarms had technical causes, the majority of which were related to the pulse oximetry probe detecting that it was off the patient or searching for a pulse. Although these alarms are likely perceived differently by clinical staff (most monitors generate different sounds for technical alarms), they still represent a substantial contribution to the alarm environment. Minimizing them in patients who must remain continuously monitored requires more intensive effort to implement other types of interventions than the main focus of this study, such as changing pulse oximetry probes and electrocardiographic leads/wires.
In one-third of huddles, monitoring was simply discontinued. We observed in many cases that, while these patients may have had legitimate indications for monitoring upon admission, their conditions had improved; after brief multidisciplinary discussion, the team concluded that monitoring was no longer indicated. This observation may suggest interventions at the ordering phase, such as prespecifying a monitoring duration.22,23
This study’s findings were consistent with a quasi-experimental study of safety huddle-based alarm discussions in a pediatric intensive care unit that showed a patient-level reduction of 116 alarms per patient-day in those discussed in huddles relative to controls.11 A smaller quasi-experimental study of implementing a nighttime alarm “ward round” in an adult intensive care unit showed a significant reduction in unit-level alarms/patient-day from 168 to 84.9 In a quality improvement report, a monitoring care process bundle that included discussion of alarm settings showed a reduction in unit-level alarms/patient-day from 180 to 40.10 Our study strengthens the body of literature using a cluster-randomized design, measuring patient- and unit-level outcomes, and including implementation outcomes that explain effectiveness findings.
On a hypothetical unit similar to the ones we studied with 20 occupied beds and 60 alarms/patient-day, an average of 1200 alarms would occur each day. We delivered the intervention to 0.85 patients per day. Changes were made at the bedside in 60% of those with the intervention delivered, and those patients had a difference in differences of 119 fewer alarms compared with the comparison patients on control units. In this scenario, we could expect a relative reduction of 0.85 x 0.60 x 119 = 61 fewer alarms/day total on the unit or a 5% reduction. However, that estimated reduction did not account for the arrival of new patients with high alarm rates, which certainly occurred in this study and explained the lack of effect at the unit level.
As described above, the intervention dose was low, which translated into a lack of effect at the unit level despite a strong effect at the patient level. This result was partly due to the manual process required to produce the alarm dashboards that restricted their availability to nonholiday weekdays. The study was performed at one hospital, which limited generalizability. The study hospital was already convening daily safety huddles that were well attended by nurses and physicians. Other hospitals without existing huddle structures may face challenges in implementing similar multidisciplinary alarm discussions. In addition, the study design was randomized at the unit (rather than patient) level, which limited our ability to balance potential confounders at the patient level.
Conclusion
A safety huddle intervention strategy to drive alarm customization was effective in safely reducing alarms for individual children discussed. However, unit-level alarm rates were not affected by the intervention due to a low dose. Leaders of efforts to reduce alarms should consider beginning with passive interventions (such as changes to default settings and alarm delays) and use huddle-based discussion as a second-line intervention to address remaining patients with high alarm rates.
Acknowledgments
We thank Matthew MacMurchy, BA, for his assistance with data collection.
Funding/Support
This study was supported by a Young Investigator Award (Bonafide, PI) from the Academic Pediatric Association.
Role of the Funder/Sponsor
The Academic Pediatric Association had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit for publication.
Disclosures
No relevant financial activities, aside from the grant funding from the Academic Pediatric Association listed above, are reported.
1. Schondelmeyer AC, Brady PW, Goel VV, et al. Physiologic monitor alarm rates at 5 children’s hospitals. J Hosp Med. 2018;In press. PubMed
2. Cvach M, Kitchens M, Smith K, Harris P, Flack MN. Customizing alarm limits based on specific needs of patients. Biomed Instrum Technol. 2017;51(3):227-234. PubMed
3. Bonafide CP, Lin R, Zander M, et al. Association between exposure to nonactionable physiologic monitor alarms and response time in a children’s hospital. J Hosp Med. 2015;10(6):345-351. PubMed
4. Bonafide CP, Localio AR, Holmes JH, et al. Video analysis of factors associated with response time to physiologic monitor alarms in a children’s hospital. JAMA Pediatr. 2017;171(6):524-531. PubMed
5. Lange K, Nowak M, Zoller R, Lauer W. Boundary conditions for safe detection of clinical alarms: An observational study to identify the cognitive and perceptual demands on an Intensive Care Unit. In: In: D. de Waard, K.A. Brookhuis, A. Toffetti, A. Stuiver, C. Weikert, D. Coelho, D. Manzey, A.B. Ünal, S. Röttger, and N. Merat (Eds.) Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2015 Annual Conference. Groningen, Netherlands; 2016.
6. Westbrook JI, Li L, Hooper TD, Raban MZ, Middleton S, Lehnbom EC. Effectiveness of a ‘Do not interrupt’ bundled intervention to reduce interruptions during medication administration: a cluster randomised controlled feasibility study. BMJ Qual Saf. 2017;26:734-742. PubMed
7. Chopra V, McMahon LF Jr. Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199-1200. PubMed
8. Turmell JW, Coke L, Catinella R, Hosford T, Majeski A. Alarm fatigue: use of an evidence-based alarm management strategy. J Nurs Care Qual. 2017;32(1):47-54. PubMed
9. Koerber JP, Walker J, Worsley M, Thorpe CM. An alarm ward round reduces the frequency of false alarms on the ICU at night. J Intensive Care Soc. 2011;12(1):75-76.
10. Dandoy CE, Davies SM, Flesch L, et al. A team-based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686-1694. PubMed
11. Dewan M, Wolfe H, Lin R, et al. Impact of a safety huddle–based intervention on monitor alarm rates in low-acuity pediatric intensive care unit patients. J Hosp Med. 2017;12(8):652-657. PubMed
12. Goldenhar LM, Brady PW, Sutcliffe KM, Muething SE. Huddling for high reliability and situation awareness. BMJ Qual Saf. 2013;22(11):899-906. PubMed
13. Brady PW, Muething S, Kotagal U, et al. Improving situation awareness to reduce unrecognized clinical deterioration and serious safety events. Pediatrics. 2013;131:e298-308. PubMed
14. Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217-226. PubMed
15. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4(1):50. PubMed
16. Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65-76. PubMed
17. Allen JD, Linnan LA, Emmons KM. Fidelity and its relationship to implementation effectiveness, adaptation, and dissemination. In: Dissemination and Implementation Research in Health: Translating Science to Practice (Brownson RC, Proctor EK, Colditz GA Eds.). Oxford University Press; 2012:281-304.
18. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42:377-381. PubMed
19. Singer JD, Willett JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press; 2003.
20. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27:299-309. PubMed
21. Gardner W, Mulvey EP, Shaw EC. Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychol Bull. 1995;118:392-404. PubMed
22. Dressler R, Dryer MM, Coletti C, Mahoney D, Doorey AJ. Altering overuse of cardiac telemetry in non-intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174(11):1852-1854. PubMed
23. Boggan JC, Navar-Boggan AM, Patel V, Schulteis RD, Simel DL. Reductions in telemetry order duration do not reduce telemetry utilization. J Hosp Med. 2014;9(12):795-796. PubMed
1. Schondelmeyer AC, Brady PW, Goel VV, et al. Physiologic monitor alarm rates at 5 children’s hospitals. J Hosp Med. 2018;In press. PubMed
2. Cvach M, Kitchens M, Smith K, Harris P, Flack MN. Customizing alarm limits based on specific needs of patients. Biomed Instrum Technol. 2017;51(3):227-234. PubMed
3. Bonafide CP, Lin R, Zander M, et al. Association between exposure to nonactionable physiologic monitor alarms and response time in a children’s hospital. J Hosp Med. 2015;10(6):345-351. PubMed
4. Bonafide CP, Localio AR, Holmes JH, et al. Video analysis of factors associated with response time to physiologic monitor alarms in a children’s hospital. JAMA Pediatr. 2017;171(6):524-531. PubMed
5. Lange K, Nowak M, Zoller R, Lauer W. Boundary conditions for safe detection of clinical alarms: An observational study to identify the cognitive and perceptual demands on an Intensive Care Unit. In: In: D. de Waard, K.A. Brookhuis, A. Toffetti, A. Stuiver, C. Weikert, D. Coelho, D. Manzey, A.B. Ünal, S. Röttger, and N. Merat (Eds.) Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2015 Annual Conference. Groningen, Netherlands; 2016.
6. Westbrook JI, Li L, Hooper TD, Raban MZ, Middleton S, Lehnbom EC. Effectiveness of a ‘Do not interrupt’ bundled intervention to reduce interruptions during medication administration: a cluster randomised controlled feasibility study. BMJ Qual Saf. 2017;26:734-742. PubMed
7. Chopra V, McMahon LF Jr. Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199-1200. PubMed
8. Turmell JW, Coke L, Catinella R, Hosford T, Majeski A. Alarm fatigue: use of an evidence-based alarm management strategy. J Nurs Care Qual. 2017;32(1):47-54. PubMed
9. Koerber JP, Walker J, Worsley M, Thorpe CM. An alarm ward round reduces the frequency of false alarms on the ICU at night. J Intensive Care Soc. 2011;12(1):75-76.
10. Dandoy CE, Davies SM, Flesch L, et al. A team-based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686-1694. PubMed
11. Dewan M, Wolfe H, Lin R, et al. Impact of a safety huddle–based intervention on monitor alarm rates in low-acuity pediatric intensive care unit patients. J Hosp Med. 2017;12(8):652-657. PubMed
12. Goldenhar LM, Brady PW, Sutcliffe KM, Muething SE. Huddling for high reliability and situation awareness. BMJ Qual Saf. 2013;22(11):899-906. PubMed
13. Brady PW, Muething S, Kotagal U, et al. Improving situation awareness to reduce unrecognized clinical deterioration and serious safety events. Pediatrics. 2013;131:e298-308. PubMed
14. Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217-226. PubMed
15. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4(1):50. PubMed
16. Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65-76. PubMed
17. Allen JD, Linnan LA, Emmons KM. Fidelity and its relationship to implementation effectiveness, adaptation, and dissemination. In: Dissemination and Implementation Research in Health: Translating Science to Practice (Brownson RC, Proctor EK, Colditz GA Eds.). Oxford University Press; 2012:281-304.
18. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42:377-381. PubMed
19. Singer JD, Willett JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press; 2003.
20. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27:299-309. PubMed
21. Gardner W, Mulvey EP, Shaw EC. Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychol Bull. 1995;118:392-404. PubMed
22. Dressler R, Dryer MM, Coletti C, Mahoney D, Doorey AJ. Altering overuse of cardiac telemetry in non-intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174(11):1852-1854. PubMed
23. Boggan JC, Navar-Boggan AM, Patel V, Schulteis RD, Simel DL. Reductions in telemetry order duration do not reduce telemetry utilization. J Hosp Med. 2014;9(12):795-796. PubMed
© 2018 Society of Hospital Medicine
Review of Physiologic Monitor Alarms
Clinical alarm safety has become a recent target for improvement in many hospitals. In 2013, The Joint Commission released a National Patient Safety Goal prompting accredited hospitals to establish alarm safety as a hospital priority, identify the most important alarm signals to manage, and, by 2016, develop policies and procedures that address alarm management.[1] In addition, the Emergency Care Research Institute has named alarm hazards the top health technology hazard each year since 2012.[2]
The primary arguments supporting the elevation of alarm management to a national hospital priority in the United States include the following: (1) clinicians rely on alarms to notify them of important physiologic changes, (2) alarms occur frequently and usually do not warrant clinical intervention, and (3) alarm overload renders clinicians unable to respond to all alarms, resulting in alarm fatigue: responding more slowly or ignoring alarms that may represent actual clinical deterioration.[3, 4] These arguments are built largely on anecdotal data, reported safety event databases, and small studies that have not previously been systematically analyzed.
Despite the national focus on alarms, we still know very little about fundamental questions key to improving alarm safety. In this systematic review, we aimed to answer 3 key questions about physiologic monitor alarms: (1) What proportion of alarms warrant attention or clinical intervention (ie, actionable alarms), and how does this proportion vary between adult and pediatric populations and between intensive care unit (ICU) and ward settings? (2) What is the relationship between alarm exposure and clinician response time? (3) What interventions are effective in reducing the frequency of alarms?
We limited our scope to monitor alarms because few studies have evaluated the characteristics of alarms from other medical devices, and because missing relevant monitor alarms could adversely impact patient safety.
METHODS
We performed a systematic review of the literature in accordance with the Meta‐Analysis of Observational Studies in Epidemiology guidelines[5] and developed this manuscript using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement.[6]
Eligibility Criteria
With help from an experienced biomedical librarian (C.D.S.), we searched PubMed, the Cumulative Index to Nursing and Allied Health Literature, Scopus, Cochrane Library,
We included peer‐reviewed, original research studies published in English, Spanish, or French that addressed the questions outlined above. Eligible patient populations were children and adults admitted to hospital inpatient units and emergency departments (EDs). We excluded alarms in procedural suites or operating rooms (typically responded to by anesthesiologists already with the patient) because of the differences in environment of care, staff‐to‐patient ratio, and equipment. We included observational studies reporting the actionability of physiologic monitor alarms (ie, alarms warranting special attention or clinical intervention), as well as nurse responses to these alarms. We excluded studies focused on the effects of alarms unrelated to patient safety, such as families' and patients' stress, noise, or sleep disturbance. We included only intervention studies evaluating pragmatic interventions ready for clinical implementation (ie, not experimental devices or software algorithms).
Selection Process and Data Extraction
First, 2 authors screened the titles and abstracts of articles for eligibility. To maximize sensitivity, if at least 1 author considered the article relevant, the article proceeded to full‐text review. Second, the full texts of articles screened were independently reviewed by 2 authors in an unblinded fashion to determine their eligibility. Any disagreements concerning eligibility were resolved by team consensus. To assure consistency in eligibility determinations across the team, a core group of the authors (C.W.P, C.P.B., E.E., and V.V.G.) held a series of meetings to review and discuss each potentially eligible article and reach consensus on the final list of included articles. Two authors independently extracted the following characteristics from included studies: alarm review methods, analytic design, fidelity measurement, consideration of unintended adverse safety consequences, and key results. Reviewers were not blinded to journal, authors, or affiliations.
Synthesis of Results and Risk Assessment
Given the high degree of heterogeneity in methodology, we were unable to generate summary proportions of the observational studies or perform a meta‐analysis of the intervention studies. Thus, we organized the studies into clinically relevant categories and presented key aspects in tables. Due to the heterogeneity of the studies and the controversy surrounding quality scores,[5] we did not generate summary scores of study quality. Instead, we evaluated and reported key design elements that had the potential to bias the results. To recognize the more comprehensive studies in the field, we developed by consensus a set of characteristics that distinguished studies with lower risk of bias. These characteristics are shown and defined in Table 1.
| First Author and Publication Year | Alarm Review Method | Indicators of Potential Bias for Observational Studies | Indicators of Potential Bias for Intervention Studies | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Monitor System | Direct Observation | Medical Record Review | Rhythm Annotation | Video Observation | Remote Monitoring Staff | Medical Device Industry Involved | Two Independent Reviewers | At Least 1 Reviewer Is a Clinical Expert | Reviewer Not Simultaneously in Patient Care | Clear Definition of Alarm Actionability | Census Included | Statistical Testing or QI SPC Methods | Fidelity Assessed | Safety Assessed | Lower Risk of Bias | |
| ||||||||||||||||
| Adult Observational | ||||||||||||||||
| Atzema 2006[7] | ✓* | ✓ | ✓ | |||||||||||||
| Billinghurst 2003[8] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Biot 2000[9] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Chambrin 1999[10] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Drew 2014[11] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
| Gazarian 2014[12] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Grges 2009[13] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Gross 2011[15] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Inokuchi 2013[14] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Koski 1990[16] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Morales Snchez 2014[17] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Pergher 2014[18] | ✓ | ✓ | ||||||||||||||
| Siebig 2010[19] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Voepel‐Lewis 2013[20] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Way 2014[21] | ✓ | ✓ | ✓ | |||||||||||||
| Pediatric Observational | ||||||||||||||||
| Bonafide 2015[22] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Lawless 1994[23] | ✓ | ✓ | ||||||||||||||
| Rosman 2013[24] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Talley 2011[25] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Tsien 1997[26] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| van Pul 2015[27] | ✓ | |||||||||||||||
| Varpio 2012[28] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Mixed Adult and Pediatric Observational | ||||||||||||||||
| O'Carroll 1986[29] | ✓ | |||||||||||||||
| Wiklund 1994[30] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Adult Intervention | ||||||||||||||||
| Albert 2015[32] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Cvach 2013[33] | ✓ | ✓ | ||||||||||||||
| Cvach 2014[34] | ✓ | ✓ | ||||||||||||||
| Graham 2010[35] | ✓ | |||||||||||||||
| Rheineck‐Leyssius 1997[36] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Taenzer 2010[31] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Whalen 2014[37] | ✓ | ✓ | ✓ | |||||||||||||
| Pediatric Intervention | ||||||||||||||||
| Dandoy 2014[38] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
For the purposes of this review, we defined nonactionable alarms as including both invalid (false) alarms that do not that accurately represent the physiologic status of the patient and alarms that are valid but do not warrant special attention or clinical intervention (nuisance alarms). We did not separate out invalid alarms due to the tremendous variation between studies in how validity was measured.
RESULTS
Study Selection
Search results produced 4629 articles (see the flow diagram in the Supporting Information in the online version of this article), of which 32 articles were eligible: 24 observational studies describing alarm characteristics and 8 studies describing interventions to reduce alarm frequency.
Observational Study Characteristics
Characteristics of included studies are shown in Table 1. Of the 24 observational studies,[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] 15 included adult patients,[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] 7 included pediatric patients,[22, 23, 24, 25, 26, 27, 28] and 2 included both adult and pediatric patients.[29, 30] All were single‐hospital studies, except for 1 study by Chambrin and colleagues[10] that included 5 sites. The number of patient‐hours examined in each study ranged from 60 to 113,880.[7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30] Hospital settings included ICUs (n = 16),[9, 10, 11, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 29] general wards (n = 5),[12, 15, 20, 22, 28] EDs (n = 2),[7, 21] postanesthesia care unit (PACU) (n = 1),[30] and cardiac care unit (CCU) (n = 1).[8] Studies varied in the type of physiologic signals recorded and data collection methods, ranging from direct observation by a nurse who was simultaneously caring for patients[29] to video recording with expert review.[14, 19, 22] Four observational studies met the criteria for lower risk of bias.[11, 14, 15, 22]
Intervention Study Characteristics
Of the 8 intervention studies, 7 included adult patients,[31, 32, 33, 34, 35, 36, 37] and 1 included pediatric patients.[38] All were single‐hospital studies; 6 were quasi‐experimental[31, 33, 34, 35, 37, 38] and 2 were experimental.[32, 36] Settings included progressive care units (n = 3),[33, 34, 35] CCUs (n = 3),[32, 33, 37] wards (n = 2),[31, 38] PACU (n = 1),[36] and a step‐down unit (n = 1).[32] All except 1 study[32] used the monitoring system to record alarm data. Several studies evaluated multicomponent interventions that included combinations of the following: widening alarm parameters,[31, 35, 36, 37, 38] instituting alarm delays,[31, 34, 36, 38] reconfiguring alarm acuity,[35, 37] use of secondary notifications,[34] daily change of electrocardiographic electrodes or use of disposable electrocardiographic wires,[32, 33, 38] universal monitoring in high‐risk populations,[31] and timely discontinuation of monitoring in low‐risk populations.[38] Four intervention studies met our prespecified lower risk of bias criteria.[31, 32, 36, 38]
Proportion of Alarms Considered Actionable
Results of the observational studies are provided in Table 2. The proportion of alarms that were actionable was <1% to 26% in adult ICU settings,[9, 10, 11, 13, 14, 16, 17, 19] 20% to 36% in adult ward settings,[12, 15, 20] 17% in a mixed adult and pediatric PACU setting,[30] 3% to 13% in pediatric ICU settings,[22, 23, 24, 25, 26] and 1% in a pediatric ward setting.[22]
| Signals Included | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| First Author and Publication Year | Setting | Monitored Patient‐Hours | SpO2 | ECG Arrhythmia | ECG Parametersa | Blood Pressure | Total Alarms | Actionable Alarms | Alarm Response | Lower Risk of Bias |
| ||||||||||
| Adult | ||||||||||
| Atzema 2006[7] | ED | 371 | ✓ | 1,762 | 0.20% | |||||
| Billinghurst 2003[8] | CCU | 420 | ✓ | 751 | Not reported; 17% were valid | Nurses with higher acuity patients and smaller % of valid alarms had slower response rates | ||||
| Biot 2000[9] | ICU | 250 | ✓ | ✓ | ✓ | ✓ | 3,665 | 3% | ||
| Chambrin 1999[10] | ICU | 1,971 | ✓ | ✓ | ✓ | ✓ | 3,188 | 26% | ||
| Drew 2014[11] | ICU | 48,173 | ✓ | ✓ | ✓ | ✓ | 2,558,760 | 0.3% of 3,861 VT alarms | ✓ | |
| Gazarian 2014[12] | Ward | 54 nurse‐hours | ✓ | ✓ | ✓ | 205 | 22% | Response to 47% of alarms | ||
| Grges 2009[13] | ICU | 200 | ✓ | ✓ | ✓ | ✓ | 1,214 | 5% | ||
| Gross 2011[15] | Ward | 530 | ✓ | ✓ | ✓ | ✓ | 4,393 | 20% | ✓ | |
| Inokuchi 2013[14] | ICU | 2,697 | ✓ | ✓ | ✓ | ✓ | 11,591 | 6% | ✓ | |
| Koski 1990[16] | ICU | 400 | ✓ | ✓ | 2,322 | 12% | ||||
| Morales Snchez 2014[17] | ICU | 434 sessions | ✓ | ✓ | ✓ | 215 | 25% | Response to 93% of alarms, of which 50% were within 10 seconds | ||
| Pergher 2014[18] | ICU | 60 | ✓ | 76 | Not reported | 72% of alarms stopped before nurse response or had >10 minutes response time | ||||
| Siebig 2010[19] | ICU | 982 | ✓ | ✓ | ✓ | ✓ | 5,934 | 15% | ||
| Voepel‐Lewis 2013[20] | Ward | 1,616 | ✓ | 710 | 36% | Response time was longer for patients in highest quartile of total alarms | ||||
| Way 2014[21] | ED | 93 | ✓ | ✓ | ✓ | ✓ | 572 | Not reported; 75% were valid | Nurses responded to more alarms in resuscitation room vs acute care area, but response time was longer | |
| Pediatric | ||||||||||
| Bonafide 2015[22] | Ward + ICU | 210 | ✓ | ✓ | ✓ | ✓ | 5,070 | 13% PICU, 1% ward | Incremental increases in response time as number of nonactionable alarms in preceding 120 minutes increased | ✓ |
| Lawless 1994[23] | ICU | 928 | ✓ | ✓ | ✓ | 2,176 | 6% | |||
| Rosman 2013[24] | ICU | 8,232 | ✓ | ✓ | ✓ | ✓ | 54,656 | 4% of rhythm alarms true critical" | ||
| Talley 2011[25] | ICU | 1,470∥ | ✓ | ✓ | ✓ | ✓ | 2,245 | 3% | ||
| Tsien 1997[26] | ICU | 298 | ✓ | ✓ | ✓ | 2,942 | 8% | |||
| van Pul 2015[27] | ICU | 113,880∥ | ✓ | ✓ | ✓ | ✓ | 222,751 | Not reported | Assigned nurse did not respond to 6% of alarms within 45 seconds | |
| Varpio 2012[28] | Ward | 49 unit‐hours | ✓ | ✓ | ✓ | ✓ | 446 | Not reported | 70% of all alarms and 41% of crisis alarms were not responded to within 1 minute | |
| Both | ||||||||||
| O'Carroll 1986[29] | ICU | 2,258∥ | ✓ | 284 | 2% | |||||
| Wiklund 1994[30] | PACU | 207 | ✓ | ✓ | ✓ | 1,891 | 17% | |||
Relationship Between Alarm Exposure and Response Time
Whereas 9 studies addressed response time,[8, 12, 17, 18, 20, 21, 22, 27, 28] only 2 evaluated the relationship between alarm burden and nurse response time.[20, 22] Voepel‐Lewis and colleagues found that nurse responses were slower to patients with the highest quartile of alarms (57.6 seconds) compared to those with the lowest (45.4 seconds) or medium (42.3 seconds) quartiles of alarms on an adult ward (P = 0.046). They did not find an association between false alarm exposure and response time.[20] Bonafide and colleagues found incremental increases in response time as the number of nonactionable alarms in the preceding 120 minutes increased (P < 0.001 in the pediatric ICU, P = 0.009 on the pediatric ward).[22]
Interventions Effective in Reducing Alarms
Results of the 8 intervention studies are provided in Table 3. Three studies evaluated single interventions;[32, 33, 36] the remainder of the studies tested interventions with multiple components such that it was impossible to separate the effect of each component. Below, we have summarized study results, arranged by component. Because only 1 study focused on pediatric patients,[38] results from pediatric and adult settings are combined.
| First Author and Publication Year | Design | Setting | Main Intervention Components | Other/ Comments | Key Results | Results Statistically Significant? | Lower Risk of Bias | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Widen Default Settings | Alarm Delays | Reconfigure Alarm Acuity | Secondary Notification | ECG Changes | |||||||
| |||||||||||
| Adult | |||||||||||
| Albert 2015[32] | Experimental (cluster‐randomized) | CCU | ✓ | Disposable vs reusable wires | Disposable leads had 29% fewer no‐telemetry, leads‐fail, and leads‐off alarms and similar artifact alarms | ✓ | ✓ | ||||
| Cvach 2013[33] | Quasi‐experimental (before and after) | CCU and PCU | ✓ | Daily change of electrodes | 46% fewer alarms/bed/day | ||||||
| Cvach 2014[34] | Quasi‐experimental (ITS) | PCU | ✓* | ✓ | Slope of regression line suggests decrease of 0.75 alarms/bed/day | ||||||
| Graham 2010[35] | Quasi‐experimental (before and after) | PCU | ✓ | ✓ | 43% fewer crisis, warning, and system warning alarms on unit | ||||||
| Rheineck‐Leyssius 1997[36] | Experimental (RCT) | PACU | ✓ | ✓ | Alarm limit of 85% had fewer alarms/patient but higher incidence of true hypoxemia for >1 minute (6% vs 2%) | ✓ | ✓ | ||||
| Taenzer 2010[31] | Quasi‐experimental (before and after with concurrent controls) | Ward | ✓ | ✓ | Universal SpO2 monitoring | Rescue events decreased from 3.4 to 1.2 per 1,000 discharges; transfers to ICU decreased from 5.6 to 2.9 per 1,000 patient‐days, only 4 alarms/patient‐day | ✓ | ✓ | |||
| Whalen 2014[37] | Quasi‐experimental (before and after) | CCU | ✓ | ✓ | 89% fewer audible alarms on unit | ✓ | |||||
| Pediatric | |||||||||||
| Dandoy 2014[38] | Quasi‐experimental (ITS) | Ward | ✓ | ✓ | ✓ | Timely monitor discontinuation; daily change of ECG electrodes | Decrease in alarms/patient‐days from 180 to 40 | ✓ | |||
Widening alarm parameter default settings was evaluated in 5 studies:[31, 35, 36, 37, 38] 1 single intervention randomized controlled trial (RCT),[36] and 4 multiple‐intervention, quasi‐experimental studies.[31, 35, 37, 38] In the RCT, using a lower SpO2 limit of 85% instead of the standard 90% resulted in 61% fewer alarms. In the 4 multiple intervention studies, 1 study reported significant reductions in alarm rates (P < 0.001),[37] 1 study did not report preintervention alarm rates but reported a postintervention alarm rate of 4 alarms per patient‐day,[31] and 2 studies reported reductions in alarm rates but did not report any statistical testing.[35, 38] Of the 3 studies examining patient safety, 1 study with universal monitoring reported fewer rescue events and transfers to the ICU postimplementation,[31] 1 study reported no missed acute decompensations,[38] and 1 study (the RCT) reported significantly more true hypoxemia events (P = 0.001).[36]
Alarm delays were evaluated in 4 studies:[31, 34, 36, 38] 3 multiple‐intervention, quasi‐experimental studies[31, 34, 38] and 1 retrospective analysis of data from an RCT.[36] One study combined alarm delays with widening defaults in a universal monitoring strategy and reported a postintervention alarm rate of 4 alarms per patient.[31] Another study evaluated delays as part of a secondary notification pager system and found a negatively sloping regression line that suggested a decreasing alarm rate, but did not report statistical testing.[34] The third study reported a reduction in alarm rates but did not report statistical testing.[38] The RCT compared the impact of a hypothetical 15‐second alarm delay to that of a lower SpO2 limit reduction and reported a similar reduction in alarms.[36] Of the 4 studies examining patient safety, 1 study with universal monitoring reported improvements,[31] 2 studies reported no adverse outcomes,[35, 38] and the retrospective analysis of data from the RCT reported the theoretical adverse outcome of delayed detection of sudden, severe desaturations.[36]
Reconfiguring alarm acuity was evaluated in 2 studies, both of which were multiple‐intervention quasi‐experimental studies.[35, 37] Both showed reductions in alarm rates: 1 was significant without increasing adverse events (P < 0.001),[37] and the other did not report statistical testing or safety outcomes.[35]
Secondary notification of nurses using pagers was the main intervention component of 1 study incorporating delays between the alarms and the alarm pages.[34] As mentioned above, a negatively sloping regression line was displayed, but no statistical testing or safety outcomes were reported.
Disposable electrocardiographic lead wires or daily electrode changes were evaluated in 3 studies:[32, 33, 38] 1 single intervention cluster‐randomized trial[32] and 2 quasi‐experimental studies.[33, 38] In the cluster‐randomized trial, disposable lead wires were compared to reusable lead wires, with disposable lead wires having significantly fewer technical alarms for lead signal failures (P = 0.03) but a similar number of monitoring artifact alarms (P = 0.44).[32] In a single‐intervention, quasi‐experimental study, daily electrode change showed a reduction in alarms, but no statistical testing was reported.[33] One multiple‐intervention, quasi‐experimental study incorporating daily electrode change showed fewer alarms without statistical testing.[38] Of the 2 studies examining patient safety, both reported no adverse outcomes.[32, 38]
DISCUSSION
This systematic review of physiologic monitor alarms in the hospital yielded the following main findings: (1) between 74% and 99% of physiologic monitor alarms were not actionable, (2) a significant relationship between alarm exposure and nurse response time was demonstrated in 2 small observational studies, and (3) although interventions were most often studied in combination, results from the studies with lower risk of bias suggest that widening alarm parameters, implementing alarm delays, and using disposable electrocardiographic lead wires and/or changing electrodes daily are the most promising interventions for reducing alarms. Only 5 of 8 intervention studies measured intervention safety and found that widening alarm parameters and implementing alarm delays had mixed safety outcomes, whereas disposable electrocardiographic lead wires and daily electrode changes had no adverse safety outcomes.[29, 30, 34, 35, 36] Safety measures are crucial to ensuring the highest level of patient safety is met; interventions are rendered useless without ensuring actionable alarms are not disabled. The variation in results across studies likely reflects the wide range of care settings as well as differences in design and quality.
This field is still in its infancy, with 18 of the 32 articles published in the past 5 years. We anticipate improvements in quality and rigor as the field matures, as well as clinically tested interventions that incorporate smart alarms. Smart alarms integrate data from multiple physiologic signals and the patient's history to better detect physiologic changes in the patient and improve the positive predictive value of alarms. Academicindustry partnerships will be required to implement and rigorously test smart alarms and other emerging technologies in the hospital.
To our knowledge, this is the first systematic review focused on monitor alarms with specific review questions relevant to alarm fatigue. Cvach recently published an integrative review of alarm fatigue using research published through 2011.[39] Our review builds upon her work by contributing a more extensive and systematic search strategy with databases spanning nursing, medicine, and engineering, including additional languages, and including newer studies published through April 2015. In addition, we included multiple cross‐team checks in our eligibility review to ensure high sensitivity and specificity of the resulting set of studies.
Although we focused on interventions aiming to reduce alarms, there has also been important recent work focused on reducing telemetry utilization in adult hospital populations as well as work focused on reducing pulse oximetry utilization in children admitted with respiratory conditions. Dressler and colleagues reported an immediate and sustained reduction in telemetry utilization in hospitalized adults upon redesign of cardiac telemetry order sets to include the clinical indication, which defaulted to the American Heart Association guideline‐recommended telemetry duration.[40] Instructions for bedside nurses were also included in the order set to facilitate appropriate telemetry discontinuation. Schondelmeyer and colleagues reported reductions in continuous pulse oximetry utilization in hospitalized children with asthma and bronchiolitis upon introduction of a multifaceted quality improvement program that included provider education, a nurse handoff checklist, and discontinuation criteria incorporated into order sets.[41]
Limitations of This Review and the Underlying Body of Work
There are limitations to this systematic review and its underlying body of work. With respect to our approach to this systematic review, we focused only on monitor alarms. Numerous other medical devices generate alarms in the patient‐care environment that also can contribute to alarm fatigue and deserve equally rigorous evaluation. With respect to the underlying body of work, the quality of individual studies was generally low. For example, determinations of alarm actionability were often made by a single rater without evaluation of the reliability or validity of these determinations, and statistical testing was often missing. There were also limitations specific to intervention studies, including evaluation of nongeneralizable patient populations, failure to measure the fidelity of the interventions, inadequate measures of intervention safety, and failure to statistically evaluate alarm reductions. Finally, though not necessarily a limitation, several studies were conducted by authors involved in or funded by the medical device industry.[11, 15, 19, 31, 32] This has the potential to introduce bias, although we have no indication that the quality of the science was adversely impacted.
Moving forward, the research agenda for physiologic monitor alarms should include the following: (1) more intensive focus on evaluating the relationship between alarm exposure and response time with analysis of important mediating factors that may promote or prevent alarm fatigue, (2) emphasis on studying interventions aimed at improving alarm management using rigorous designs such as cluster‐randomized trials and trials randomized by individual participant, (3) monitoring and reporting clinically meaningful balancing measures that represent unintended consequences of disabling or delaying potentially important alarms and possibly reducing the clinicians' ability to detect true patient deterioration and intervene in a timely manner, and (4) support for transparent academicindustry partnerships to evaluate new alarm technology in real‐world settings. As evidence‐based interventions emerge, there will be new opportunities to study different implementation strategies of these interventions to optimize effectiveness.
CONCLUSIONS
The body of literature relevant to physiologic monitor alarm characteristics and alarm fatigue is limited but growing rapidly. Although we know that most alarms are not actionable and that there appears to be a relationship between alarm exposure and response time that could be caused by alarm fatigue, we cannot yet say with certainty that we know which interventions are most effective in safely reducing unnecessary alarms. Interventions that appear most promising and should be prioritized for intensive evaluation include widening alarm parameters, implementing alarm delays, and using disposable electrocardiographic lead wires and changing electrodes daily. Careful evaluation of these interventions must include systematically examining adverse patient safety consequences.
Acknowledgements
The authors thank Amogh Karnik and Micheal Sellars for their technical assistance during the review and extraction process.
Disclosures: Ms. Zander is supported by the Society of Hospital Medicine Student Hospitalist Scholar Grant. Dr. Bonafide and Ms. Stemler are supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors report no conflicts of interest.
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Clinical alarm safety has become a recent target for improvement in many hospitals. In 2013, The Joint Commission released a National Patient Safety Goal prompting accredited hospitals to establish alarm safety as a hospital priority, identify the most important alarm signals to manage, and, by 2016, develop policies and procedures that address alarm management.[1] In addition, the Emergency Care Research Institute has named alarm hazards the top health technology hazard each year since 2012.[2]
The primary arguments supporting the elevation of alarm management to a national hospital priority in the United States include the following: (1) clinicians rely on alarms to notify them of important physiologic changes, (2) alarms occur frequently and usually do not warrant clinical intervention, and (3) alarm overload renders clinicians unable to respond to all alarms, resulting in alarm fatigue: responding more slowly or ignoring alarms that may represent actual clinical deterioration.[3, 4] These arguments are built largely on anecdotal data, reported safety event databases, and small studies that have not previously been systematically analyzed.
Despite the national focus on alarms, we still know very little about fundamental questions key to improving alarm safety. In this systematic review, we aimed to answer 3 key questions about physiologic monitor alarms: (1) What proportion of alarms warrant attention or clinical intervention (ie, actionable alarms), and how does this proportion vary between adult and pediatric populations and between intensive care unit (ICU) and ward settings? (2) What is the relationship between alarm exposure and clinician response time? (3) What interventions are effective in reducing the frequency of alarms?
We limited our scope to monitor alarms because few studies have evaluated the characteristics of alarms from other medical devices, and because missing relevant monitor alarms could adversely impact patient safety.
METHODS
We performed a systematic review of the literature in accordance with the Meta‐Analysis of Observational Studies in Epidemiology guidelines[5] and developed this manuscript using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement.[6]
Eligibility Criteria
With help from an experienced biomedical librarian (C.D.S.), we searched PubMed, the Cumulative Index to Nursing and Allied Health Literature, Scopus, Cochrane Library,
We included peer‐reviewed, original research studies published in English, Spanish, or French that addressed the questions outlined above. Eligible patient populations were children and adults admitted to hospital inpatient units and emergency departments (EDs). We excluded alarms in procedural suites or operating rooms (typically responded to by anesthesiologists already with the patient) because of the differences in environment of care, staff‐to‐patient ratio, and equipment. We included observational studies reporting the actionability of physiologic monitor alarms (ie, alarms warranting special attention or clinical intervention), as well as nurse responses to these alarms. We excluded studies focused on the effects of alarms unrelated to patient safety, such as families' and patients' stress, noise, or sleep disturbance. We included only intervention studies evaluating pragmatic interventions ready for clinical implementation (ie, not experimental devices or software algorithms).
Selection Process and Data Extraction
First, 2 authors screened the titles and abstracts of articles for eligibility. To maximize sensitivity, if at least 1 author considered the article relevant, the article proceeded to full‐text review. Second, the full texts of articles screened were independently reviewed by 2 authors in an unblinded fashion to determine their eligibility. Any disagreements concerning eligibility were resolved by team consensus. To assure consistency in eligibility determinations across the team, a core group of the authors (C.W.P, C.P.B., E.E., and V.V.G.) held a series of meetings to review and discuss each potentially eligible article and reach consensus on the final list of included articles. Two authors independently extracted the following characteristics from included studies: alarm review methods, analytic design, fidelity measurement, consideration of unintended adverse safety consequences, and key results. Reviewers were not blinded to journal, authors, or affiliations.
Synthesis of Results and Risk Assessment
Given the high degree of heterogeneity in methodology, we were unable to generate summary proportions of the observational studies or perform a meta‐analysis of the intervention studies. Thus, we organized the studies into clinically relevant categories and presented key aspects in tables. Due to the heterogeneity of the studies and the controversy surrounding quality scores,[5] we did not generate summary scores of study quality. Instead, we evaluated and reported key design elements that had the potential to bias the results. To recognize the more comprehensive studies in the field, we developed by consensus a set of characteristics that distinguished studies with lower risk of bias. These characteristics are shown and defined in Table 1.
| First Author and Publication Year | Alarm Review Method | Indicators of Potential Bias for Observational Studies | Indicators of Potential Bias for Intervention Studies | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Monitor System | Direct Observation | Medical Record Review | Rhythm Annotation | Video Observation | Remote Monitoring Staff | Medical Device Industry Involved | Two Independent Reviewers | At Least 1 Reviewer Is a Clinical Expert | Reviewer Not Simultaneously in Patient Care | Clear Definition of Alarm Actionability | Census Included | Statistical Testing or QI SPC Methods | Fidelity Assessed | Safety Assessed | Lower Risk of Bias | |
| ||||||||||||||||
| Adult Observational | ||||||||||||||||
| Atzema 2006[7] | ✓* | ✓ | ✓ | |||||||||||||
| Billinghurst 2003[8] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Biot 2000[9] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Chambrin 1999[10] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Drew 2014[11] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
| Gazarian 2014[12] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Grges 2009[13] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Gross 2011[15] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Inokuchi 2013[14] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Koski 1990[16] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Morales Snchez 2014[17] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Pergher 2014[18] | ✓ | ✓ | ||||||||||||||
| Siebig 2010[19] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Voepel‐Lewis 2013[20] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Way 2014[21] | ✓ | ✓ | ✓ | |||||||||||||
| Pediatric Observational | ||||||||||||||||
| Bonafide 2015[22] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Lawless 1994[23] | ✓ | ✓ | ||||||||||||||
| Rosman 2013[24] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Talley 2011[25] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Tsien 1997[26] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| van Pul 2015[27] | ✓ | |||||||||||||||
| Varpio 2012[28] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Mixed Adult and Pediatric Observational | ||||||||||||||||
| O'Carroll 1986[29] | ✓ | |||||||||||||||
| Wiklund 1994[30] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Adult Intervention | ||||||||||||||||
| Albert 2015[32] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Cvach 2013[33] | ✓ | ✓ | ||||||||||||||
| Cvach 2014[34] | ✓ | ✓ | ||||||||||||||
| Graham 2010[35] | ✓ | |||||||||||||||
| Rheineck‐Leyssius 1997[36] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Taenzer 2010[31] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Whalen 2014[37] | ✓ | ✓ | ✓ | |||||||||||||
| Pediatric Intervention | ||||||||||||||||
| Dandoy 2014[38] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
For the purposes of this review, we defined nonactionable alarms as including both invalid (false) alarms that do not that accurately represent the physiologic status of the patient and alarms that are valid but do not warrant special attention or clinical intervention (nuisance alarms). We did not separate out invalid alarms due to the tremendous variation between studies in how validity was measured.
RESULTS
Study Selection
Search results produced 4629 articles (see the flow diagram in the Supporting Information in the online version of this article), of which 32 articles were eligible: 24 observational studies describing alarm characteristics and 8 studies describing interventions to reduce alarm frequency.
Observational Study Characteristics
Characteristics of included studies are shown in Table 1. Of the 24 observational studies,[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] 15 included adult patients,[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] 7 included pediatric patients,[22, 23, 24, 25, 26, 27, 28] and 2 included both adult and pediatric patients.[29, 30] All were single‐hospital studies, except for 1 study by Chambrin and colleagues[10] that included 5 sites. The number of patient‐hours examined in each study ranged from 60 to 113,880.[7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30] Hospital settings included ICUs (n = 16),[9, 10, 11, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 29] general wards (n = 5),[12, 15, 20, 22, 28] EDs (n = 2),[7, 21] postanesthesia care unit (PACU) (n = 1),[30] and cardiac care unit (CCU) (n = 1).[8] Studies varied in the type of physiologic signals recorded and data collection methods, ranging from direct observation by a nurse who was simultaneously caring for patients[29] to video recording with expert review.[14, 19, 22] Four observational studies met the criteria for lower risk of bias.[11, 14, 15, 22]
Intervention Study Characteristics
Of the 8 intervention studies, 7 included adult patients,[31, 32, 33, 34, 35, 36, 37] and 1 included pediatric patients.[38] All were single‐hospital studies; 6 were quasi‐experimental[31, 33, 34, 35, 37, 38] and 2 were experimental.[32, 36] Settings included progressive care units (n = 3),[33, 34, 35] CCUs (n = 3),[32, 33, 37] wards (n = 2),[31, 38] PACU (n = 1),[36] and a step‐down unit (n = 1).[32] All except 1 study[32] used the monitoring system to record alarm data. Several studies evaluated multicomponent interventions that included combinations of the following: widening alarm parameters,[31, 35, 36, 37, 38] instituting alarm delays,[31, 34, 36, 38] reconfiguring alarm acuity,[35, 37] use of secondary notifications,[34] daily change of electrocardiographic electrodes or use of disposable electrocardiographic wires,[32, 33, 38] universal monitoring in high‐risk populations,[31] and timely discontinuation of monitoring in low‐risk populations.[38] Four intervention studies met our prespecified lower risk of bias criteria.[31, 32, 36, 38]
Proportion of Alarms Considered Actionable
Results of the observational studies are provided in Table 2. The proportion of alarms that were actionable was <1% to 26% in adult ICU settings,[9, 10, 11, 13, 14, 16, 17, 19] 20% to 36% in adult ward settings,[12, 15, 20] 17% in a mixed adult and pediatric PACU setting,[30] 3% to 13% in pediatric ICU settings,[22, 23, 24, 25, 26] and 1% in a pediatric ward setting.[22]
| Signals Included | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| First Author and Publication Year | Setting | Monitored Patient‐Hours | SpO2 | ECG Arrhythmia | ECG Parametersa | Blood Pressure | Total Alarms | Actionable Alarms | Alarm Response | Lower Risk of Bias |
| ||||||||||
| Adult | ||||||||||
| Atzema 2006[7] | ED | 371 | ✓ | 1,762 | 0.20% | |||||
| Billinghurst 2003[8] | CCU | 420 | ✓ | 751 | Not reported; 17% were valid | Nurses with higher acuity patients and smaller % of valid alarms had slower response rates | ||||
| Biot 2000[9] | ICU | 250 | ✓ | ✓ | ✓ | ✓ | 3,665 | 3% | ||
| Chambrin 1999[10] | ICU | 1,971 | ✓ | ✓ | ✓ | ✓ | 3,188 | 26% | ||
| Drew 2014[11] | ICU | 48,173 | ✓ | ✓ | ✓ | ✓ | 2,558,760 | 0.3% of 3,861 VT alarms | ✓ | |
| Gazarian 2014[12] | Ward | 54 nurse‐hours | ✓ | ✓ | ✓ | 205 | 22% | Response to 47% of alarms | ||
| Grges 2009[13] | ICU | 200 | ✓ | ✓ | ✓ | ✓ | 1,214 | 5% | ||
| Gross 2011[15] | Ward | 530 | ✓ | ✓ | ✓ | ✓ | 4,393 | 20% | ✓ | |
| Inokuchi 2013[14] | ICU | 2,697 | ✓ | ✓ | ✓ | ✓ | 11,591 | 6% | ✓ | |
| Koski 1990[16] | ICU | 400 | ✓ | ✓ | 2,322 | 12% | ||||
| Morales Snchez 2014[17] | ICU | 434 sessions | ✓ | ✓ | ✓ | 215 | 25% | Response to 93% of alarms, of which 50% were within 10 seconds | ||
| Pergher 2014[18] | ICU | 60 | ✓ | 76 | Not reported | 72% of alarms stopped before nurse response or had >10 minutes response time | ||||
| Siebig 2010[19] | ICU | 982 | ✓ | ✓ | ✓ | ✓ | 5,934 | 15% | ||
| Voepel‐Lewis 2013[20] | Ward | 1,616 | ✓ | 710 | 36% | Response time was longer for patients in highest quartile of total alarms | ||||
| Way 2014[21] | ED | 93 | ✓ | ✓ | ✓ | ✓ | 572 | Not reported; 75% were valid | Nurses responded to more alarms in resuscitation room vs acute care area, but response time was longer | |
| Pediatric | ||||||||||
| Bonafide 2015[22] | Ward + ICU | 210 | ✓ | ✓ | ✓ | ✓ | 5,070 | 13% PICU, 1% ward | Incremental increases in response time as number of nonactionable alarms in preceding 120 minutes increased | ✓ |
| Lawless 1994[23] | ICU | 928 | ✓ | ✓ | ✓ | 2,176 | 6% | |||
| Rosman 2013[24] | ICU | 8,232 | ✓ | ✓ | ✓ | ✓ | 54,656 | 4% of rhythm alarms true critical" | ||
| Talley 2011[25] | ICU | 1,470∥ | ✓ | ✓ | ✓ | ✓ | 2,245 | 3% | ||
| Tsien 1997[26] | ICU | 298 | ✓ | ✓ | ✓ | 2,942 | 8% | |||
| van Pul 2015[27] | ICU | 113,880∥ | ✓ | ✓ | ✓ | ✓ | 222,751 | Not reported | Assigned nurse did not respond to 6% of alarms within 45 seconds | |
| Varpio 2012[28] | Ward | 49 unit‐hours | ✓ | ✓ | ✓ | ✓ | 446 | Not reported | 70% of all alarms and 41% of crisis alarms were not responded to within 1 minute | |
| Both | ||||||||||
| O'Carroll 1986[29] | ICU | 2,258∥ | ✓ | 284 | 2% | |||||
| Wiklund 1994[30] | PACU | 207 | ✓ | ✓ | ✓ | 1,891 | 17% | |||
Relationship Between Alarm Exposure and Response Time
Whereas 9 studies addressed response time,[8, 12, 17, 18, 20, 21, 22, 27, 28] only 2 evaluated the relationship between alarm burden and nurse response time.[20, 22] Voepel‐Lewis and colleagues found that nurse responses were slower to patients with the highest quartile of alarms (57.6 seconds) compared to those with the lowest (45.4 seconds) or medium (42.3 seconds) quartiles of alarms on an adult ward (P = 0.046). They did not find an association between false alarm exposure and response time.[20] Bonafide and colleagues found incremental increases in response time as the number of nonactionable alarms in the preceding 120 minutes increased (P < 0.001 in the pediatric ICU, P = 0.009 on the pediatric ward).[22]
Interventions Effective in Reducing Alarms
Results of the 8 intervention studies are provided in Table 3. Three studies evaluated single interventions;[32, 33, 36] the remainder of the studies tested interventions with multiple components such that it was impossible to separate the effect of each component. Below, we have summarized study results, arranged by component. Because only 1 study focused on pediatric patients,[38] results from pediatric and adult settings are combined.
| First Author and Publication Year | Design | Setting | Main Intervention Components | Other/ Comments | Key Results | Results Statistically Significant? | Lower Risk of Bias | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Widen Default Settings | Alarm Delays | Reconfigure Alarm Acuity | Secondary Notification | ECG Changes | |||||||
| |||||||||||
| Adult | |||||||||||
| Albert 2015[32] | Experimental (cluster‐randomized) | CCU | ✓ | Disposable vs reusable wires | Disposable leads had 29% fewer no‐telemetry, leads‐fail, and leads‐off alarms and similar artifact alarms | ✓ | ✓ | ||||
| Cvach 2013[33] | Quasi‐experimental (before and after) | CCU and PCU | ✓ | Daily change of electrodes | 46% fewer alarms/bed/day | ||||||
| Cvach 2014[34] | Quasi‐experimental (ITS) | PCU | ✓* | ✓ | Slope of regression line suggests decrease of 0.75 alarms/bed/day | ||||||
| Graham 2010[35] | Quasi‐experimental (before and after) | PCU | ✓ | ✓ | 43% fewer crisis, warning, and system warning alarms on unit | ||||||
| Rheineck‐Leyssius 1997[36] | Experimental (RCT) | PACU | ✓ | ✓ | Alarm limit of 85% had fewer alarms/patient but higher incidence of true hypoxemia for >1 minute (6% vs 2%) | ✓ | ✓ | ||||
| Taenzer 2010[31] | Quasi‐experimental (before and after with concurrent controls) | Ward | ✓ | ✓ | Universal SpO2 monitoring | Rescue events decreased from 3.4 to 1.2 per 1,000 discharges; transfers to ICU decreased from 5.6 to 2.9 per 1,000 patient‐days, only 4 alarms/patient‐day | ✓ | ✓ | |||
| Whalen 2014[37] | Quasi‐experimental (before and after) | CCU | ✓ | ✓ | 89% fewer audible alarms on unit | ✓ | |||||
| Pediatric | |||||||||||
| Dandoy 2014[38] | Quasi‐experimental (ITS) | Ward | ✓ | ✓ | ✓ | Timely monitor discontinuation; daily change of ECG electrodes | Decrease in alarms/patient‐days from 180 to 40 | ✓ | |||
Widening alarm parameter default settings was evaluated in 5 studies:[31, 35, 36, 37, 38] 1 single intervention randomized controlled trial (RCT),[36] and 4 multiple‐intervention, quasi‐experimental studies.[31, 35, 37, 38] In the RCT, using a lower SpO2 limit of 85% instead of the standard 90% resulted in 61% fewer alarms. In the 4 multiple intervention studies, 1 study reported significant reductions in alarm rates (P < 0.001),[37] 1 study did not report preintervention alarm rates but reported a postintervention alarm rate of 4 alarms per patient‐day,[31] and 2 studies reported reductions in alarm rates but did not report any statistical testing.[35, 38] Of the 3 studies examining patient safety, 1 study with universal monitoring reported fewer rescue events and transfers to the ICU postimplementation,[31] 1 study reported no missed acute decompensations,[38] and 1 study (the RCT) reported significantly more true hypoxemia events (P = 0.001).[36]
Alarm delays were evaluated in 4 studies:[31, 34, 36, 38] 3 multiple‐intervention, quasi‐experimental studies[31, 34, 38] and 1 retrospective analysis of data from an RCT.[36] One study combined alarm delays with widening defaults in a universal monitoring strategy and reported a postintervention alarm rate of 4 alarms per patient.[31] Another study evaluated delays as part of a secondary notification pager system and found a negatively sloping regression line that suggested a decreasing alarm rate, but did not report statistical testing.[34] The third study reported a reduction in alarm rates but did not report statistical testing.[38] The RCT compared the impact of a hypothetical 15‐second alarm delay to that of a lower SpO2 limit reduction and reported a similar reduction in alarms.[36] Of the 4 studies examining patient safety, 1 study with universal monitoring reported improvements,[31] 2 studies reported no adverse outcomes,[35, 38] and the retrospective analysis of data from the RCT reported the theoretical adverse outcome of delayed detection of sudden, severe desaturations.[36]
Reconfiguring alarm acuity was evaluated in 2 studies, both of which were multiple‐intervention quasi‐experimental studies.[35, 37] Both showed reductions in alarm rates: 1 was significant without increasing adverse events (P < 0.001),[37] and the other did not report statistical testing or safety outcomes.[35]
Secondary notification of nurses using pagers was the main intervention component of 1 study incorporating delays between the alarms and the alarm pages.[34] As mentioned above, a negatively sloping regression line was displayed, but no statistical testing or safety outcomes were reported.
Disposable electrocardiographic lead wires or daily electrode changes were evaluated in 3 studies:[32, 33, 38] 1 single intervention cluster‐randomized trial[32] and 2 quasi‐experimental studies.[33, 38] In the cluster‐randomized trial, disposable lead wires were compared to reusable lead wires, with disposable lead wires having significantly fewer technical alarms for lead signal failures (P = 0.03) but a similar number of monitoring artifact alarms (P = 0.44).[32] In a single‐intervention, quasi‐experimental study, daily electrode change showed a reduction in alarms, but no statistical testing was reported.[33] One multiple‐intervention, quasi‐experimental study incorporating daily electrode change showed fewer alarms without statistical testing.[38] Of the 2 studies examining patient safety, both reported no adverse outcomes.[32, 38]
DISCUSSION
This systematic review of physiologic monitor alarms in the hospital yielded the following main findings: (1) between 74% and 99% of physiologic monitor alarms were not actionable, (2) a significant relationship between alarm exposure and nurse response time was demonstrated in 2 small observational studies, and (3) although interventions were most often studied in combination, results from the studies with lower risk of bias suggest that widening alarm parameters, implementing alarm delays, and using disposable electrocardiographic lead wires and/or changing electrodes daily are the most promising interventions for reducing alarms. Only 5 of 8 intervention studies measured intervention safety and found that widening alarm parameters and implementing alarm delays had mixed safety outcomes, whereas disposable electrocardiographic lead wires and daily electrode changes had no adverse safety outcomes.[29, 30, 34, 35, 36] Safety measures are crucial to ensuring the highest level of patient safety is met; interventions are rendered useless without ensuring actionable alarms are not disabled. The variation in results across studies likely reflects the wide range of care settings as well as differences in design and quality.
This field is still in its infancy, with 18 of the 32 articles published in the past 5 years. We anticipate improvements in quality and rigor as the field matures, as well as clinically tested interventions that incorporate smart alarms. Smart alarms integrate data from multiple physiologic signals and the patient's history to better detect physiologic changes in the patient and improve the positive predictive value of alarms. Academicindustry partnerships will be required to implement and rigorously test smart alarms and other emerging technologies in the hospital.
To our knowledge, this is the first systematic review focused on monitor alarms with specific review questions relevant to alarm fatigue. Cvach recently published an integrative review of alarm fatigue using research published through 2011.[39] Our review builds upon her work by contributing a more extensive and systematic search strategy with databases spanning nursing, medicine, and engineering, including additional languages, and including newer studies published through April 2015. In addition, we included multiple cross‐team checks in our eligibility review to ensure high sensitivity and specificity of the resulting set of studies.
Although we focused on interventions aiming to reduce alarms, there has also been important recent work focused on reducing telemetry utilization in adult hospital populations as well as work focused on reducing pulse oximetry utilization in children admitted with respiratory conditions. Dressler and colleagues reported an immediate and sustained reduction in telemetry utilization in hospitalized adults upon redesign of cardiac telemetry order sets to include the clinical indication, which defaulted to the American Heart Association guideline‐recommended telemetry duration.[40] Instructions for bedside nurses were also included in the order set to facilitate appropriate telemetry discontinuation. Schondelmeyer and colleagues reported reductions in continuous pulse oximetry utilization in hospitalized children with asthma and bronchiolitis upon introduction of a multifaceted quality improvement program that included provider education, a nurse handoff checklist, and discontinuation criteria incorporated into order sets.[41]
Limitations of This Review and the Underlying Body of Work
There are limitations to this systematic review and its underlying body of work. With respect to our approach to this systematic review, we focused only on monitor alarms. Numerous other medical devices generate alarms in the patient‐care environment that also can contribute to alarm fatigue and deserve equally rigorous evaluation. With respect to the underlying body of work, the quality of individual studies was generally low. For example, determinations of alarm actionability were often made by a single rater without evaluation of the reliability or validity of these determinations, and statistical testing was often missing. There were also limitations specific to intervention studies, including evaluation of nongeneralizable patient populations, failure to measure the fidelity of the interventions, inadequate measures of intervention safety, and failure to statistically evaluate alarm reductions. Finally, though not necessarily a limitation, several studies were conducted by authors involved in or funded by the medical device industry.[11, 15, 19, 31, 32] This has the potential to introduce bias, although we have no indication that the quality of the science was adversely impacted.
Moving forward, the research agenda for physiologic monitor alarms should include the following: (1) more intensive focus on evaluating the relationship between alarm exposure and response time with analysis of important mediating factors that may promote or prevent alarm fatigue, (2) emphasis on studying interventions aimed at improving alarm management using rigorous designs such as cluster‐randomized trials and trials randomized by individual participant, (3) monitoring and reporting clinically meaningful balancing measures that represent unintended consequences of disabling or delaying potentially important alarms and possibly reducing the clinicians' ability to detect true patient deterioration and intervene in a timely manner, and (4) support for transparent academicindustry partnerships to evaluate new alarm technology in real‐world settings. As evidence‐based interventions emerge, there will be new opportunities to study different implementation strategies of these interventions to optimize effectiveness.
CONCLUSIONS
The body of literature relevant to physiologic monitor alarm characteristics and alarm fatigue is limited but growing rapidly. Although we know that most alarms are not actionable and that there appears to be a relationship between alarm exposure and response time that could be caused by alarm fatigue, we cannot yet say with certainty that we know which interventions are most effective in safely reducing unnecessary alarms. Interventions that appear most promising and should be prioritized for intensive evaluation include widening alarm parameters, implementing alarm delays, and using disposable electrocardiographic lead wires and changing electrodes daily. Careful evaluation of these interventions must include systematically examining adverse patient safety consequences.
Acknowledgements
The authors thank Amogh Karnik and Micheal Sellars for their technical assistance during the review and extraction process.
Disclosures: Ms. Zander is supported by the Society of Hospital Medicine Student Hospitalist Scholar Grant. Dr. Bonafide and Ms. Stemler are supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors report no conflicts of interest.
Clinical alarm safety has become a recent target for improvement in many hospitals. In 2013, The Joint Commission released a National Patient Safety Goal prompting accredited hospitals to establish alarm safety as a hospital priority, identify the most important alarm signals to manage, and, by 2016, develop policies and procedures that address alarm management.[1] In addition, the Emergency Care Research Institute has named alarm hazards the top health technology hazard each year since 2012.[2]
The primary arguments supporting the elevation of alarm management to a national hospital priority in the United States include the following: (1) clinicians rely on alarms to notify them of important physiologic changes, (2) alarms occur frequently and usually do not warrant clinical intervention, and (3) alarm overload renders clinicians unable to respond to all alarms, resulting in alarm fatigue: responding more slowly or ignoring alarms that may represent actual clinical deterioration.[3, 4] These arguments are built largely on anecdotal data, reported safety event databases, and small studies that have not previously been systematically analyzed.
Despite the national focus on alarms, we still know very little about fundamental questions key to improving alarm safety. In this systematic review, we aimed to answer 3 key questions about physiologic monitor alarms: (1) What proportion of alarms warrant attention or clinical intervention (ie, actionable alarms), and how does this proportion vary between adult and pediatric populations and between intensive care unit (ICU) and ward settings? (2) What is the relationship between alarm exposure and clinician response time? (3) What interventions are effective in reducing the frequency of alarms?
We limited our scope to monitor alarms because few studies have evaluated the characteristics of alarms from other medical devices, and because missing relevant monitor alarms could adversely impact patient safety.
METHODS
We performed a systematic review of the literature in accordance with the Meta‐Analysis of Observational Studies in Epidemiology guidelines[5] and developed this manuscript using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement.[6]
Eligibility Criteria
With help from an experienced biomedical librarian (C.D.S.), we searched PubMed, the Cumulative Index to Nursing and Allied Health Literature, Scopus, Cochrane Library,
We included peer‐reviewed, original research studies published in English, Spanish, or French that addressed the questions outlined above. Eligible patient populations were children and adults admitted to hospital inpatient units and emergency departments (EDs). We excluded alarms in procedural suites or operating rooms (typically responded to by anesthesiologists already with the patient) because of the differences in environment of care, staff‐to‐patient ratio, and equipment. We included observational studies reporting the actionability of physiologic monitor alarms (ie, alarms warranting special attention or clinical intervention), as well as nurse responses to these alarms. We excluded studies focused on the effects of alarms unrelated to patient safety, such as families' and patients' stress, noise, or sleep disturbance. We included only intervention studies evaluating pragmatic interventions ready for clinical implementation (ie, not experimental devices or software algorithms).
Selection Process and Data Extraction
First, 2 authors screened the titles and abstracts of articles for eligibility. To maximize sensitivity, if at least 1 author considered the article relevant, the article proceeded to full‐text review. Second, the full texts of articles screened were independently reviewed by 2 authors in an unblinded fashion to determine their eligibility. Any disagreements concerning eligibility were resolved by team consensus. To assure consistency in eligibility determinations across the team, a core group of the authors (C.W.P, C.P.B., E.E., and V.V.G.) held a series of meetings to review and discuss each potentially eligible article and reach consensus on the final list of included articles. Two authors independently extracted the following characteristics from included studies: alarm review methods, analytic design, fidelity measurement, consideration of unintended adverse safety consequences, and key results. Reviewers were not blinded to journal, authors, or affiliations.
Synthesis of Results and Risk Assessment
Given the high degree of heterogeneity in methodology, we were unable to generate summary proportions of the observational studies or perform a meta‐analysis of the intervention studies. Thus, we organized the studies into clinically relevant categories and presented key aspects in tables. Due to the heterogeneity of the studies and the controversy surrounding quality scores,[5] we did not generate summary scores of study quality. Instead, we evaluated and reported key design elements that had the potential to bias the results. To recognize the more comprehensive studies in the field, we developed by consensus a set of characteristics that distinguished studies with lower risk of bias. These characteristics are shown and defined in Table 1.
| First Author and Publication Year | Alarm Review Method | Indicators of Potential Bias for Observational Studies | Indicators of Potential Bias for Intervention Studies | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Monitor System | Direct Observation | Medical Record Review | Rhythm Annotation | Video Observation | Remote Monitoring Staff | Medical Device Industry Involved | Two Independent Reviewers | At Least 1 Reviewer Is a Clinical Expert | Reviewer Not Simultaneously in Patient Care | Clear Definition of Alarm Actionability | Census Included | Statistical Testing or QI SPC Methods | Fidelity Assessed | Safety Assessed | Lower Risk of Bias | |
| ||||||||||||||||
| Adult Observational | ||||||||||||||||
| Atzema 2006[7] | ✓* | ✓ | ✓ | |||||||||||||
| Billinghurst 2003[8] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Biot 2000[9] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Chambrin 1999[10] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Drew 2014[11] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||
| Gazarian 2014[12] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Grges 2009[13] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Gross 2011[15] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Inokuchi 2013[14] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Koski 1990[16] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Morales Snchez 2014[17] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Pergher 2014[18] | ✓ | ✓ | ||||||||||||||
| Siebig 2010[19] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Voepel‐Lewis 2013[20] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Way 2014[21] | ✓ | ✓ | ✓ | |||||||||||||
| Pediatric Observational | ||||||||||||||||
| Bonafide 2015[22] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Lawless 1994[23] | ✓ | ✓ | ||||||||||||||
| Rosman 2013[24] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Talley 2011[25] | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
| Tsien 1997[26] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| van Pul 2015[27] | ✓ | |||||||||||||||
| Varpio 2012[28] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Mixed Adult and Pediatric Observational | ||||||||||||||||
| O'Carroll 1986[29] | ✓ | |||||||||||||||
| Wiklund 1994[30] | ✓ | ✓ | ✓ | ✓ | ||||||||||||
| Adult Intervention | ||||||||||||||||
| Albert 2015[32] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||
| Cvach 2013[33] | ✓ | ✓ | ||||||||||||||
| Cvach 2014[34] | ✓ | ✓ | ||||||||||||||
| Graham 2010[35] | ✓ | |||||||||||||||
| Rheineck‐Leyssius 1997[36] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Taenzer 2010[31] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
| Whalen 2014[37] | ✓ | ✓ | ✓ | |||||||||||||
| Pediatric Intervention | ||||||||||||||||
| Dandoy 2014[38] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||
For the purposes of this review, we defined nonactionable alarms as including both invalid (false) alarms that do not that accurately represent the physiologic status of the patient and alarms that are valid but do not warrant special attention or clinical intervention (nuisance alarms). We did not separate out invalid alarms due to the tremendous variation between studies in how validity was measured.
RESULTS
Study Selection
Search results produced 4629 articles (see the flow diagram in the Supporting Information in the online version of this article), of which 32 articles were eligible: 24 observational studies describing alarm characteristics and 8 studies describing interventions to reduce alarm frequency.
Observational Study Characteristics
Characteristics of included studies are shown in Table 1. Of the 24 observational studies,[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] 15 included adult patients,[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] 7 included pediatric patients,[22, 23, 24, 25, 26, 27, 28] and 2 included both adult and pediatric patients.[29, 30] All were single‐hospital studies, except for 1 study by Chambrin and colleagues[10] that included 5 sites. The number of patient‐hours examined in each study ranged from 60 to 113,880.[7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30] Hospital settings included ICUs (n = 16),[9, 10, 11, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 29] general wards (n = 5),[12, 15, 20, 22, 28] EDs (n = 2),[7, 21] postanesthesia care unit (PACU) (n = 1),[30] and cardiac care unit (CCU) (n = 1).[8] Studies varied in the type of physiologic signals recorded and data collection methods, ranging from direct observation by a nurse who was simultaneously caring for patients[29] to video recording with expert review.[14, 19, 22] Four observational studies met the criteria for lower risk of bias.[11, 14, 15, 22]
Intervention Study Characteristics
Of the 8 intervention studies, 7 included adult patients,[31, 32, 33, 34, 35, 36, 37] and 1 included pediatric patients.[38] All were single‐hospital studies; 6 were quasi‐experimental[31, 33, 34, 35, 37, 38] and 2 were experimental.[32, 36] Settings included progressive care units (n = 3),[33, 34, 35] CCUs (n = 3),[32, 33, 37] wards (n = 2),[31, 38] PACU (n = 1),[36] and a step‐down unit (n = 1).[32] All except 1 study[32] used the monitoring system to record alarm data. Several studies evaluated multicomponent interventions that included combinations of the following: widening alarm parameters,[31, 35, 36, 37, 38] instituting alarm delays,[31, 34, 36, 38] reconfiguring alarm acuity,[35, 37] use of secondary notifications,[34] daily change of electrocardiographic electrodes or use of disposable electrocardiographic wires,[32, 33, 38] universal monitoring in high‐risk populations,[31] and timely discontinuation of monitoring in low‐risk populations.[38] Four intervention studies met our prespecified lower risk of bias criteria.[31, 32, 36, 38]
Proportion of Alarms Considered Actionable
Results of the observational studies are provided in Table 2. The proportion of alarms that were actionable was <1% to 26% in adult ICU settings,[9, 10, 11, 13, 14, 16, 17, 19] 20% to 36% in adult ward settings,[12, 15, 20] 17% in a mixed adult and pediatric PACU setting,[30] 3% to 13% in pediatric ICU settings,[22, 23, 24, 25, 26] and 1% in a pediatric ward setting.[22]
| Signals Included | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| First Author and Publication Year | Setting | Monitored Patient‐Hours | SpO2 | ECG Arrhythmia | ECG Parametersa | Blood Pressure | Total Alarms | Actionable Alarms | Alarm Response | Lower Risk of Bias |
| ||||||||||
| Adult | ||||||||||
| Atzema 2006[7] | ED | 371 | ✓ | 1,762 | 0.20% | |||||
| Billinghurst 2003[8] | CCU | 420 | ✓ | 751 | Not reported; 17% were valid | Nurses with higher acuity patients and smaller % of valid alarms had slower response rates | ||||
| Biot 2000[9] | ICU | 250 | ✓ | ✓ | ✓ | ✓ | 3,665 | 3% | ||
| Chambrin 1999[10] | ICU | 1,971 | ✓ | ✓ | ✓ | ✓ | 3,188 | 26% | ||
| Drew 2014[11] | ICU | 48,173 | ✓ | ✓ | ✓ | ✓ | 2,558,760 | 0.3% of 3,861 VT alarms | ✓ | |
| Gazarian 2014[12] | Ward | 54 nurse‐hours | ✓ | ✓ | ✓ | 205 | 22% | Response to 47% of alarms | ||
| Grges 2009[13] | ICU | 200 | ✓ | ✓ | ✓ | ✓ | 1,214 | 5% | ||
| Gross 2011[15] | Ward | 530 | ✓ | ✓ | ✓ | ✓ | 4,393 | 20% | ✓ | |
| Inokuchi 2013[14] | ICU | 2,697 | ✓ | ✓ | ✓ | ✓ | 11,591 | 6% | ✓ | |
| Koski 1990[16] | ICU | 400 | ✓ | ✓ | 2,322 | 12% | ||||
| Morales Snchez 2014[17] | ICU | 434 sessions | ✓ | ✓ | ✓ | 215 | 25% | Response to 93% of alarms, of which 50% were within 10 seconds | ||
| Pergher 2014[18] | ICU | 60 | ✓ | 76 | Not reported | 72% of alarms stopped before nurse response or had >10 minutes response time | ||||
| Siebig 2010[19] | ICU | 982 | ✓ | ✓ | ✓ | ✓ | 5,934 | 15% | ||
| Voepel‐Lewis 2013[20] | Ward | 1,616 | ✓ | 710 | 36% | Response time was longer for patients in highest quartile of total alarms | ||||
| Way 2014[21] | ED | 93 | ✓ | ✓ | ✓ | ✓ | 572 | Not reported; 75% were valid | Nurses responded to more alarms in resuscitation room vs acute care area, but response time was longer | |
| Pediatric | ||||||||||
| Bonafide 2015[22] | Ward + ICU | 210 | ✓ | ✓ | ✓ | ✓ | 5,070 | 13% PICU, 1% ward | Incremental increases in response time as number of nonactionable alarms in preceding 120 minutes increased | ✓ |
| Lawless 1994[23] | ICU | 928 | ✓ | ✓ | ✓ | 2,176 | 6% | |||
| Rosman 2013[24] | ICU | 8,232 | ✓ | ✓ | ✓ | ✓ | 54,656 | 4% of rhythm alarms true critical" | ||
| Talley 2011[25] | ICU | 1,470∥ | ✓ | ✓ | ✓ | ✓ | 2,245 | 3% | ||
| Tsien 1997[26] | ICU | 298 | ✓ | ✓ | ✓ | 2,942 | 8% | |||
| van Pul 2015[27] | ICU | 113,880∥ | ✓ | ✓ | ✓ | ✓ | 222,751 | Not reported | Assigned nurse did not respond to 6% of alarms within 45 seconds | |
| Varpio 2012[28] | Ward | 49 unit‐hours | ✓ | ✓ | ✓ | ✓ | 446 | Not reported | 70% of all alarms and 41% of crisis alarms were not responded to within 1 minute | |
| Both | ||||||||||
| O'Carroll 1986[29] | ICU | 2,258∥ | ✓ | 284 | 2% | |||||
| Wiklund 1994[30] | PACU | 207 | ✓ | ✓ | ✓ | 1,891 | 17% | |||
Relationship Between Alarm Exposure and Response Time
Whereas 9 studies addressed response time,[8, 12, 17, 18, 20, 21, 22, 27, 28] only 2 evaluated the relationship between alarm burden and nurse response time.[20, 22] Voepel‐Lewis and colleagues found that nurse responses were slower to patients with the highest quartile of alarms (57.6 seconds) compared to those with the lowest (45.4 seconds) or medium (42.3 seconds) quartiles of alarms on an adult ward (P = 0.046). They did not find an association between false alarm exposure and response time.[20] Bonafide and colleagues found incremental increases in response time as the number of nonactionable alarms in the preceding 120 minutes increased (P < 0.001 in the pediatric ICU, P = 0.009 on the pediatric ward).[22]
Interventions Effective in Reducing Alarms
Results of the 8 intervention studies are provided in Table 3. Three studies evaluated single interventions;[32, 33, 36] the remainder of the studies tested interventions with multiple components such that it was impossible to separate the effect of each component. Below, we have summarized study results, arranged by component. Because only 1 study focused on pediatric patients,[38] results from pediatric and adult settings are combined.
| First Author and Publication Year | Design | Setting | Main Intervention Components | Other/ Comments | Key Results | Results Statistically Significant? | Lower Risk of Bias | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Widen Default Settings | Alarm Delays | Reconfigure Alarm Acuity | Secondary Notification | ECG Changes | |||||||
| |||||||||||
| Adult | |||||||||||
| Albert 2015[32] | Experimental (cluster‐randomized) | CCU | ✓ | Disposable vs reusable wires | Disposable leads had 29% fewer no‐telemetry, leads‐fail, and leads‐off alarms and similar artifact alarms | ✓ | ✓ | ||||
| Cvach 2013[33] | Quasi‐experimental (before and after) | CCU and PCU | ✓ | Daily change of electrodes | 46% fewer alarms/bed/day | ||||||
| Cvach 2014[34] | Quasi‐experimental (ITS) | PCU | ✓* | ✓ | Slope of regression line suggests decrease of 0.75 alarms/bed/day | ||||||
| Graham 2010[35] | Quasi‐experimental (before and after) | PCU | ✓ | ✓ | 43% fewer crisis, warning, and system warning alarms on unit | ||||||
| Rheineck‐Leyssius 1997[36] | Experimental (RCT) | PACU | ✓ | ✓ | Alarm limit of 85% had fewer alarms/patient but higher incidence of true hypoxemia for >1 minute (6% vs 2%) | ✓ | ✓ | ||||
| Taenzer 2010[31] | Quasi‐experimental (before and after with concurrent controls) | Ward | ✓ | ✓ | Universal SpO2 monitoring | Rescue events decreased from 3.4 to 1.2 per 1,000 discharges; transfers to ICU decreased from 5.6 to 2.9 per 1,000 patient‐days, only 4 alarms/patient‐day | ✓ | ✓ | |||
| Whalen 2014[37] | Quasi‐experimental (before and after) | CCU | ✓ | ✓ | 89% fewer audible alarms on unit | ✓ | |||||
| Pediatric | |||||||||||
| Dandoy 2014[38] | Quasi‐experimental (ITS) | Ward | ✓ | ✓ | ✓ | Timely monitor discontinuation; daily change of ECG electrodes | Decrease in alarms/patient‐days from 180 to 40 | ✓ | |||
Widening alarm parameter default settings was evaluated in 5 studies:[31, 35, 36, 37, 38] 1 single intervention randomized controlled trial (RCT),[36] and 4 multiple‐intervention, quasi‐experimental studies.[31, 35, 37, 38] In the RCT, using a lower SpO2 limit of 85% instead of the standard 90% resulted in 61% fewer alarms. In the 4 multiple intervention studies, 1 study reported significant reductions in alarm rates (P < 0.001),[37] 1 study did not report preintervention alarm rates but reported a postintervention alarm rate of 4 alarms per patient‐day,[31] and 2 studies reported reductions in alarm rates but did not report any statistical testing.[35, 38] Of the 3 studies examining patient safety, 1 study with universal monitoring reported fewer rescue events and transfers to the ICU postimplementation,[31] 1 study reported no missed acute decompensations,[38] and 1 study (the RCT) reported significantly more true hypoxemia events (P = 0.001).[36]
Alarm delays were evaluated in 4 studies:[31, 34, 36, 38] 3 multiple‐intervention, quasi‐experimental studies[31, 34, 38] and 1 retrospective analysis of data from an RCT.[36] One study combined alarm delays with widening defaults in a universal monitoring strategy and reported a postintervention alarm rate of 4 alarms per patient.[31] Another study evaluated delays as part of a secondary notification pager system and found a negatively sloping regression line that suggested a decreasing alarm rate, but did not report statistical testing.[34] The third study reported a reduction in alarm rates but did not report statistical testing.[38] The RCT compared the impact of a hypothetical 15‐second alarm delay to that of a lower SpO2 limit reduction and reported a similar reduction in alarms.[36] Of the 4 studies examining patient safety, 1 study with universal monitoring reported improvements,[31] 2 studies reported no adverse outcomes,[35, 38] and the retrospective analysis of data from the RCT reported the theoretical adverse outcome of delayed detection of sudden, severe desaturations.[36]
Reconfiguring alarm acuity was evaluated in 2 studies, both of which were multiple‐intervention quasi‐experimental studies.[35, 37] Both showed reductions in alarm rates: 1 was significant without increasing adverse events (P < 0.001),[37] and the other did not report statistical testing or safety outcomes.[35]
Secondary notification of nurses using pagers was the main intervention component of 1 study incorporating delays between the alarms and the alarm pages.[34] As mentioned above, a negatively sloping regression line was displayed, but no statistical testing or safety outcomes were reported.
Disposable electrocardiographic lead wires or daily electrode changes were evaluated in 3 studies:[32, 33, 38] 1 single intervention cluster‐randomized trial[32] and 2 quasi‐experimental studies.[33, 38] In the cluster‐randomized trial, disposable lead wires were compared to reusable lead wires, with disposable lead wires having significantly fewer technical alarms for lead signal failures (P = 0.03) but a similar number of monitoring artifact alarms (P = 0.44).[32] In a single‐intervention, quasi‐experimental study, daily electrode change showed a reduction in alarms, but no statistical testing was reported.[33] One multiple‐intervention, quasi‐experimental study incorporating daily electrode change showed fewer alarms without statistical testing.[38] Of the 2 studies examining patient safety, both reported no adverse outcomes.[32, 38]
DISCUSSION
This systematic review of physiologic monitor alarms in the hospital yielded the following main findings: (1) between 74% and 99% of physiologic monitor alarms were not actionable, (2) a significant relationship between alarm exposure and nurse response time was demonstrated in 2 small observational studies, and (3) although interventions were most often studied in combination, results from the studies with lower risk of bias suggest that widening alarm parameters, implementing alarm delays, and using disposable electrocardiographic lead wires and/or changing electrodes daily are the most promising interventions for reducing alarms. Only 5 of 8 intervention studies measured intervention safety and found that widening alarm parameters and implementing alarm delays had mixed safety outcomes, whereas disposable electrocardiographic lead wires and daily electrode changes had no adverse safety outcomes.[29, 30, 34, 35, 36] Safety measures are crucial to ensuring the highest level of patient safety is met; interventions are rendered useless without ensuring actionable alarms are not disabled. The variation in results across studies likely reflects the wide range of care settings as well as differences in design and quality.
This field is still in its infancy, with 18 of the 32 articles published in the past 5 years. We anticipate improvements in quality and rigor as the field matures, as well as clinically tested interventions that incorporate smart alarms. Smart alarms integrate data from multiple physiologic signals and the patient's history to better detect physiologic changes in the patient and improve the positive predictive value of alarms. Academicindustry partnerships will be required to implement and rigorously test smart alarms and other emerging technologies in the hospital.
To our knowledge, this is the first systematic review focused on monitor alarms with specific review questions relevant to alarm fatigue. Cvach recently published an integrative review of alarm fatigue using research published through 2011.[39] Our review builds upon her work by contributing a more extensive and systematic search strategy with databases spanning nursing, medicine, and engineering, including additional languages, and including newer studies published through April 2015. In addition, we included multiple cross‐team checks in our eligibility review to ensure high sensitivity and specificity of the resulting set of studies.
Although we focused on interventions aiming to reduce alarms, there has also been important recent work focused on reducing telemetry utilization in adult hospital populations as well as work focused on reducing pulse oximetry utilization in children admitted with respiratory conditions. Dressler and colleagues reported an immediate and sustained reduction in telemetry utilization in hospitalized adults upon redesign of cardiac telemetry order sets to include the clinical indication, which defaulted to the American Heart Association guideline‐recommended telemetry duration.[40] Instructions for bedside nurses were also included in the order set to facilitate appropriate telemetry discontinuation. Schondelmeyer and colleagues reported reductions in continuous pulse oximetry utilization in hospitalized children with asthma and bronchiolitis upon introduction of a multifaceted quality improvement program that included provider education, a nurse handoff checklist, and discontinuation criteria incorporated into order sets.[41]
Limitations of This Review and the Underlying Body of Work
There are limitations to this systematic review and its underlying body of work. With respect to our approach to this systematic review, we focused only on monitor alarms. Numerous other medical devices generate alarms in the patient‐care environment that also can contribute to alarm fatigue and deserve equally rigorous evaluation. With respect to the underlying body of work, the quality of individual studies was generally low. For example, determinations of alarm actionability were often made by a single rater without evaluation of the reliability or validity of these determinations, and statistical testing was often missing. There were also limitations specific to intervention studies, including evaluation of nongeneralizable patient populations, failure to measure the fidelity of the interventions, inadequate measures of intervention safety, and failure to statistically evaluate alarm reductions. Finally, though not necessarily a limitation, several studies were conducted by authors involved in or funded by the medical device industry.[11, 15, 19, 31, 32] This has the potential to introduce bias, although we have no indication that the quality of the science was adversely impacted.
Moving forward, the research agenda for physiologic monitor alarms should include the following: (1) more intensive focus on evaluating the relationship between alarm exposure and response time with analysis of important mediating factors that may promote or prevent alarm fatigue, (2) emphasis on studying interventions aimed at improving alarm management using rigorous designs such as cluster‐randomized trials and trials randomized by individual participant, (3) monitoring and reporting clinically meaningful balancing measures that represent unintended consequences of disabling or delaying potentially important alarms and possibly reducing the clinicians' ability to detect true patient deterioration and intervene in a timely manner, and (4) support for transparent academicindustry partnerships to evaluate new alarm technology in real‐world settings. As evidence‐based interventions emerge, there will be new opportunities to study different implementation strategies of these interventions to optimize effectiveness.
CONCLUSIONS
The body of literature relevant to physiologic monitor alarm characteristics and alarm fatigue is limited but growing rapidly. Although we know that most alarms are not actionable and that there appears to be a relationship between alarm exposure and response time that could be caused by alarm fatigue, we cannot yet say with certainty that we know which interventions are most effective in safely reducing unnecessary alarms. Interventions that appear most promising and should be prioritized for intensive evaluation include widening alarm parameters, implementing alarm delays, and using disposable electrocardiographic lead wires and changing electrodes daily. Careful evaluation of these interventions must include systematically examining adverse patient safety consequences.
Acknowledgements
The authors thank Amogh Karnik and Micheal Sellars for their technical assistance during the review and extraction process.
Disclosures: Ms. Zander is supported by the Society of Hospital Medicine Student Hospitalist Scholar Grant. Dr. Bonafide and Ms. Stemler are supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors report no conflicts of interest.
- National Patient Safety Goals Effective January 1, 2015. The Joint Commission Web site. http://www.jointcommission.org/assets/1/6/2015_NPSG_HAP.pdf. Accessed July 17, 2015.
- ECRI Institute. 2015 Top 10 Health Technology Hazards. Available at: https://www.ecri.org/Pages/2015‐Hazards.aspx. Accessed June 23, 2015.
- , . Alarm fatigue: a patient safety concern. AACN Adv Crit Care. 2013;24(4):378–386.
- , . Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199–1200.
- , , , et al. Meta‐analysis of observational studies in epidemiology: a proposal for reporting. Meta‐analysis Of Observational Studies in Epidemiology (MOOSE) Group. JAMA. 2000;283(15):2008–2012.
- , , , ; PRISMA Group. Preferred reporting items for systematic reviews and meta‐analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–269, W64.
- , , , , . ALARMED: adverse events in low‐risk patients with chest pain receiving continuous electrocardiographic monitoring in the emergency department. A pilot study. Am J Emerg Med. 2006;24:62–67.
- , , . Patient and nurse‐related implications of remote cardiac telemetry. Clin Nurs Res. 2003;12(4):356–370.
- , , , , . Clinical evaluation of alarm efficiency in intensive care [in French]. Ann Fr Anesth Reanim. 2000;19:459–466.
- , , , , , . Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25:1360–1366.
- , , , et al. Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PloS One. 2014;9(10):e110274.
- . Nurses' response to frequency and types of electrocardiography alarms in a non‐ critical care setting: a descriptive study. Int J Nurs Stud. 2014;51(2):190–197.
- , , . Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108:1546–1552.
- , , , et al. The proportion of clinically relevant alarms decreases as patient clinical severity decreases in intensive care units: a pilot study. BMJ Open. 2013;3(9):e003354–e003354.
- , , . Physiologic monitoring alarm load on medical/surgical floors of a community hospital. Biomed Instrum Technol. 2011;45:29–36.
- , , , . Frequency and reliability of alarms in the monitoring of cardiac postoperative patients. Int J Clin Monit Comput. 1990;7(2):129–133.
- , , , et al. Audit of the bedside monitor alarms in a critical care unit [in Spanish]. Enferm Intensiva. 2014;25(3):83–90.
- , . Stimulus‐response time to invasive blood pressure alarms: implications for the safety of critical‐care patients. Rev Gaúcha Enferm. 2014;35(2):135–141.
- , , , , , . Intensive care unit alarms— how many do we need? Crit Care Med. 2010;38:451–456.
- , , , et al. Pulse oximetry desaturation alarms on a general postoperative adult unit: a prospective observational study of nurse response time. Int J Nurs Stud. 2013;50(10):1351–1358.
- , , . Whats that noise? Bedside monitoring in the Emergency Department. Int Emerg Nurs. 2014;22(4):197–201.
- , , , et al. Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351.
- . Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985.
- , , , , . What are we missing? Arrhythmia detection in the pediatric intensive care unit. J Pediatr. 2013;163(2):511–514.
- , , , et al. Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;45(s1):38–45.
- , . Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25:614–619.
- , , , , . Safe patient monitoring is challenging but still feasible in a neonatal intensive care unit with single family rooms. Acta Paediatr Oslo Nor 1992. 2015;104(6):e247–e254.
- , , , . The helpful or hindering effects of in‐hospital patient monitor alarms on nurses: a qualitative analysis. CIN Comput Inform Nurs. 2012;30(4):210–217.
- . Survey of alarms in an intensive therapy unit. Anaesthesia. 1986;41(7):742–744.
- , , , . Postanesthesia monitoring revisited: frequency of true and false alarms from different monitoring devices. J Clin Anesth. 1994;6(3):182–188.
- , , , . Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before‐and‐after concurrence study. Anesthesiology. 2010;112(2):282–287.
- , , , et al. Differences in alarm events between disposable and reusable electrocardiography lead wires. Am J Crit Care. 2015;24(1):67–74.
- , , , . Daily electrode change and effect on cardiac monitor alarms: an evidence‐based practice approach. J Nurs Care Qual. 2013;28:265–271.
- , , , . Use of pagers with an alarm escalation system to reduce cardiac monitor alarm signals. J Nurs Care Qual. 2014;29(1):9–18.
- , . Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19:28–34.
- , . Influence of pulse oximeter lower alarm limit on the incidence of hypoxaemia in the recovery room. Br J Anaesth. 1997;79(4):460–464.
- , , , , , . Novel approach to cardiac alarm management on telemetry units. J Cardiovasc Nurs. 2014;29(5):E13–E22.
- , , , et al. A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694.
- . Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277.
- , , , , . Altering overuse of cardiac telemetry in non‐intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174(11):1852–1854.
- , , , et al. Using quality improvement to reduce continuous pulse oximetry use in children with wheezing. Pediatrics. 2015;135(4):e1044–e1051.
- , . The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non‐randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–384.
- National Patient Safety Goals Effective January 1, 2015. The Joint Commission Web site. http://www.jointcommission.org/assets/1/6/2015_NPSG_HAP.pdf. Accessed July 17, 2015.
- ECRI Institute. 2015 Top 10 Health Technology Hazards. Available at: https://www.ecri.org/Pages/2015‐Hazards.aspx. Accessed June 23, 2015.
- , . Alarm fatigue: a patient safety concern. AACN Adv Crit Care. 2013;24(4):378–386.
- , . Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199–1200.
- , , , et al. Meta‐analysis of observational studies in epidemiology: a proposal for reporting. Meta‐analysis Of Observational Studies in Epidemiology (MOOSE) Group. JAMA. 2000;283(15):2008–2012.
- , , , ; PRISMA Group. Preferred reporting items for systematic reviews and meta‐analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–269, W64.
- , , , , . ALARMED: adverse events in low‐risk patients with chest pain receiving continuous electrocardiographic monitoring in the emergency department. A pilot study. Am J Emerg Med. 2006;24:62–67.
- , , . Patient and nurse‐related implications of remote cardiac telemetry. Clin Nurs Res. 2003;12(4):356–370.
- , , , , . Clinical evaluation of alarm efficiency in intensive care [in French]. Ann Fr Anesth Reanim. 2000;19:459–466.
- , , , , , . Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25:1360–1366.
- , , , et al. Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PloS One. 2014;9(10):e110274.
- . Nurses' response to frequency and types of electrocardiography alarms in a non‐ critical care setting: a descriptive study. Int J Nurs Stud. 2014;51(2):190–197.
- , , . Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108:1546–1552.
- , , , et al. The proportion of clinically relevant alarms decreases as patient clinical severity decreases in intensive care units: a pilot study. BMJ Open. 2013;3(9):e003354–e003354.
- , , . Physiologic monitoring alarm load on medical/surgical floors of a community hospital. Biomed Instrum Technol. 2011;45:29–36.
- , , , . Frequency and reliability of alarms in the monitoring of cardiac postoperative patients. Int J Clin Monit Comput. 1990;7(2):129–133.
- , , , et al. Audit of the bedside monitor alarms in a critical care unit [in Spanish]. Enferm Intensiva. 2014;25(3):83–90.
- , . Stimulus‐response time to invasive blood pressure alarms: implications for the safety of critical‐care patients. Rev Gaúcha Enferm. 2014;35(2):135–141.
- , , , , , . Intensive care unit alarms— how many do we need? Crit Care Med. 2010;38:451–456.
- , , , et al. Pulse oximetry desaturation alarms on a general postoperative adult unit: a prospective observational study of nurse response time. Int J Nurs Stud. 2013;50(10):1351–1358.
- , , . Whats that noise? Bedside monitoring in the Emergency Department. Int Emerg Nurs. 2014;22(4):197–201.
- , , , et al. Association between exposure to nonactionable physiologic monitor alarms and response time in a children's hospital. J Hosp Med. 2015;10(6):345–351.
- . Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985.
- , , , , . What are we missing? Arrhythmia detection in the pediatric intensive care unit. J Pediatr. 2013;163(2):511–514.
- , , , et al. Cardiopulmonary monitors and clinically significant events in critically ill children. Biomed Instrum Technol. 2011;45(s1):38–45.
- , . Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25:614–619.
- , , , , . Safe patient monitoring is challenging but still feasible in a neonatal intensive care unit with single family rooms. Acta Paediatr Oslo Nor 1992. 2015;104(6):e247–e254.
- , , , . The helpful or hindering effects of in‐hospital patient monitor alarms on nurses: a qualitative analysis. CIN Comput Inform Nurs. 2012;30(4):210–217.
- . Survey of alarms in an intensive therapy unit. Anaesthesia. 1986;41(7):742–744.
- , , , . Postanesthesia monitoring revisited: frequency of true and false alarms from different monitoring devices. J Clin Anesth. 1994;6(3):182–188.
- , , , . Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before‐and‐after concurrence study. Anesthesiology. 2010;112(2):282–287.
- , , , et al. Differences in alarm events between disposable and reusable electrocardiography lead wires. Am J Crit Care. 2015;24(1):67–74.
- , , , . Daily electrode change and effect on cardiac monitor alarms: an evidence‐based practice approach. J Nurs Care Qual. 2013;28:265–271.
- , , , . Use of pagers with an alarm escalation system to reduce cardiac monitor alarm signals. J Nurs Care Qual. 2014;29(1):9–18.
- , . Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19:28–34.
- , . Influence of pulse oximeter lower alarm limit on the incidence of hypoxaemia in the recovery room. Br J Anaesth. 1997;79(4):460–464.
- , , , , , . Novel approach to cardiac alarm management on telemetry units. J Cardiovasc Nurs. 2014;29(5):E13–E22.
- , , , et al. A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694.
- . Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277.
- , , , , . Altering overuse of cardiac telemetry in non‐intensive care unit settings by hardwiring the use of American Heart Association guidelines. JAMA Intern Med. 2014;174(11):1852–1854.
- , , , et al. Using quality improvement to reduce continuous pulse oximetry use in children with wheezing. Pediatrics. 2015;135(4):e1044–e1051.
- , . The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non‐randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–384.