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Physiologic Monitor Alarm Rates at 5 Children’s Hospitals
Alarm fatigue is a patient safety hazard in hospitals1 that occurs when exposure to high rates of alarms leads clinicians to ignore or delay their responses to the alarms.2,3 To date, most studies of physiologic monitor alarms in hospitalized children have used data from single institutions and often only a few units within each institution.4 These limited studies have found that alarms in pediatric units are rarely actionable.2 They have also shown that physiologic monitor alarms occur frequently in children’s hospitals and that alarm rates can vary widely within a single institution,5 but the extent of variation between children’s hospitals is unknown. In this study, we aimed to describe and compare physiologic monitor alarm characteristics and the proportion of patients monitored in the inpatient units of 5 children’s hospitals.
METHODS
We performed a cross-sectional study using a point-prevalence design of physiologic monitor alarms and monitoring during a 24-hour period at 5 large, freestanding tertiary-care children’s hospitals. At the time of the study, each hospital had an alarm management committee in place and was working to address alarm fatigue. Each hospital’s institutional review board reviewed and approved the study.
We collected 24 consecutive hours of data from the inpatient units of each hospital between March 24, 2015, and May 1, 2015. Each hospital selected the data collection date within that window based on the availability of staff to perform data collection.6 We excluded emergency departments, procedural areas, and inpatient psychiatry and rehabilitation units. By using existing central alarm-collection software that interfaced with bedside physiologic monitors, we collected data on audible alarms generated for apnea, arrhythmia, low and high oxygen saturation, heart rate, respiratory rate, blood pressure, and exhaled carbon dioxide. Bedside alarm systems and alarm collection software differed between centers; therefore, alarm types that were not consistently collected at every institution (eg, alarms for electrode and device malfunction, ventilators, intracranial and central venous pressure monitors, and temperatures probes) were excluded. To estimate alarm rates and to account for fluctuations in hospital census throughout the day,7 we collected census (to calculate the number of alarms per patient day) and the number of monitored patients (to calculate the number of alarms per monitored-patient day, including only monitored patients in the denominator) on each unit at 3 time points, 8 hours apart. Patients were considered continuously monitored if they had presence of a waveform and data for pulse oximetry, respiratory rate, and/or heart rate at the time of data collection. We then determined the rate of alarms by unit type—medical-surgical unit (MSU), neonatal intensive care unit (NICU), or pediatric intensive care unit (PICU)—and the alarm types. Based on prior literature demonstrating up to 95% of alarms contributed by a minority of patients on a single unit,8 we also calculated the percentage of alarms contributed by beds in the highest quartile of alarms. We also assessed the percentage of patients monitored by unit type. The Supplementary Appendix shows the alarm parameter thresholds in use at the time of the study.
RESULTS
Averaged across study hospitals, one-quarter of the monitored beds were responsible for 71% of alarms in MSUs, 61% of alarms in NICUs, and 63% of alarms in PICUs.
DISCUSSION
Physiologic monitor alarm rates and the proportion of patients monitored varied widely between unit types and among the tertiary-care children’s hospitals in our study. We found that among MSUs, the hospital with the lowest proportion of beds monitored had the highest alarm rate, with over triple the rate seen at the hospital with the lowest alarm rate. Regardless of unit type, a small subgroup of patients at each hospital contributed a disproportionate share of alarms. These findings are concerning because of the patient morbidity and mortality associated with alarm fatigue1 and the studies suggesting that higher alarm rates may lead to delays in response to potentially critical alarms.2
We previously described alarm rates at a single children’s hospital and found that alarm rates were high both in and outside of the ICU areas.5 This study supports those findings and goes further to show that alarm rates on some MSUs approached rates seen in the ICU areas at other centers.4 However, our results should be considered in the context of several limitations. First, the 5 study hospitals utilized different bedside monitors, equipment, and software to collect alarm data. It is possible that this impacted how alarms were counted, though there were no technical specifications to suggest that results should have been biased in a specific way. Second, our data did not reflect alarm validity (ie, whether an alarm accurately reflected the physiologic state of the patient) or factors outside of the number of patients monitored—such as practices around ICU admission and transfer as well as monitor practices such as lead changes, the type of leads employed, and the degree to which alarm parameter thresholds could be customized, which may have also affected alarm rates. Finally, we excluded alarm types that were not consistently collected at all hospitals. We were also unable to capture alarms from other alarm-generating devices, including ventilators and infusion pumps, which have also been identified as sources of alarm-related safety issues in hospitals.9-11 This suggests that the alarm rates reported here underestimate the total number of audible alarms experienced by staff and by hospitalized patients and families.
While our data collection was limited in scope, the striking differences in alarm rates between hospitals and between similar units in the same hospitals suggest that unit- and hospital-level factors—including default alarm parameter threshold settings, types of monitors used, and monitoring practices such as the degree to which alarm parameters are customized to the patient’s physiologic state—likely contribute to the variability. It is also important to note that while there were clear outlier hospitals, no single hospital had the lowest alarm rate across all unit types. And while we found that a small number of patients contributed disproportionately to alarms, monitoring fewer patients overall was not consistently associated with lower alarm rates. While it is difficult to draw conclusions based on a limited study, these findings suggest that solutions to meaningfully lower alarm rates may be multifaceted. Standardization of care in multiple areas of medicine has shown the potential to decrease unnecessary utilization of testing and therapies while maintaining good patient outcomes.12-15 Our findings suggest that the concept of positive deviance,16 by which some organizations produce better outcomes than others despite similar limitations, may help identify successful alarm reduction strategies for further testing. Larger quantitative studies of alarm rates and ethnographic or qualitative studies of monitoring practices may reveal practices and policies that are associated with lower alarm rates with similar or improved monitoring outcomes.
CONCLUSION
We found wide variability in physiologic monitor alarm rates and the proportion of patients monitored across 5 children’s hospitals. Because alarm fatigue remains a pressing patient safety concern, further study of the features of high-performing (low-alarm) hospital systems may help identify barriers and facilitators of safe, effective monitoring and develop targeted interventions to reduce alarms.
ACKNOWLEDGEMENTS
The authors thank Melinda Egan, Matt MacMurchy, and Shannon Stemler for their assistance with data collection.
Disclosure
Dr. Bonafide is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K23HL116427. Dr. Brady is supported by the Agency for Healthcare Research and Quality under Award Number K08HS23827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Agency for Healthcare Research and Quality. There was no external funding obtained for this study. The authors have no conflicts of interest to disclose.
1. Sentinel Event Alert Issue 50: Medical device alarm safety in hospitals. The Joint Commission. April 8, 2013. www.jointcommission.org/sea_issue_50. Accessed December 16, 2017.
2. 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
3. Voepel-Lewis T, Parker ML, Burke CN, 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. PubMed
4. Paine CW, Goel VV, Ely E, et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136-144. PubMed
5. Schondelmeyer AC, Bonafide CP, Goel VV, et al. The frequency of physiologic monitor alarms in a children’s hospital. J Hosp Med. 2016;11(11):796-798. PubMed
6. Zingg W, Hopkins S, Gayet-Ageron A, et al. Health-care-associated infections in neonates, children, and adolescents: An analysis of paediatric data from the European Centre for Disease Prevention and Control point-prevalence survey. Lancet Infect Dis. 2017;17(4):381-389. PubMed
7. Fieldston E, Ragavan M, Jayaraman B, Metlay J, Pati S. Traditional measures of hospital utilization may not accurately reflect dynamic patient demand: Findings from a children’s hospital. Hosp Pediatr. 2012;2(1):10-18. PubMed
8. 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
9. Pham JC, Williams TL, Sparnon EM, Cillie TK, Scharen HF, Marella WM. Ventilator-related adverse events: A taxonomy and findings from 3 incident reporting systems. Respir Care. 2016;61(5):621-631. PubMed
10. Cho OM, Kim H, Lee YW, Cho I. Clinical alarms in intensive care units: Perceived obstacles of alarm management and alarm fatigue in nurses. Healthc Inform Res. 2016;22(1):46-53. PubMed
11. Edworthy J, Hellier E. Alarms and human behaviour: Implications for medical alarms. Br J Anaesth. 2006;97(1):12-17. PubMed
12. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 1: The content, quality, and accessibility of care. Ann Intern Med. 2003;138(4):273-287. PubMed
13. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 2: Health outcomes and satisfaction with care. Ann Intern Med. 2003;138(4):288-298. PubMed
14. Lion KC, Wright DR, Spencer S, Zhou C, Del Beccaro M, Mangione-Smith R. Standardized clinical pathways for hospitalized children and outcomes. Pediatrics. 2016;137(4) e20151202. PubMed
15. Goodman DC. Unwarranted variation in pediatric medical care. Pediatr Clin North Am. 2009;56(4):745-755. PubMed
16. Baxter R, Taylor N, Kellar I, Lawton R. What methods are used to apply positive deviance within healthcare organisations? A systematic review. BMJ Qual Saf. 2016;25(3):190-201. PubMed
Alarm fatigue is a patient safety hazard in hospitals1 that occurs when exposure to high rates of alarms leads clinicians to ignore or delay their responses to the alarms.2,3 To date, most studies of physiologic monitor alarms in hospitalized children have used data from single institutions and often only a few units within each institution.4 These limited studies have found that alarms in pediatric units are rarely actionable.2 They have also shown that physiologic monitor alarms occur frequently in children’s hospitals and that alarm rates can vary widely within a single institution,5 but the extent of variation between children’s hospitals is unknown. In this study, we aimed to describe and compare physiologic monitor alarm characteristics and the proportion of patients monitored in the inpatient units of 5 children’s hospitals.
METHODS
We performed a cross-sectional study using a point-prevalence design of physiologic monitor alarms and monitoring during a 24-hour period at 5 large, freestanding tertiary-care children’s hospitals. At the time of the study, each hospital had an alarm management committee in place and was working to address alarm fatigue. Each hospital’s institutional review board reviewed and approved the study.
We collected 24 consecutive hours of data from the inpatient units of each hospital between March 24, 2015, and May 1, 2015. Each hospital selected the data collection date within that window based on the availability of staff to perform data collection.6 We excluded emergency departments, procedural areas, and inpatient psychiatry and rehabilitation units. By using existing central alarm-collection software that interfaced with bedside physiologic monitors, we collected data on audible alarms generated for apnea, arrhythmia, low and high oxygen saturation, heart rate, respiratory rate, blood pressure, and exhaled carbon dioxide. Bedside alarm systems and alarm collection software differed between centers; therefore, alarm types that were not consistently collected at every institution (eg, alarms for electrode and device malfunction, ventilators, intracranial and central venous pressure monitors, and temperatures probes) were excluded. To estimate alarm rates and to account for fluctuations in hospital census throughout the day,7 we collected census (to calculate the number of alarms per patient day) and the number of monitored patients (to calculate the number of alarms per monitored-patient day, including only monitored patients in the denominator) on each unit at 3 time points, 8 hours apart. Patients were considered continuously monitored if they had presence of a waveform and data for pulse oximetry, respiratory rate, and/or heart rate at the time of data collection. We then determined the rate of alarms by unit type—medical-surgical unit (MSU), neonatal intensive care unit (NICU), or pediatric intensive care unit (PICU)—and the alarm types. Based on prior literature demonstrating up to 95% of alarms contributed by a minority of patients on a single unit,8 we also calculated the percentage of alarms contributed by beds in the highest quartile of alarms. We also assessed the percentage of patients monitored by unit type. The Supplementary Appendix shows the alarm parameter thresholds in use at the time of the study.
RESULTS
Averaged across study hospitals, one-quarter of the monitored beds were responsible for 71% of alarms in MSUs, 61% of alarms in NICUs, and 63% of alarms in PICUs.
DISCUSSION
Physiologic monitor alarm rates and the proportion of patients monitored varied widely between unit types and among the tertiary-care children’s hospitals in our study. We found that among MSUs, the hospital with the lowest proportion of beds monitored had the highest alarm rate, with over triple the rate seen at the hospital with the lowest alarm rate. Regardless of unit type, a small subgroup of patients at each hospital contributed a disproportionate share of alarms. These findings are concerning because of the patient morbidity and mortality associated with alarm fatigue1 and the studies suggesting that higher alarm rates may lead to delays in response to potentially critical alarms.2
We previously described alarm rates at a single children’s hospital and found that alarm rates were high both in and outside of the ICU areas.5 This study supports those findings and goes further to show that alarm rates on some MSUs approached rates seen in the ICU areas at other centers.4 However, our results should be considered in the context of several limitations. First, the 5 study hospitals utilized different bedside monitors, equipment, and software to collect alarm data. It is possible that this impacted how alarms were counted, though there were no technical specifications to suggest that results should have been biased in a specific way. Second, our data did not reflect alarm validity (ie, whether an alarm accurately reflected the physiologic state of the patient) or factors outside of the number of patients monitored—such as practices around ICU admission and transfer as well as monitor practices such as lead changes, the type of leads employed, and the degree to which alarm parameter thresholds could be customized, which may have also affected alarm rates. Finally, we excluded alarm types that were not consistently collected at all hospitals. We were also unable to capture alarms from other alarm-generating devices, including ventilators and infusion pumps, which have also been identified as sources of alarm-related safety issues in hospitals.9-11 This suggests that the alarm rates reported here underestimate the total number of audible alarms experienced by staff and by hospitalized patients and families.
While our data collection was limited in scope, the striking differences in alarm rates between hospitals and between similar units in the same hospitals suggest that unit- and hospital-level factors—including default alarm parameter threshold settings, types of monitors used, and monitoring practices such as the degree to which alarm parameters are customized to the patient’s physiologic state—likely contribute to the variability. It is also important to note that while there were clear outlier hospitals, no single hospital had the lowest alarm rate across all unit types. And while we found that a small number of patients contributed disproportionately to alarms, monitoring fewer patients overall was not consistently associated with lower alarm rates. While it is difficult to draw conclusions based on a limited study, these findings suggest that solutions to meaningfully lower alarm rates may be multifaceted. Standardization of care in multiple areas of medicine has shown the potential to decrease unnecessary utilization of testing and therapies while maintaining good patient outcomes.12-15 Our findings suggest that the concept of positive deviance,16 by which some organizations produce better outcomes than others despite similar limitations, may help identify successful alarm reduction strategies for further testing. Larger quantitative studies of alarm rates and ethnographic or qualitative studies of monitoring practices may reveal practices and policies that are associated with lower alarm rates with similar or improved monitoring outcomes.
CONCLUSION
We found wide variability in physiologic monitor alarm rates and the proportion of patients monitored across 5 children’s hospitals. Because alarm fatigue remains a pressing patient safety concern, further study of the features of high-performing (low-alarm) hospital systems may help identify barriers and facilitators of safe, effective monitoring and develop targeted interventions to reduce alarms.
ACKNOWLEDGEMENTS
The authors thank Melinda Egan, Matt MacMurchy, and Shannon Stemler for their assistance with data collection.
Disclosure
Dr. Bonafide is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K23HL116427. Dr. Brady is supported by the Agency for Healthcare Research and Quality under Award Number K08HS23827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Agency for Healthcare Research and Quality. There was no external funding obtained for this study. The authors have no conflicts of interest to disclose.
Alarm fatigue is a patient safety hazard in hospitals1 that occurs when exposure to high rates of alarms leads clinicians to ignore or delay their responses to the alarms.2,3 To date, most studies of physiologic monitor alarms in hospitalized children have used data from single institutions and often only a few units within each institution.4 These limited studies have found that alarms in pediatric units are rarely actionable.2 They have also shown that physiologic monitor alarms occur frequently in children’s hospitals and that alarm rates can vary widely within a single institution,5 but the extent of variation between children’s hospitals is unknown. In this study, we aimed to describe and compare physiologic monitor alarm characteristics and the proportion of patients monitored in the inpatient units of 5 children’s hospitals.
METHODS
We performed a cross-sectional study using a point-prevalence design of physiologic monitor alarms and monitoring during a 24-hour period at 5 large, freestanding tertiary-care children’s hospitals. At the time of the study, each hospital had an alarm management committee in place and was working to address alarm fatigue. Each hospital’s institutional review board reviewed and approved the study.
We collected 24 consecutive hours of data from the inpatient units of each hospital between March 24, 2015, and May 1, 2015. Each hospital selected the data collection date within that window based on the availability of staff to perform data collection.6 We excluded emergency departments, procedural areas, and inpatient psychiatry and rehabilitation units. By using existing central alarm-collection software that interfaced with bedside physiologic monitors, we collected data on audible alarms generated for apnea, arrhythmia, low and high oxygen saturation, heart rate, respiratory rate, blood pressure, and exhaled carbon dioxide. Bedside alarm systems and alarm collection software differed between centers; therefore, alarm types that were not consistently collected at every institution (eg, alarms for electrode and device malfunction, ventilators, intracranial and central venous pressure monitors, and temperatures probes) were excluded. To estimate alarm rates and to account for fluctuations in hospital census throughout the day,7 we collected census (to calculate the number of alarms per patient day) and the number of monitored patients (to calculate the number of alarms per monitored-patient day, including only monitored patients in the denominator) on each unit at 3 time points, 8 hours apart. Patients were considered continuously monitored if they had presence of a waveform and data for pulse oximetry, respiratory rate, and/or heart rate at the time of data collection. We then determined the rate of alarms by unit type—medical-surgical unit (MSU), neonatal intensive care unit (NICU), or pediatric intensive care unit (PICU)—and the alarm types. Based on prior literature demonstrating up to 95% of alarms contributed by a minority of patients on a single unit,8 we also calculated the percentage of alarms contributed by beds in the highest quartile of alarms. We also assessed the percentage of patients monitored by unit type. The Supplementary Appendix shows the alarm parameter thresholds in use at the time of the study.
RESULTS
Averaged across study hospitals, one-quarter of the monitored beds were responsible for 71% of alarms in MSUs, 61% of alarms in NICUs, and 63% of alarms in PICUs.
DISCUSSION
Physiologic monitor alarm rates and the proportion of patients monitored varied widely between unit types and among the tertiary-care children’s hospitals in our study. We found that among MSUs, the hospital with the lowest proportion of beds monitored had the highest alarm rate, with over triple the rate seen at the hospital with the lowest alarm rate. Regardless of unit type, a small subgroup of patients at each hospital contributed a disproportionate share of alarms. These findings are concerning because of the patient morbidity and mortality associated with alarm fatigue1 and the studies suggesting that higher alarm rates may lead to delays in response to potentially critical alarms.2
We previously described alarm rates at a single children’s hospital and found that alarm rates were high both in and outside of the ICU areas.5 This study supports those findings and goes further to show that alarm rates on some MSUs approached rates seen in the ICU areas at other centers.4 However, our results should be considered in the context of several limitations. First, the 5 study hospitals utilized different bedside monitors, equipment, and software to collect alarm data. It is possible that this impacted how alarms were counted, though there were no technical specifications to suggest that results should have been biased in a specific way. Second, our data did not reflect alarm validity (ie, whether an alarm accurately reflected the physiologic state of the patient) or factors outside of the number of patients monitored—such as practices around ICU admission and transfer as well as monitor practices such as lead changes, the type of leads employed, and the degree to which alarm parameter thresholds could be customized, which may have also affected alarm rates. Finally, we excluded alarm types that were not consistently collected at all hospitals. We were also unable to capture alarms from other alarm-generating devices, including ventilators and infusion pumps, which have also been identified as sources of alarm-related safety issues in hospitals.9-11 This suggests that the alarm rates reported here underestimate the total number of audible alarms experienced by staff and by hospitalized patients and families.
While our data collection was limited in scope, the striking differences in alarm rates between hospitals and between similar units in the same hospitals suggest that unit- and hospital-level factors—including default alarm parameter threshold settings, types of monitors used, and monitoring practices such as the degree to which alarm parameters are customized to the patient’s physiologic state—likely contribute to the variability. It is also important to note that while there were clear outlier hospitals, no single hospital had the lowest alarm rate across all unit types. And while we found that a small number of patients contributed disproportionately to alarms, monitoring fewer patients overall was not consistently associated with lower alarm rates. While it is difficult to draw conclusions based on a limited study, these findings suggest that solutions to meaningfully lower alarm rates may be multifaceted. Standardization of care in multiple areas of medicine has shown the potential to decrease unnecessary utilization of testing and therapies while maintaining good patient outcomes.12-15 Our findings suggest that the concept of positive deviance,16 by which some organizations produce better outcomes than others despite similar limitations, may help identify successful alarm reduction strategies for further testing. Larger quantitative studies of alarm rates and ethnographic or qualitative studies of monitoring practices may reveal practices and policies that are associated with lower alarm rates with similar or improved monitoring outcomes.
CONCLUSION
We found wide variability in physiologic monitor alarm rates and the proportion of patients monitored across 5 children’s hospitals. Because alarm fatigue remains a pressing patient safety concern, further study of the features of high-performing (low-alarm) hospital systems may help identify barriers and facilitators of safe, effective monitoring and develop targeted interventions to reduce alarms.
ACKNOWLEDGEMENTS
The authors thank Melinda Egan, Matt MacMurchy, and Shannon Stemler for their assistance with data collection.
Disclosure
Dr. Bonafide is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K23HL116427. Dr. Brady is supported by the Agency for Healthcare Research and Quality under Award Number K08HS23827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Agency for Healthcare Research and Quality. There was no external funding obtained for this study. The authors have no conflicts of interest to disclose.
1. Sentinel Event Alert Issue 50: Medical device alarm safety in hospitals. The Joint Commission. April 8, 2013. www.jointcommission.org/sea_issue_50. Accessed December 16, 2017.
2. 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
3. Voepel-Lewis T, Parker ML, Burke CN, 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. PubMed
4. Paine CW, Goel VV, Ely E, et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136-144. PubMed
5. Schondelmeyer AC, Bonafide CP, Goel VV, et al. The frequency of physiologic monitor alarms in a children’s hospital. J Hosp Med. 2016;11(11):796-798. PubMed
6. Zingg W, Hopkins S, Gayet-Ageron A, et al. Health-care-associated infections in neonates, children, and adolescents: An analysis of paediatric data from the European Centre for Disease Prevention and Control point-prevalence survey. Lancet Infect Dis. 2017;17(4):381-389. PubMed
7. Fieldston E, Ragavan M, Jayaraman B, Metlay J, Pati S. Traditional measures of hospital utilization may not accurately reflect dynamic patient demand: Findings from a children’s hospital. Hosp Pediatr. 2012;2(1):10-18. PubMed
8. 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
9. Pham JC, Williams TL, Sparnon EM, Cillie TK, Scharen HF, Marella WM. Ventilator-related adverse events: A taxonomy and findings from 3 incident reporting systems. Respir Care. 2016;61(5):621-631. PubMed
10. Cho OM, Kim H, Lee YW, Cho I. Clinical alarms in intensive care units: Perceived obstacles of alarm management and alarm fatigue in nurses. Healthc Inform Res. 2016;22(1):46-53. PubMed
11. Edworthy J, Hellier E. Alarms and human behaviour: Implications for medical alarms. Br J Anaesth. 2006;97(1):12-17. PubMed
12. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 1: The content, quality, and accessibility of care. Ann Intern Med. 2003;138(4):273-287. PubMed
13. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 2: Health outcomes and satisfaction with care. Ann Intern Med. 2003;138(4):288-298. PubMed
14. Lion KC, Wright DR, Spencer S, Zhou C, Del Beccaro M, Mangione-Smith R. Standardized clinical pathways for hospitalized children and outcomes. Pediatrics. 2016;137(4) e20151202. PubMed
15. Goodman DC. Unwarranted variation in pediatric medical care. Pediatr Clin North Am. 2009;56(4):745-755. PubMed
16. Baxter R, Taylor N, Kellar I, Lawton R. What methods are used to apply positive deviance within healthcare organisations? A systematic review. BMJ Qual Saf. 2016;25(3):190-201. PubMed
1. Sentinel Event Alert Issue 50: Medical device alarm safety in hospitals. The Joint Commission. April 8, 2013. www.jointcommission.org/sea_issue_50. Accessed December 16, 2017.
2. 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
3. Voepel-Lewis T, Parker ML, Burke CN, 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. PubMed
4. Paine CW, Goel VV, Ely E, et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136-144. PubMed
5. Schondelmeyer AC, Bonafide CP, Goel VV, et al. The frequency of physiologic monitor alarms in a children’s hospital. J Hosp Med. 2016;11(11):796-798. PubMed
6. Zingg W, Hopkins S, Gayet-Ageron A, et al. Health-care-associated infections in neonates, children, and adolescents: An analysis of paediatric data from the European Centre for Disease Prevention and Control point-prevalence survey. Lancet Infect Dis. 2017;17(4):381-389. PubMed
7. Fieldston E, Ragavan M, Jayaraman B, Metlay J, Pati S. Traditional measures of hospital utilization may not accurately reflect dynamic patient demand: Findings from a children’s hospital. Hosp Pediatr. 2012;2(1):10-18. PubMed
8. 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
9. Pham JC, Williams TL, Sparnon EM, Cillie TK, Scharen HF, Marella WM. Ventilator-related adverse events: A taxonomy and findings from 3 incident reporting systems. Respir Care. 2016;61(5):621-631. PubMed
10. Cho OM, Kim H, Lee YW, Cho I. Clinical alarms in intensive care units: Perceived obstacles of alarm management and alarm fatigue in nurses. Healthc Inform Res. 2016;22(1):46-53. PubMed
11. Edworthy J, Hellier E. Alarms and human behaviour: Implications for medical alarms. Br J Anaesth. 2006;97(1):12-17. PubMed
12. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 1: The content, quality, and accessibility of care. Ann Intern Med. 2003;138(4):273-287. PubMed
13. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 2: Health outcomes and satisfaction with care. Ann Intern Med. 2003;138(4):288-298. PubMed
14. Lion KC, Wright DR, Spencer S, Zhou C, Del Beccaro M, Mangione-Smith R. Standardized clinical pathways for hospitalized children and outcomes. Pediatrics. 2016;137(4) e20151202. PubMed
15. Goodman DC. Unwarranted variation in pediatric medical care. Pediatr Clin North Am. 2009;56(4):745-755. PubMed
16. Baxter R, Taylor N, Kellar I, Lawton R. What methods are used to apply positive deviance within healthcare organisations? A systematic review. BMJ Qual Saf. 2016;25(3):190-201. PubMed
© 2018 Society of Hospital Medicine
Monitor Alarms in a Children's Hospital
Physiologic monitor alarms are an inescapable part of the soundtrack for hospitals. Data from primarily adult hospitals have shown that alarms occur at high rates, and most alarms are not actionable.[1] Small studies have suggested that high alarm rates can lead to alarm fatigue.[2, 3] To prioritize alarm types to target in future intervention studies, in this study we aimed to investigate the alarm rates on all inpatient units and the most common causes of alarms at a children's hospital.
METHODS
This was a cross‐sectional study of audible physiologic monitor alarms at Cincinnati Children's Hospital Medical Center (CCHMC) over 7 consecutive days during August 2014. CCHMC is a 522‐bed free‐standing children's hospital. Inpatient beds are equipped with GE Healthcare (Little Chalfont, United Kingdom) bedside monitors (models Dash 3000, 4000, and 5000, and Solar 8000). Age‐specific vital sign parameters were employed for monitors on all units.
We obtained date, time, and type of alarm from bedside physiologic monitors using Connexall middleware (GlobeStar Systems, Toronto, Ontario, Canada).
We determined unit census using the electronic health records for the time period concurrent with the alarm data collection. Given previously described variation in hospital census over the day,[4] we used 4 daily census measurements (6:00 am, 12:00 pm, 6:00 pm, and 11:00 pm) rather than 1 single measurement to more accurately reflect the hospital census.
The CCHMC Institutional Review Board determined this work to be not human subjects research.
Statistical Analysis
For each unit and each census time interval, we generated a rate based on the number of occupied beds (alarms per patient‐day) resulting in a total of 28 rates (4 census measurement periods per/day 7 days) for each unit over the study period. We used descriptive statistics to summarize alarms per patient‐day by unit. Analysis of variance was used to compare alarm rates between units. For significant main effects, we used Tukey's multiple comparisons tests for all pairwise comparisons to control the type I experiment‐wise error rate. Alarms were then classified by alarm cause (eg, high heart rate). We summarized the cause for all alarms using counts and percentages.
RESULTS
There were a total of 220,813 audible alarms over 1 week. Median alarm rate per patient‐day by unit ranged from 30.4 to 228.5; the highest alarm rates occurred in the cardiac intensive care unit, with a median of 228.5 (interquartile range [IQR], 193275) followed by the pediatric intensive care unit (172.4; IQR, 141188) (Figure 1). The average alarm rate was significantly different among the units (P < 0.01).
Technical alarms (eg, alarms for artifact, lead failure), comprised 33% of the total number of alarms. The remaining 67% of alarms were for clinical conditions, the most common of which was low oxygen saturation (30% of clinical alarms) (Figure 2).
DISCUSSION
We described alarm rates and causes over multiple units at a large children's hospital. To our knowledge, this is the first description of alarm rates across multiple pediatric inpatient units. Alarm counts were high even for the general units, indicating that a nurse taking care of 4 monitored patients would need to process a physiologic monitor alarm every 4 minutes on average, in addition to other sources of alarms such as infusion pumps.
Alarm rates were highest in the intensive care unit areas, which may be attributable to both higher rates of monitoring and sicker patients. Importantly, however, alarms were quite high and variable on the acute care units. This suggests that factors other than patient acuity may have substantial influence on alarm rates.
Technical alarms, alarms that do not indicate a change in patient condition, accounted for the largest percentage of alarms during the study period. This is consistent with prior literature that has suggested that regular electrode replacement, which decreases technical alarms, can be effective in reducing alarm rates.[5, 6] The most common vital sign change to cause alarms was low oxygen saturation, followed by elevated heart rate and elevated respiratory rate. Whereas in most healthy patients, certain low oxygen levels would prompt initiation of supplemental oxygen, there are many conditions in which elevated heart rate and respiratory rate may not require titration of any particular therapy. These may be potential intervention targets for hospitals trying to improve alarm rates.
Limitations
There are several limitations to our study. First, our results are not necessarily generalizable to other types of hospitals or those utilizing monitors from other vendors. Second, we were unable to include other sources of alarms such as infusion pumps and ventilators. However, given the high alarm rates from physiologic monitors alone, these data add urgency to the need for further investigation in the pediatric setting.
CONCLUSION
Alarm rates at a single children's hospital varied depending on the unit. Strategies targeted at reducing technical alarms and reducing nonactionable clinical alarms for low oxygen saturation, high heart rate, and high respiratory rate may offer the greatest opportunity to reduce alarm rates.
Acknowledgements
The authors acknowledge Melinda Egan for her assistance in obtaining data for this study and Ting Sa for her assistance with data management.
Disclosures: Dr. Bonafide is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. Dr. Bonafide also holds a Young Investigator Award grant from the Academic Pediatric Association evaluating the impact of a data‐driven monitor alarm reduction strategy implemented in safety huddles. Dr. Brady is supported by the Agency for Healthcare Research and Quality under award number K08HS23827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Agency for Healthcare Research and Quality. This study was funded by the Arnold W. Strauss Fellow Grant, Cincinnati Children's Hospital Medical Center. The authors have no conflicts of interest to disclose.
- , , , et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144.
- , , , 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.
- , , , 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.
- , , , , . Traditional measures of hospital utilization may not accurately reflect dynamic patient demand: findings from a children's hospital. Hosp Pediatr. 2012;2(1):10–18.
- , , , et al. A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694.
- , , , . Daily electrode change and effect on cardiac monitor alarms: an evidence‐based practice approach. J Nurs Care Qual. 2013;28(3):265–271.
Physiologic monitor alarms are an inescapable part of the soundtrack for hospitals. Data from primarily adult hospitals have shown that alarms occur at high rates, and most alarms are not actionable.[1] Small studies have suggested that high alarm rates can lead to alarm fatigue.[2, 3] To prioritize alarm types to target in future intervention studies, in this study we aimed to investigate the alarm rates on all inpatient units and the most common causes of alarms at a children's hospital.
METHODS
This was a cross‐sectional study of audible physiologic monitor alarms at Cincinnati Children's Hospital Medical Center (CCHMC) over 7 consecutive days during August 2014. CCHMC is a 522‐bed free‐standing children's hospital. Inpatient beds are equipped with GE Healthcare (Little Chalfont, United Kingdom) bedside monitors (models Dash 3000, 4000, and 5000, and Solar 8000). Age‐specific vital sign parameters were employed for monitors on all units.
We obtained date, time, and type of alarm from bedside physiologic monitors using Connexall middleware (GlobeStar Systems, Toronto, Ontario, Canada).
We determined unit census using the electronic health records for the time period concurrent with the alarm data collection. Given previously described variation in hospital census over the day,[4] we used 4 daily census measurements (6:00 am, 12:00 pm, 6:00 pm, and 11:00 pm) rather than 1 single measurement to more accurately reflect the hospital census.
The CCHMC Institutional Review Board determined this work to be not human subjects research.
Statistical Analysis
For each unit and each census time interval, we generated a rate based on the number of occupied beds (alarms per patient‐day) resulting in a total of 28 rates (4 census measurement periods per/day 7 days) for each unit over the study period. We used descriptive statistics to summarize alarms per patient‐day by unit. Analysis of variance was used to compare alarm rates between units. For significant main effects, we used Tukey's multiple comparisons tests for all pairwise comparisons to control the type I experiment‐wise error rate. Alarms were then classified by alarm cause (eg, high heart rate). We summarized the cause for all alarms using counts and percentages.
RESULTS
There were a total of 220,813 audible alarms over 1 week. Median alarm rate per patient‐day by unit ranged from 30.4 to 228.5; the highest alarm rates occurred in the cardiac intensive care unit, with a median of 228.5 (interquartile range [IQR], 193275) followed by the pediatric intensive care unit (172.4; IQR, 141188) (Figure 1). The average alarm rate was significantly different among the units (P < 0.01).
Technical alarms (eg, alarms for artifact, lead failure), comprised 33% of the total number of alarms. The remaining 67% of alarms were for clinical conditions, the most common of which was low oxygen saturation (30% of clinical alarms) (Figure 2).
DISCUSSION
We described alarm rates and causes over multiple units at a large children's hospital. To our knowledge, this is the first description of alarm rates across multiple pediatric inpatient units. Alarm counts were high even for the general units, indicating that a nurse taking care of 4 monitored patients would need to process a physiologic monitor alarm every 4 minutes on average, in addition to other sources of alarms such as infusion pumps.
Alarm rates were highest in the intensive care unit areas, which may be attributable to both higher rates of monitoring and sicker patients. Importantly, however, alarms were quite high and variable on the acute care units. This suggests that factors other than patient acuity may have substantial influence on alarm rates.
Technical alarms, alarms that do not indicate a change in patient condition, accounted for the largest percentage of alarms during the study period. This is consistent with prior literature that has suggested that regular electrode replacement, which decreases technical alarms, can be effective in reducing alarm rates.[5, 6] The most common vital sign change to cause alarms was low oxygen saturation, followed by elevated heart rate and elevated respiratory rate. Whereas in most healthy patients, certain low oxygen levels would prompt initiation of supplemental oxygen, there are many conditions in which elevated heart rate and respiratory rate may not require titration of any particular therapy. These may be potential intervention targets for hospitals trying to improve alarm rates.
Limitations
There are several limitations to our study. First, our results are not necessarily generalizable to other types of hospitals or those utilizing monitors from other vendors. Second, we were unable to include other sources of alarms such as infusion pumps and ventilators. However, given the high alarm rates from physiologic monitors alone, these data add urgency to the need for further investigation in the pediatric setting.
CONCLUSION
Alarm rates at a single children's hospital varied depending on the unit. Strategies targeted at reducing technical alarms and reducing nonactionable clinical alarms for low oxygen saturation, high heart rate, and high respiratory rate may offer the greatest opportunity to reduce alarm rates.
Acknowledgements
The authors acknowledge Melinda Egan for her assistance in obtaining data for this study and Ting Sa for her assistance with data management.
Disclosures: Dr. Bonafide is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. Dr. Bonafide also holds a Young Investigator Award grant from the Academic Pediatric Association evaluating the impact of a data‐driven monitor alarm reduction strategy implemented in safety huddles. Dr. Brady is supported by the Agency for Healthcare Research and Quality under award number K08HS23827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Agency for Healthcare Research and Quality. This study was funded by the Arnold W. Strauss Fellow Grant, Cincinnati Children's Hospital Medical Center. The authors have no conflicts of interest to disclose.
Physiologic monitor alarms are an inescapable part of the soundtrack for hospitals. Data from primarily adult hospitals have shown that alarms occur at high rates, and most alarms are not actionable.[1] Small studies have suggested that high alarm rates can lead to alarm fatigue.[2, 3] To prioritize alarm types to target in future intervention studies, in this study we aimed to investigate the alarm rates on all inpatient units and the most common causes of alarms at a children's hospital.
METHODS
This was a cross‐sectional study of audible physiologic monitor alarms at Cincinnati Children's Hospital Medical Center (CCHMC) over 7 consecutive days during August 2014. CCHMC is a 522‐bed free‐standing children's hospital. Inpatient beds are equipped with GE Healthcare (Little Chalfont, United Kingdom) bedside monitors (models Dash 3000, 4000, and 5000, and Solar 8000). Age‐specific vital sign parameters were employed for monitors on all units.
We obtained date, time, and type of alarm from bedside physiologic monitors using Connexall middleware (GlobeStar Systems, Toronto, Ontario, Canada).
We determined unit census using the electronic health records for the time period concurrent with the alarm data collection. Given previously described variation in hospital census over the day,[4] we used 4 daily census measurements (6:00 am, 12:00 pm, 6:00 pm, and 11:00 pm) rather than 1 single measurement to more accurately reflect the hospital census.
The CCHMC Institutional Review Board determined this work to be not human subjects research.
Statistical Analysis
For each unit and each census time interval, we generated a rate based on the number of occupied beds (alarms per patient‐day) resulting in a total of 28 rates (4 census measurement periods per/day 7 days) for each unit over the study period. We used descriptive statistics to summarize alarms per patient‐day by unit. Analysis of variance was used to compare alarm rates between units. For significant main effects, we used Tukey's multiple comparisons tests for all pairwise comparisons to control the type I experiment‐wise error rate. Alarms were then classified by alarm cause (eg, high heart rate). We summarized the cause for all alarms using counts and percentages.
RESULTS
There were a total of 220,813 audible alarms over 1 week. Median alarm rate per patient‐day by unit ranged from 30.4 to 228.5; the highest alarm rates occurred in the cardiac intensive care unit, with a median of 228.5 (interquartile range [IQR], 193275) followed by the pediatric intensive care unit (172.4; IQR, 141188) (Figure 1). The average alarm rate was significantly different among the units (P < 0.01).
Technical alarms (eg, alarms for artifact, lead failure), comprised 33% of the total number of alarms. The remaining 67% of alarms were for clinical conditions, the most common of which was low oxygen saturation (30% of clinical alarms) (Figure 2).
DISCUSSION
We described alarm rates and causes over multiple units at a large children's hospital. To our knowledge, this is the first description of alarm rates across multiple pediatric inpatient units. Alarm counts were high even for the general units, indicating that a nurse taking care of 4 monitored patients would need to process a physiologic monitor alarm every 4 minutes on average, in addition to other sources of alarms such as infusion pumps.
Alarm rates were highest in the intensive care unit areas, which may be attributable to both higher rates of monitoring and sicker patients. Importantly, however, alarms were quite high and variable on the acute care units. This suggests that factors other than patient acuity may have substantial influence on alarm rates.
Technical alarms, alarms that do not indicate a change in patient condition, accounted for the largest percentage of alarms during the study period. This is consistent with prior literature that has suggested that regular electrode replacement, which decreases technical alarms, can be effective in reducing alarm rates.[5, 6] The most common vital sign change to cause alarms was low oxygen saturation, followed by elevated heart rate and elevated respiratory rate. Whereas in most healthy patients, certain low oxygen levels would prompt initiation of supplemental oxygen, there are many conditions in which elevated heart rate and respiratory rate may not require titration of any particular therapy. These may be potential intervention targets for hospitals trying to improve alarm rates.
Limitations
There are several limitations to our study. First, our results are not necessarily generalizable to other types of hospitals or those utilizing monitors from other vendors. Second, we were unable to include other sources of alarms such as infusion pumps and ventilators. However, given the high alarm rates from physiologic monitors alone, these data add urgency to the need for further investigation in the pediatric setting.
CONCLUSION
Alarm rates at a single children's hospital varied depending on the unit. Strategies targeted at reducing technical alarms and reducing nonactionable clinical alarms for low oxygen saturation, high heart rate, and high respiratory rate may offer the greatest opportunity to reduce alarm rates.
Acknowledgements
The authors acknowledge Melinda Egan for her assistance in obtaining data for this study and Ting Sa for her assistance with data management.
Disclosures: Dr. Bonafide is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. Dr. Bonafide also holds a Young Investigator Award grant from the Academic Pediatric Association evaluating the impact of a data‐driven monitor alarm reduction strategy implemented in safety huddles. Dr. Brady is supported by the Agency for Healthcare Research and Quality under award number K08HS23827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Agency for Healthcare Research and Quality. This study was funded by the Arnold W. Strauss Fellow Grant, Cincinnati Children's Hospital Medical Center. The authors have no conflicts of interest to disclose.
- , , , et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144.
- , , , 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.
- , , , 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.
- , , , , . Traditional measures of hospital utilization may not accurately reflect dynamic patient demand: findings from a children's hospital. Hosp Pediatr. 2012;2(1):10–18.
- , , , et al. A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694.
- , , , . Daily electrode change and effect on cardiac monitor alarms: an evidence‐based practice approach. J Nurs Care Qual. 2013;28(3):265–271.
- , , , et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144.
- , , , 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.
- , , , 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.
- , , , , . Traditional measures of hospital utilization may not accurately reflect dynamic patient demand: findings from a children's hospital. Hosp Pediatr. 2012;2(1):10–18.
- , , , et al. A team‐based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686–e1694.
- , , , . Daily electrode change and effect on cardiac monitor alarms: an evidence‐based practice approach. J Nurs Care Qual. 2013;28(3):265–271.
Alarm Fatigue
Alarm fatigue is not a new issue for hospitals. In a commentary written over 3 decades ago, Kerr and Hayes described what they saw as an alarming issue developing in intensive care units.[1] Recently multiple organizations, including The Joint Commission and the Emergency Care Research Institute have called out alarm fatigue as a patient safety problem,[2, 3, 4] and organizations such as the American Academy of Pediatrics and the American Heart Association are backing away from recommendations for continuous monitoring.[5, 6] Hospitals are in a scramble to set up alarm committees and address alarms locally as recommended by The Joint Commission.[2] In this issue of the Journal of Hospital Medicine, Paine and colleagues set out to review the small but growing body of literature addressing physiologic monitor alarms and interventions that have tried to address alarm fatigue.[7]
After searching through 4629 titles, the authors found 32 articles addressing their key questions: What proportion of alarms are actionable? What is the relationship between clinicians' alarm exposure and response time? Which interventions are effective for reducing alarm rates? The majority of studies identified were observational, with only 8 studies addressing interventions to reduce alarms. Many of the identified studies occurred in units taking care of adults, though 10 descriptive studies and 1 intervention study occurred in pediatric settings. Perhaps the most concerning finding of all, though not surprising to those who work in the hospital setting, was that somewhere between <1% and 26% of alarms across all studies were considered actionable. Although only specifically addressed in 2 studies, the issue of alarm fatigue (i.e., more alarms leading to slower and sometimes absent clinician response) was supported in both, with nurses having slower responses when exposed to a higher numbers of alarms.[8, 9]
The authors note several limitations of their work, one of which is the modest body of literature on the topic. Although several interventions, including widening alarm parameters, increasing alarm delays, and using disposable leads or daily lead changes, have early evidence of success in safely reducing unnecessary alarms, the heterogeneity of this literature precluded a meta‐analysis. Further, the lack of standard definitions and the variety of methods of determining alarm validity make comparison across studies challenging. For this reason, the authors note that they did not distinguish nuisance alarms (i.e., alarms that accurately reflect the patient condition but do not require any intervention) from invalid alarms (i.e., alarms that do not correctly reflect the patient condition). This is relevant because it is likely that interventions to reduce invalid alarms (e.g., frequent lead changes) may be distinct from those that will successfully address nuisance alarms (e.g., widening alarm limits). It is also important to note that although patient safety is of paramount importance, there were other negative consequences of alarms that the authors did not address in this systemic review. Moreover, although avoiding unrecognized deterioration should be a primary goal of any program to reduce alarm fatigue, death remains uncommon compared to the number of patients, families, and healthcare workers exposed to high numbers of alarms during hospitalization. The high number of nonactionable alarms suggests that part of the burden of this problem may lie in more difficult to quantify outcomes such as sleep quality,[10, 11, 12] patient and parent quality of life during hospitalization,[13, 14] and interrupted tasks and cognitive work of healthcare providers.[15]
Paine and colleagues' review has some certain and some less certain implications for the future of alarm research. First, there is an imminent need for researchers and improvers to develop a consensus around terminology and metrics. We need to agree on what is and is not an actionable alarm, and we need valid and sensitive metrics to better understand the consequences of not monitoring a patient who should be on monitors. Second, hospitals addressing alarm fatigue need benchmarks. As hospitals rush to comply with The Joint Commission National Patient Safety Goals for alarm management,[2] it is safe to say that our goal should not be zero alarms, but how low do you go? What can we consider a safe number of alarms in our hospitals? Smart alarms hold tremendous potential to improve the sensitivity and positive predictive value of alarms. However, their ultimate success is dependent on engineers in industry to develop the technology as well as researchers in the hospital setting to validate the technology's performance in clinical care. Additionally, hospitals need to know which interventions are most effective to implement and how to reliably implement these in daily practice. What seems less certain is what type of research is best suited to address this need. The authors recommend randomized trials as an immediate next step, and certainly trials are the gold standard in determining efficacy. However, trials may overstate effectiveness as complex bundled interventions play out in complex and dynamic hospital systems. Quasiexperimental study designs, including time series and step‐wedge designs, would allow for further scientific discovery, such as which interventions are most effective in certain patient populations, while describing reliable implementation of effective methods that lead to lower alarms rates. In both classical randomized controlled trials and quasiexperiments, factorial designs[16, 17] could give us a better understanding of both the comparative effect and any interaction between interventions.
Alarm fatigue is a widespread problem that has negative effects for patients, families, nurses, and physicians. This review demonstrates that the great majority of alarms do not help clinicians and likely contribute to alarm fatigue. The opportunity to improve care is unquestionably vast, and attention from The Joint Commission and the lay press ensures change will occur. What is critical now is for hospitalists, intensivists, nurses, researchers, and hospital administrators to find the right combination of scientific discovery, thoughtful collaboration with industry, and quality improvement that will inform the literature on which interventions worked, how, and in what setting, and ultimately lead to safer (and quieter) hospitals.
Disclosures
Dr. Brady is supported by the Agency for Healthcare Research and Quality under award number K08HS023827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Landrigan is supported in part by the Children's Hospital Association for his work as an executive council member of the Pediatric Research in Inpatient Settings network. Dr. Landrigan serves as a consultant to Virgin Pulse regarding sleep, safety, and health. In addition, Dr. Landrigan has received monetary awards, honoraria, and travel reimbursement from multiple academic and professional organizations for delivering lectures on sleep deprivation, physician performance, handoffs, and patient safety, and has served as an expert witness in cases regarding patient safety. The authors report no other funding, financial relationships, or conflicts of interest.
- , . An “alarming” situation in the intensive therapy unit. Intensive Care Med. 1983;9(3):103–104.
- The Joint Commission. National Patient Safety Goal on Alarm Management. Available at: http://www.jointcommission.org/assets/1/18/JCP0713_Announce_New_NSPG.pdf. Accessed October 23, 2015.
- Joint Commission. Medical device alarm safety in hospitals. Sentinel Event Alert. 2013;(50):1–3.
- Top 10 health technology hazards for 2014. Health Devices. 2013;42(11):354–380.
- , , , et al. Clinical practice guideline: the diagnosis, management, and prevention of bronchiolitis. Pediatrics. 2014;134(5):e1474–e1502.
- , , , et al. Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical‐Care Nurses. Circulation. 2004;110(17):2721–2746.
- , , , , , , . Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144.
- , , , 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.
- , , , 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.
- . Sleep deprivation is an additional stress for parents staying in hospital. J Spec Pediatr Nurs. 2008;13(2):111–122.
- , , , , . The sound intensity and characteristics of variable‐pitch pulse oximeters. J Clin Monit Comput. 2008;22(3):199–207.
- , , , . Factors influencing sleep for parents of critically ill hospitalised children: a qualitative analysis. Intensive Crit Care Nurs. 2011;27(1):37–45.
- , , , , . Perceptions of stress, worry, and support in Black and White mothers of hospitalized, medically fragile infants. J Pediatr Nurs. 2002;17(2):82–88.
- , , , . Parents' responses to stress in the neonatal intensive care unit. Crit Care Nurs. 2013;33(4):52–59; quiz 60.
- , . Alarm fatigue and its influence on staff performance. IIE Trans Healthc Syst Eng. 2015;5(3):183–196.
- , , . Quality Improvement Through Planned Experimentation. 3rd ed. New York, NY: McGraw‐Hill; 1991.
- , . The Health Care Data Guide: Learning From Data for Improvement. San Francisco, CA: Jossey‐Bass; 2011.
Alarm fatigue is not a new issue for hospitals. In a commentary written over 3 decades ago, Kerr and Hayes described what they saw as an alarming issue developing in intensive care units.[1] Recently multiple organizations, including The Joint Commission and the Emergency Care Research Institute have called out alarm fatigue as a patient safety problem,[2, 3, 4] and organizations such as the American Academy of Pediatrics and the American Heart Association are backing away from recommendations for continuous monitoring.[5, 6] Hospitals are in a scramble to set up alarm committees and address alarms locally as recommended by The Joint Commission.[2] In this issue of the Journal of Hospital Medicine, Paine and colleagues set out to review the small but growing body of literature addressing physiologic monitor alarms and interventions that have tried to address alarm fatigue.[7]
After searching through 4629 titles, the authors found 32 articles addressing their key questions: What proportion of alarms are actionable? What is the relationship between clinicians' alarm exposure and response time? Which interventions are effective for reducing alarm rates? The majority of studies identified were observational, with only 8 studies addressing interventions to reduce alarms. Many of the identified studies occurred in units taking care of adults, though 10 descriptive studies and 1 intervention study occurred in pediatric settings. Perhaps the most concerning finding of all, though not surprising to those who work in the hospital setting, was that somewhere between <1% and 26% of alarms across all studies were considered actionable. Although only specifically addressed in 2 studies, the issue of alarm fatigue (i.e., more alarms leading to slower and sometimes absent clinician response) was supported in both, with nurses having slower responses when exposed to a higher numbers of alarms.[8, 9]
The authors note several limitations of their work, one of which is the modest body of literature on the topic. Although several interventions, including widening alarm parameters, increasing alarm delays, and using disposable leads or daily lead changes, have early evidence of success in safely reducing unnecessary alarms, the heterogeneity of this literature precluded a meta‐analysis. Further, the lack of standard definitions and the variety of methods of determining alarm validity make comparison across studies challenging. For this reason, the authors note that they did not distinguish nuisance alarms (i.e., alarms that accurately reflect the patient condition but do not require any intervention) from invalid alarms (i.e., alarms that do not correctly reflect the patient condition). This is relevant because it is likely that interventions to reduce invalid alarms (e.g., frequent lead changes) may be distinct from those that will successfully address nuisance alarms (e.g., widening alarm limits). It is also important to note that although patient safety is of paramount importance, there were other negative consequences of alarms that the authors did not address in this systemic review. Moreover, although avoiding unrecognized deterioration should be a primary goal of any program to reduce alarm fatigue, death remains uncommon compared to the number of patients, families, and healthcare workers exposed to high numbers of alarms during hospitalization. The high number of nonactionable alarms suggests that part of the burden of this problem may lie in more difficult to quantify outcomes such as sleep quality,[10, 11, 12] patient and parent quality of life during hospitalization,[13, 14] and interrupted tasks and cognitive work of healthcare providers.[15]
Paine and colleagues' review has some certain and some less certain implications for the future of alarm research. First, there is an imminent need for researchers and improvers to develop a consensus around terminology and metrics. We need to agree on what is and is not an actionable alarm, and we need valid and sensitive metrics to better understand the consequences of not monitoring a patient who should be on monitors. Second, hospitals addressing alarm fatigue need benchmarks. As hospitals rush to comply with The Joint Commission National Patient Safety Goals for alarm management,[2] it is safe to say that our goal should not be zero alarms, but how low do you go? What can we consider a safe number of alarms in our hospitals? Smart alarms hold tremendous potential to improve the sensitivity and positive predictive value of alarms. However, their ultimate success is dependent on engineers in industry to develop the technology as well as researchers in the hospital setting to validate the technology's performance in clinical care. Additionally, hospitals need to know which interventions are most effective to implement and how to reliably implement these in daily practice. What seems less certain is what type of research is best suited to address this need. The authors recommend randomized trials as an immediate next step, and certainly trials are the gold standard in determining efficacy. However, trials may overstate effectiveness as complex bundled interventions play out in complex and dynamic hospital systems. Quasiexperimental study designs, including time series and step‐wedge designs, would allow for further scientific discovery, such as which interventions are most effective in certain patient populations, while describing reliable implementation of effective methods that lead to lower alarms rates. In both classical randomized controlled trials and quasiexperiments, factorial designs[16, 17] could give us a better understanding of both the comparative effect and any interaction between interventions.
Alarm fatigue is a widespread problem that has negative effects for patients, families, nurses, and physicians. This review demonstrates that the great majority of alarms do not help clinicians and likely contribute to alarm fatigue. The opportunity to improve care is unquestionably vast, and attention from The Joint Commission and the lay press ensures change will occur. What is critical now is for hospitalists, intensivists, nurses, researchers, and hospital administrators to find the right combination of scientific discovery, thoughtful collaboration with industry, and quality improvement that will inform the literature on which interventions worked, how, and in what setting, and ultimately lead to safer (and quieter) hospitals.
Disclosures
Dr. Brady is supported by the Agency for Healthcare Research and Quality under award number K08HS023827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Landrigan is supported in part by the Children's Hospital Association for his work as an executive council member of the Pediatric Research in Inpatient Settings network. Dr. Landrigan serves as a consultant to Virgin Pulse regarding sleep, safety, and health. In addition, Dr. Landrigan has received monetary awards, honoraria, and travel reimbursement from multiple academic and professional organizations for delivering lectures on sleep deprivation, physician performance, handoffs, and patient safety, and has served as an expert witness in cases regarding patient safety. The authors report no other funding, financial relationships, or conflicts of interest.
Alarm fatigue is not a new issue for hospitals. In a commentary written over 3 decades ago, Kerr and Hayes described what they saw as an alarming issue developing in intensive care units.[1] Recently multiple organizations, including The Joint Commission and the Emergency Care Research Institute have called out alarm fatigue as a patient safety problem,[2, 3, 4] and organizations such as the American Academy of Pediatrics and the American Heart Association are backing away from recommendations for continuous monitoring.[5, 6] Hospitals are in a scramble to set up alarm committees and address alarms locally as recommended by The Joint Commission.[2] In this issue of the Journal of Hospital Medicine, Paine and colleagues set out to review the small but growing body of literature addressing physiologic monitor alarms and interventions that have tried to address alarm fatigue.[7]
After searching through 4629 titles, the authors found 32 articles addressing their key questions: What proportion of alarms are actionable? What is the relationship between clinicians' alarm exposure and response time? Which interventions are effective for reducing alarm rates? The majority of studies identified were observational, with only 8 studies addressing interventions to reduce alarms. Many of the identified studies occurred in units taking care of adults, though 10 descriptive studies and 1 intervention study occurred in pediatric settings. Perhaps the most concerning finding of all, though not surprising to those who work in the hospital setting, was that somewhere between <1% and 26% of alarms across all studies were considered actionable. Although only specifically addressed in 2 studies, the issue of alarm fatigue (i.e., more alarms leading to slower and sometimes absent clinician response) was supported in both, with nurses having slower responses when exposed to a higher numbers of alarms.[8, 9]
The authors note several limitations of their work, one of which is the modest body of literature on the topic. Although several interventions, including widening alarm parameters, increasing alarm delays, and using disposable leads or daily lead changes, have early evidence of success in safely reducing unnecessary alarms, the heterogeneity of this literature precluded a meta‐analysis. Further, the lack of standard definitions and the variety of methods of determining alarm validity make comparison across studies challenging. For this reason, the authors note that they did not distinguish nuisance alarms (i.e., alarms that accurately reflect the patient condition but do not require any intervention) from invalid alarms (i.e., alarms that do not correctly reflect the patient condition). This is relevant because it is likely that interventions to reduce invalid alarms (e.g., frequent lead changes) may be distinct from those that will successfully address nuisance alarms (e.g., widening alarm limits). It is also important to note that although patient safety is of paramount importance, there were other negative consequences of alarms that the authors did not address in this systemic review. Moreover, although avoiding unrecognized deterioration should be a primary goal of any program to reduce alarm fatigue, death remains uncommon compared to the number of patients, families, and healthcare workers exposed to high numbers of alarms during hospitalization. The high number of nonactionable alarms suggests that part of the burden of this problem may lie in more difficult to quantify outcomes such as sleep quality,[10, 11, 12] patient and parent quality of life during hospitalization,[13, 14] and interrupted tasks and cognitive work of healthcare providers.[15]
Paine and colleagues' review has some certain and some less certain implications for the future of alarm research. First, there is an imminent need for researchers and improvers to develop a consensus around terminology and metrics. We need to agree on what is and is not an actionable alarm, and we need valid and sensitive metrics to better understand the consequences of not monitoring a patient who should be on monitors. Second, hospitals addressing alarm fatigue need benchmarks. As hospitals rush to comply with The Joint Commission National Patient Safety Goals for alarm management,[2] it is safe to say that our goal should not be zero alarms, but how low do you go? What can we consider a safe number of alarms in our hospitals? Smart alarms hold tremendous potential to improve the sensitivity and positive predictive value of alarms. However, their ultimate success is dependent on engineers in industry to develop the technology as well as researchers in the hospital setting to validate the technology's performance in clinical care. Additionally, hospitals need to know which interventions are most effective to implement and how to reliably implement these in daily practice. What seems less certain is what type of research is best suited to address this need. The authors recommend randomized trials as an immediate next step, and certainly trials are the gold standard in determining efficacy. However, trials may overstate effectiveness as complex bundled interventions play out in complex and dynamic hospital systems. Quasiexperimental study designs, including time series and step‐wedge designs, would allow for further scientific discovery, such as which interventions are most effective in certain patient populations, while describing reliable implementation of effective methods that lead to lower alarms rates. In both classical randomized controlled trials and quasiexperiments, factorial designs[16, 17] could give us a better understanding of both the comparative effect and any interaction between interventions.
Alarm fatigue is a widespread problem that has negative effects for patients, families, nurses, and physicians. This review demonstrates that the great majority of alarms do not help clinicians and likely contribute to alarm fatigue. The opportunity to improve care is unquestionably vast, and attention from The Joint Commission and the lay press ensures change will occur. What is critical now is for hospitalists, intensivists, nurses, researchers, and hospital administrators to find the right combination of scientific discovery, thoughtful collaboration with industry, and quality improvement that will inform the literature on which interventions worked, how, and in what setting, and ultimately lead to safer (and quieter) hospitals.
Disclosures
Dr. Brady is supported by the Agency for Healthcare Research and Quality under award number K08HS023827. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Landrigan is supported in part by the Children's Hospital Association for his work as an executive council member of the Pediatric Research in Inpatient Settings network. Dr. Landrigan serves as a consultant to Virgin Pulse regarding sleep, safety, and health. In addition, Dr. Landrigan has received monetary awards, honoraria, and travel reimbursement from multiple academic and professional organizations for delivering lectures on sleep deprivation, physician performance, handoffs, and patient safety, and has served as an expert witness in cases regarding patient safety. The authors report no other funding, financial relationships, or conflicts of interest.
- , . An “alarming” situation in the intensive therapy unit. Intensive Care Med. 1983;9(3):103–104.
- The Joint Commission. National Patient Safety Goal on Alarm Management. Available at: http://www.jointcommission.org/assets/1/18/JCP0713_Announce_New_NSPG.pdf. Accessed October 23, 2015.
- Joint Commission. Medical device alarm safety in hospitals. Sentinel Event Alert. 2013;(50):1–3.
- Top 10 health technology hazards for 2014. Health Devices. 2013;42(11):354–380.
- , , , et al. Clinical practice guideline: the diagnosis, management, and prevention of bronchiolitis. Pediatrics. 2014;134(5):e1474–e1502.
- , , , et al. Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical‐Care Nurses. Circulation. 2004;110(17):2721–2746.
- , , , , , , . Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144.
- , , , 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.
- , , , 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.
- . Sleep deprivation is an additional stress for parents staying in hospital. J Spec Pediatr Nurs. 2008;13(2):111–122.
- , , , , . The sound intensity and characteristics of variable‐pitch pulse oximeters. J Clin Monit Comput. 2008;22(3):199–207.
- , , , . Factors influencing sleep for parents of critically ill hospitalised children: a qualitative analysis. Intensive Crit Care Nurs. 2011;27(1):37–45.
- , , , , . Perceptions of stress, worry, and support in Black and White mothers of hospitalized, medically fragile infants. J Pediatr Nurs. 2002;17(2):82–88.
- , , , . Parents' responses to stress in the neonatal intensive care unit. Crit Care Nurs. 2013;33(4):52–59; quiz 60.
- , . Alarm fatigue and its influence on staff performance. IIE Trans Healthc Syst Eng. 2015;5(3):183–196.
- , , . Quality Improvement Through Planned Experimentation. 3rd ed. New York, NY: McGraw‐Hill; 1991.
- , . The Health Care Data Guide: Learning From Data for Improvement. San Francisco, CA: Jossey‐Bass; 2011.
- , . An “alarming” situation in the intensive therapy unit. Intensive Care Med. 1983;9(3):103–104.
- The Joint Commission. National Patient Safety Goal on Alarm Management. Available at: http://www.jointcommission.org/assets/1/18/JCP0713_Announce_New_NSPG.pdf. Accessed October 23, 2015.
- Joint Commission. Medical device alarm safety in hospitals. Sentinel Event Alert. 2013;(50):1–3.
- Top 10 health technology hazards for 2014. Health Devices. 2013;42(11):354–380.
- , , , et al. Clinical practice guideline: the diagnosis, management, and prevention of bronchiolitis. Pediatrics. 2014;134(5):e1474–e1502.
- , , , et al. Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical‐Care Nurses. Circulation. 2004;110(17):2721–2746.
- , , , , , , . Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med. 2016;11(2):136–144.
- , , , 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.
- , , , 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.
- . Sleep deprivation is an additional stress for parents staying in hospital. J Spec Pediatr Nurs. 2008;13(2):111–122.
- , , , , . The sound intensity and characteristics of variable‐pitch pulse oximeters. J Clin Monit Comput. 2008;22(3):199–207.
- , , , . Factors influencing sleep for parents of critically ill hospitalised children: a qualitative analysis. Intensive Crit Care Nurs. 2011;27(1):37–45.
- , , , , . Perceptions of stress, worry, and support in Black and White mothers of hospitalized, medically fragile infants. J Pediatr Nurs. 2002;17(2):82–88.
- , , , . Parents' responses to stress in the neonatal intensive care unit. Crit Care Nurs. 2013;33(4):52–59; quiz 60.
- , . Alarm fatigue and its influence on staff performance. IIE Trans Healthc Syst Eng. 2015;5(3):183–196.
- , , . Quality Improvement Through Planned Experimentation. 3rd ed. New York, NY: McGraw‐Hill; 1991.
- , . The Health Care Data Guide: Learning From Data for Improvement. San Francisco, CA: Jossey‐Bass; 2011.