Christopher P Bonafide, MD, MSCE

Practice guidelines discourage continuous pulse oximetry (SpO₂) monitoring of patients with bronchiolitis who are not receiving supplemental oxygen.^1,2 Overuse of SpO₂ monitoring in this patient population has been associated with increased length of stay, unnecessary oxygen therapy, and excess hospital costs, without measurable patient benefit.^3-5 In spite of this evidence base and expert guidance, nearly half of the more than 100,000 infants admitted for bronchiolitis each year receive excess SpO₂ monitoring.^6,7

Bronchiolitis guidelines suggest that guideline-discordant SpO₂ monitoring may result in excess alarms, which disrupt families’ sleep and engender alarm fatigue among staff.¹ Pediatric nurses receive up to 155 alarms per monitored patient per day.^8,9 Frequent alarms are associated with slower nurse response times^10,11 and increased nurse subjective workload.¹²The rate of excess alarms occurring during guideline-discordant, continuously SpO₂ monitored time, compared to the rate of alarms occurring during guideline-concordant (intermittently measured SpO₂) time, has not been evaluated. The magnitude of this difference in alarm rates, if such a difference exists, will inform prioritization of guideline-discordant continuous SpO₂ measurement de-implementation. The objective of this study was to quantify the alarm burden associated with excess SpO₂ monitoring of bronchiolitis patients not receiving supplemental oxygen.

Cohort

We retrospectively evaluated SpO₂ monitoring patterns and alarm rates of children 0 to 24 months old admitted to a general pediatrics service at a tertiary care children’s hospital. We included patients who had a discharge diagnosis of bronchiolitis (International Classification of Diseases, Tenth Revision codes J45x, T17.2x, T17.3x, T17.4x, T17.5x, T17.8x, T17.9x, A37xx, J04x, J05x, J05.1x, J69.0x, J69.1x, J69.8x) between November 24, 2019, and January 21, 2020, the period of time during which alarm data and monitor data were concurrently available for analysis. In order to conservatively assure applicability of clinical practice guidelines, we excluded patients with discharge diagnoses that included other respiratory conditions (eg, reactive airway disease), patients with complex chronic conditions (CCC) as defined by the CCC version 2 classification system,¹³ and patients with intensive care unit (ICU) stays during the admission.

Time

Flowsheet data detailing nursing respiratory assessments were extracted from the electronic health record (EHR) database (Clarity, Epic Systems). Using previously validated methodology,¹⁴ we identified minutes during which patients received supplemental oxygen or high-flow nasal cannula (supplemental oxygen) based on the documented fraction of inspired oxygen (FiO₂), flow rate, and support devices. We then identified the final discontinuation of respiratory support during the hospital admission, and censored the 60 minutes after final discontinuation of supplemental oxygen based upon recent monitoring guidelines.² Minutes up to an hour after supplemental oxygen discontinuation were classified as receiving supplemental oxygen and not included in our analysis. Only minutes between the end of the censored period and hospital discharge were used in the analysis. For patients who never received respiratory support during the admission, we censored the first 60 minutes of the admission and analyzed the remainder of their stay.

SpO₂ Monitoring

We used device-integrated, physiologic-monitor, vital sign data sent each minute from the General Electric monitor network to the EHR to identify minutes during which patients were connected to physiologic monitors and transmitting signals from SpO₂ sensors. We extracted minute-level SpO₂ data from the hospital clinical data warehouse (CDW). Minutes in which SpO₂ data were present were classified as “monitored,” an approach previously validated using in-person observation.¹⁴

To categorize time as “not receiving supplemental oxygen and continuously monitored (guideline-discordant monitoring),” or “not receiving supplemental oxygen and not continuously monitored (guideline-concordant intermittent measurement),” we evaluated the percent of minutes within an hour during which the patient received SpO₂ monitoring and applied an a priori conservative rule. Hours during which ≥90% of minutes had SpO₂ monitoring data were classified as “continuously monitored.” Hours during which ≤10% of minutes had SpO₂ monitoring data were classified as “intermittently measured.” Hours during which 11% to 89% of minutes included monitor data were excluded from further analysis. The number of continuously monitored hours was tabulated for each patient. The median number of continuously monitored hours was computed; results were stratified by prior receipt of respiratory support.

Alarm Counts

Minute-level monitor alarm counts (the absolute number of abnormal vital signs that triggered a monitor to alarm) were extracted from the CDW. Alarm counts were tabulated for each patient hour. For each patient, the alarm rate (total number of alarms divided by time) was computed for continuously monitored and intermittently measured time. Results were stratified by prior receipt of respiratory support.

The study was reviewed by the institutional review board and determined to meet exemption criteria.

Our cohort included 201 admissions by 197 unique patients (Table). We evaluated 4402 hours that occurred ≥60 minutes following final discontinuation of supplemental oxygen, the time period during which guidelines discourage routine use of continuous SpO₂ monitoring. This represented a median of 19 hours (interquartile range [IQR], 14-25) per admission. We excluded 474 hours (11%) that could not be classified as either continuously or intermittently measured.

During time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 1537 hours of guideline-discordant continuous monitoring, a median of 6 hours (IQR, 3-12) per admission. Patients experienced a median of 12 hours (IQR, 5-17) of intermittent measurement. Among patients who received supplemental oxygen, 91% experienced guideline-discordant continuous SpO₂ monitoring, as compared to 68% of patients who did not receive supplemental oxygen. Among those who received guideline-discordant continuous SpO₂ monitoring, the duration of this monitoring did not differ significantly between those who had received supplemental oxygen during the admission and those who had not.

During classifiable time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 14,371 alarms; 77% (11,101) of these alarms were generated during periods of guideline-discordant continuous monitoring. The median hourly alarm rate during these periods of guideline-discordant continuous monitoring was 6.7 alarms per hour (IQR, 2.1-12.3), representing a median of 35 (IQR, 10-81) additional alarms per patient. During periods of guideline-concordant intermittent measurement, the median hourly alarm rate was 0.5 (IQR, 0.1-0.8), with a median of 5 (IQR, 1-13) alarms per patient.

Those who received supplemental oxygen earlier in the admission had higher alarm rates during continuously monitored time (7.3 per hour [IQR, 2.7-12.7]) than patients who had not received supplemental oxygen (3.3 per hour [IQR, 0.6-11.8]), likely reflecting clinical differences between these patient populations. The most frequent alarm type among continuously monitored patients who had previously received supplemental oxygen was “SpO₂ low.”

Across 4402 patient hours, guideline-discordant continuous SpO₂ monitoring of patients with bronchiolitis resulted in 11,101 alarms, at a rate of approximately 1 additional alarm every 9 minutes. Patients in our cohort received a median of 6 hours of guideline-discordant monitoring, which imposes a significant alarm burden that is potentially modifiable using targeted reduction strategies.¹⁵

Transient, self-resolved hypoxemia is a common feature of bronchiolitis and likely of little clinical consequence.¹⁶ Therefore, this rate of hypoxemia alarms is not unexpected. Though we evaluated only the period of time following final discontinuation of respiratory support, this finding is in keeping with the literature associating excess physiologic monitoring of patients with bronchiolitis with unnecessary oxygen therapy and increased length of stay,^3-5 largely because clinicians may feel compelled to respond to hypoxemia alarms with either supplemental oxygen or longer monitoring.

Our findings must be contextualized in light of the limitations of our approach. We did not evaluate nurse workload associated with guideline-discordant continuous SpO₂ monitoring. Prior work conducted by our lab has demonstrated that when nurses experience more than 40 alarms within a 2-hour period, their measured subjective workload increases to a degree associated with missing important tasks, threatening the quality and safety of the care they deliver.^12,17 Given that nurses care for multiple patients, it is likely that the excess alarms introduced by guideline-discordant continuous monitoring contribute to increased nurse workload and alarm fatigue.

Similarly, we could not evaluate whether the alarms nurses experienced were actionable. Although our lab has previously reported that ≥99% of alarms occurring on non-ICU pediatric wards are nonactionable,^10,11 it is possible that some of the alarms during guideline-discordant monitoring periods required action. However, it is unlikely that any life-sustaining actions were taken because (1) we only evaluated time >60 minutes after final discontinuation of supplemental oxygen, so by definition none of these alarms required treatment with supplemental oxygen, and (2) none of the patients we included received ICU care during their admission.

The avoidable alarm burden identified in our analysis suggests that eliminating continuous SpO₂ monitoring overuse in bronchiolitis will likely reduce nurses’ workload and alarm fatigue in hospital settings that care for children with bronchiolitis.

Practice guidelines discourage continuous pulse oximetry (SpO₂) monitoring of patients with bronchiolitis who are not receiving supplemental oxygen.^1,2 Overuse of SpO₂ monitoring in this patient population has been associated with increased length of stay, unnecessary oxygen therapy, and excess hospital costs, without measurable patient benefit.^3-5 In spite of this evidence base and expert guidance, nearly half of the more than 100,000 infants admitted for bronchiolitis each year receive excess SpO₂ monitoring.^6,7

Bronchiolitis guidelines suggest that guideline-discordant SpO₂ monitoring may result in excess alarms, which disrupt families’ sleep and engender alarm fatigue among staff.¹ Pediatric nurses receive up to 155 alarms per monitored patient per day.^8,9 Frequent alarms are associated with slower nurse response times^10,11 and increased nurse subjective workload.¹²The rate of excess alarms occurring during guideline-discordant, continuously SpO₂ monitored time, compared to the rate of alarms occurring during guideline-concordant (intermittently measured SpO₂) time, has not been evaluated. The magnitude of this difference in alarm rates, if such a difference exists, will inform prioritization of guideline-discordant continuous SpO₂ measurement de-implementation. The objective of this study was to quantify the alarm burden associated with excess SpO₂ monitoring of bronchiolitis patients not receiving supplemental oxygen.

Cohort

We retrospectively evaluated SpO₂ monitoring patterns and alarm rates of children 0 to 24 months old admitted to a general pediatrics service at a tertiary care children’s hospital. We included patients who had a discharge diagnosis of bronchiolitis (International Classification of Diseases, Tenth Revision codes J45x, T17.2x, T17.3x, T17.4x, T17.5x, T17.8x, T17.9x, A37xx, J04x, J05x, J05.1x, J69.0x, J69.1x, J69.8x) between November 24, 2019, and January 21, 2020, the period of time during which alarm data and monitor data were concurrently available for analysis. In order to conservatively assure applicability of clinical practice guidelines, we excluded patients with discharge diagnoses that included other respiratory conditions (eg, reactive airway disease), patients with complex chronic conditions (CCC) as defined by the CCC version 2 classification system,¹³ and patients with intensive care unit (ICU) stays during the admission.

Time

Flowsheet data detailing nursing respiratory assessments were extracted from the electronic health record (EHR) database (Clarity, Epic Systems). Using previously validated methodology,¹⁴ we identified minutes during which patients received supplemental oxygen or high-flow nasal cannula (supplemental oxygen) based on the documented fraction of inspired oxygen (FiO₂), flow rate, and support devices. We then identified the final discontinuation of respiratory support during the hospital admission, and censored the 60 minutes after final discontinuation of supplemental oxygen based upon recent monitoring guidelines.² Minutes up to an hour after supplemental oxygen discontinuation were classified as receiving supplemental oxygen and not included in our analysis. Only minutes between the end of the censored period and hospital discharge were used in the analysis. For patients who never received respiratory support during the admission, we censored the first 60 minutes of the admission and analyzed the remainder of their stay.

SpO₂ Monitoring

We used device-integrated, physiologic-monitor, vital sign data sent each minute from the General Electric monitor network to the EHR to identify minutes during which patients were connected to physiologic monitors and transmitting signals from SpO₂ sensors. We extracted minute-level SpO₂ data from the hospital clinical data warehouse (CDW). Minutes in which SpO₂ data were present were classified as “monitored,” an approach previously validated using in-person observation.¹⁴

To categorize time as “not receiving supplemental oxygen and continuously monitored (guideline-discordant monitoring),” or “not receiving supplemental oxygen and not continuously monitored (guideline-concordant intermittent measurement),” we evaluated the percent of minutes within an hour during which the patient received SpO₂ monitoring and applied an a priori conservative rule. Hours during which ≥90% of minutes had SpO₂ monitoring data were classified as “continuously monitored.” Hours during which ≤10% of minutes had SpO₂ monitoring data were classified as “intermittently measured.” Hours during which 11% to 89% of minutes included monitor data were excluded from further analysis. The number of continuously monitored hours was tabulated for each patient. The median number of continuously monitored hours was computed; results were stratified by prior receipt of respiratory support.

Alarm Counts

Minute-level monitor alarm counts (the absolute number of abnormal vital signs that triggered a monitor to alarm) were extracted from the CDW. Alarm counts were tabulated for each patient hour. For each patient, the alarm rate (total number of alarms divided by time) was computed for continuously monitored and intermittently measured time. Results were stratified by prior receipt of respiratory support.

The study was reviewed by the institutional review board and determined to meet exemption criteria.

Our cohort included 201 admissions by 197 unique patients (Table). We evaluated 4402 hours that occurred ≥60 minutes following final discontinuation of supplemental oxygen, the time period during which guidelines discourage routine use of continuous SpO₂ monitoring. This represented a median of 19 hours (interquartile range [IQR], 14-25) per admission. We excluded 474 hours (11%) that could not be classified as either continuously or intermittently measured.

During time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 1537 hours of guideline-discordant continuous monitoring, a median of 6 hours (IQR, 3-12) per admission. Patients experienced a median of 12 hours (IQR, 5-17) of intermittent measurement. Among patients who received supplemental oxygen, 91% experienced guideline-discordant continuous SpO₂ monitoring, as compared to 68% of patients who did not receive supplemental oxygen. Among those who received guideline-discordant continuous SpO₂ monitoring, the duration of this monitoring did not differ significantly between those who had received supplemental oxygen during the admission and those who had not.

During classifiable time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 14,371 alarms; 77% (11,101) of these alarms were generated during periods of guideline-discordant continuous monitoring. The median hourly alarm rate during these periods of guideline-discordant continuous monitoring was 6.7 alarms per hour (IQR, 2.1-12.3), representing a median of 35 (IQR, 10-81) additional alarms per patient. During periods of guideline-concordant intermittent measurement, the median hourly alarm rate was 0.5 (IQR, 0.1-0.8), with a median of 5 (IQR, 1-13) alarms per patient.

Those who received supplemental oxygen earlier in the admission had higher alarm rates during continuously monitored time (7.3 per hour [IQR, 2.7-12.7]) than patients who had not received supplemental oxygen (3.3 per hour [IQR, 0.6-11.8]), likely reflecting clinical differences between these patient populations. The most frequent alarm type among continuously monitored patients who had previously received supplemental oxygen was “SpO₂ low.”

Across 4402 patient hours, guideline-discordant continuous SpO₂ monitoring of patients with bronchiolitis resulted in 11,101 alarms, at a rate of approximately 1 additional alarm every 9 minutes. Patients in our cohort received a median of 6 hours of guideline-discordant monitoring, which imposes a significant alarm burden that is potentially modifiable using targeted reduction strategies.¹⁵

Transient, self-resolved hypoxemia is a common feature of bronchiolitis and likely of little clinical consequence.¹⁶ Therefore, this rate of hypoxemia alarms is not unexpected. Though we evaluated only the period of time following final discontinuation of respiratory support, this finding is in keeping with the literature associating excess physiologic monitoring of patients with bronchiolitis with unnecessary oxygen therapy and increased length of stay,^3-5 largely because clinicians may feel compelled to respond to hypoxemia alarms with either supplemental oxygen or longer monitoring.

Our findings must be contextualized in light of the limitations of our approach. We did not evaluate nurse workload associated with guideline-discordant continuous SpO₂ monitoring. Prior work conducted by our lab has demonstrated that when nurses experience more than 40 alarms within a 2-hour period, their measured subjective workload increases to a degree associated with missing important tasks, threatening the quality and safety of the care they deliver.^12,17 Given that nurses care for multiple patients, it is likely that the excess alarms introduced by guideline-discordant continuous monitoring contribute to increased nurse workload and alarm fatigue.

Similarly, we could not evaluate whether the alarms nurses experienced were actionable. Although our lab has previously reported that ≥99% of alarms occurring on non-ICU pediatric wards are nonactionable,^10,11 it is possible that some of the alarms during guideline-discordant monitoring periods required action. However, it is unlikely that any life-sustaining actions were taken because (1) we only evaluated time >60 minutes after final discontinuation of supplemental oxygen, so by definition none of these alarms required treatment with supplemental oxygen, and (2) none of the patients we included received ICU care during their admission.

The avoidable alarm burden identified in our analysis suggests that eliminating continuous SpO₂ monitoring overuse in bronchiolitis will likely reduce nurses’ workload and alarm fatigue in hospital settings that care for children with bronchiolitis.

Practice guidelines discourage continuous pulse oximetry (SpO₂) monitoring of patients with bronchiolitis who are not receiving supplemental oxygen.^1,2 Overuse of SpO₂ monitoring in this patient population has been associated with increased length of stay, unnecessary oxygen therapy, and excess hospital costs, without measurable patient benefit.^3-5 In spite of this evidence base and expert guidance, nearly half of the more than 100,000 infants admitted for bronchiolitis each year receive excess SpO₂ monitoring.^6,7

Bronchiolitis guidelines suggest that guideline-discordant SpO₂ monitoring may result in excess alarms, which disrupt families’ sleep and engender alarm fatigue among staff.¹ Pediatric nurses receive up to 155 alarms per monitored patient per day.^8,9 Frequent alarms are associated with slower nurse response times^10,11 and increased nurse subjective workload.¹²The rate of excess alarms occurring during guideline-discordant, continuously SpO₂ monitored time, compared to the rate of alarms occurring during guideline-concordant (intermittently measured SpO₂) time, has not been evaluated. The magnitude of this difference in alarm rates, if such a difference exists, will inform prioritization of guideline-discordant continuous SpO₂ measurement de-implementation. The objective of this study was to quantify the alarm burden associated with excess SpO₂ monitoring of bronchiolitis patients not receiving supplemental oxygen.

Cohort

We retrospectively evaluated SpO₂ monitoring patterns and alarm rates of children 0 to 24 months old admitted to a general pediatrics service at a tertiary care children’s hospital. We included patients who had a discharge diagnosis of bronchiolitis (International Classification of Diseases, Tenth Revision codes J45x, T17.2x, T17.3x, T17.4x, T17.5x, T17.8x, T17.9x, A37xx, J04x, J05x, J05.1x, J69.0x, J69.1x, J69.8x) between November 24, 2019, and January 21, 2020, the period of time during which alarm data and monitor data were concurrently available for analysis. In order to conservatively assure applicability of clinical practice guidelines, we excluded patients with discharge diagnoses that included other respiratory conditions (eg, reactive airway disease), patients with complex chronic conditions (CCC) as defined by the CCC version 2 classification system,¹³ and patients with intensive care unit (ICU) stays during the admission.

Time

Flowsheet data detailing nursing respiratory assessments were extracted from the electronic health record (EHR) database (Clarity, Epic Systems). Using previously validated methodology,¹⁴ we identified minutes during which patients received supplemental oxygen or high-flow nasal cannula (supplemental oxygen) based on the documented fraction of inspired oxygen (FiO₂), flow rate, and support devices. We then identified the final discontinuation of respiratory support during the hospital admission, and censored the 60 minutes after final discontinuation of supplemental oxygen based upon recent monitoring guidelines.² Minutes up to an hour after supplemental oxygen discontinuation were classified as receiving supplemental oxygen and not included in our analysis. Only minutes between the end of the censored period and hospital discharge were used in the analysis. For patients who never received respiratory support during the admission, we censored the first 60 minutes of the admission and analyzed the remainder of their stay.

SpO₂ Monitoring

We used device-integrated, physiologic-monitor, vital sign data sent each minute from the General Electric monitor network to the EHR to identify minutes during which patients were connected to physiologic monitors and transmitting signals from SpO₂ sensors. We extracted minute-level SpO₂ data from the hospital clinical data warehouse (CDW). Minutes in which SpO₂ data were present were classified as “monitored,” an approach previously validated using in-person observation.¹⁴

To categorize time as “not receiving supplemental oxygen and continuously monitored (guideline-discordant monitoring),” or “not receiving supplemental oxygen and not continuously monitored (guideline-concordant intermittent measurement),” we evaluated the percent of minutes within an hour during which the patient received SpO₂ monitoring and applied an a priori conservative rule. Hours during which ≥90% of minutes had SpO₂ monitoring data were classified as “continuously monitored.” Hours during which ≤10% of minutes had SpO₂ monitoring data were classified as “intermittently measured.” Hours during which 11% to 89% of minutes included monitor data were excluded from further analysis. The number of continuously monitored hours was tabulated for each patient. The median number of continuously monitored hours was computed; results were stratified by prior receipt of respiratory support.

Alarm Counts

Minute-level monitor alarm counts (the absolute number of abnormal vital signs that triggered a monitor to alarm) were extracted from the CDW. Alarm counts were tabulated for each patient hour. For each patient, the alarm rate (total number of alarms divided by time) was computed for continuously monitored and intermittently measured time. Results were stratified by prior receipt of respiratory support.

The study was reviewed by the institutional review board and determined to meet exemption criteria.

Our cohort included 201 admissions by 197 unique patients (Table). We evaluated 4402 hours that occurred ≥60 minutes following final discontinuation of supplemental oxygen, the time period during which guidelines discourage routine use of continuous SpO₂ monitoring. This represented a median of 19 hours (interquartile range [IQR], 14-25) per admission. We excluded 474 hours (11%) that could not be classified as either continuously or intermittently measured.

During time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 1537 hours of guideline-discordant continuous monitoring, a median of 6 hours (IQR, 3-12) per admission. Patients experienced a median of 12 hours (IQR, 5-17) of intermittent measurement. Among patients who received supplemental oxygen, 91% experienced guideline-discordant continuous SpO₂ monitoring, as compared to 68% of patients who did not receive supplemental oxygen. Among those who received guideline-discordant continuous SpO₂ monitoring, the duration of this monitoring did not differ significantly between those who had received supplemental oxygen during the admission and those who had not.

During classifiable time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 14,371 alarms; 77% (11,101) of these alarms were generated during periods of guideline-discordant continuous monitoring. The median hourly alarm rate during these periods of guideline-discordant continuous monitoring was 6.7 alarms per hour (IQR, 2.1-12.3), representing a median of 35 (IQR, 10-81) additional alarms per patient. During periods of guideline-concordant intermittent measurement, the median hourly alarm rate was 0.5 (IQR, 0.1-0.8), with a median of 5 (IQR, 1-13) alarms per patient.

Those who received supplemental oxygen earlier in the admission had higher alarm rates during continuously monitored time (7.3 per hour [IQR, 2.7-12.7]) than patients who had not received supplemental oxygen (3.3 per hour [IQR, 0.6-11.8]), likely reflecting clinical differences between these patient populations. The most frequent alarm type among continuously monitored patients who had previously received supplemental oxygen was “SpO₂ low.”

Across 4402 patient hours, guideline-discordant continuous SpO₂ monitoring of patients with bronchiolitis resulted in 11,101 alarms, at a rate of approximately 1 additional alarm every 9 minutes. Patients in our cohort received a median of 6 hours of guideline-discordant monitoring, which imposes a significant alarm burden that is potentially modifiable using targeted reduction strategies.¹⁵

Transient, self-resolved hypoxemia is a common feature of bronchiolitis and likely of little clinical consequence.¹⁶ Therefore, this rate of hypoxemia alarms is not unexpected. Though we evaluated only the period of time following final discontinuation of respiratory support, this finding is in keeping with the literature associating excess physiologic monitoring of patients with bronchiolitis with unnecessary oxygen therapy and increased length of stay,^3-5 largely because clinicians may feel compelled to respond to hypoxemia alarms with either supplemental oxygen or longer monitoring.

Our findings must be contextualized in light of the limitations of our approach. We did not evaluate nurse workload associated with guideline-discordant continuous SpO₂ monitoring. Prior work conducted by our lab has demonstrated that when nurses experience more than 40 alarms within a 2-hour period, their measured subjective workload increases to a degree associated with missing important tasks, threatening the quality and safety of the care they deliver.^12,17 Given that nurses care for multiple patients, it is likely that the excess alarms introduced by guideline-discordant continuous monitoring contribute to increased nurse workload and alarm fatigue.

Similarly, we could not evaluate whether the alarms nurses experienced were actionable. Although our lab has previously reported that ≥99% of alarms occurring on non-ICU pediatric wards are nonactionable,^10,11 it is possible that some of the alarms during guideline-discordant monitoring periods required action. However, it is unlikely that any life-sustaining actions were taken because (1) we only evaluated time >60 minutes after final discontinuation of supplemental oxygen, so by definition none of these alarms required treatment with supplemental oxygen, and (2) none of the patients we included received ICU care during their admission.

The avoidable alarm burden identified in our analysis suggests that eliminating continuous SpO₂ monitoring overuse in bronchiolitis will likely reduce nurses’ workload and alarm fatigue in hospital settings that care for children with bronchiolitis.

Since the onset of the COVID-19 pandemic, the proclivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for adults and its relative sparing of pediatric populations has been well characterized. Accordingly, policymakers have devoted significant attention to SARS-CoV-2’s impact on adult hospitals. Less consideration, however, has been given to children’s hospitals, which responded to the pandemic by suspending noncritical care encounters, conserving personal protective equipment, and implementing alternative care models.¹ While important, these strategic decisions may threaten the financial health of children’s hospitals.

In this issue of the Journal of Hospital Medicine, Synhorst et al¹ describe the impact of COVID-19 on US children’s hospitals.The authors utilized the Children’s Hospital Association’s PROSPECT database to compare year-over-year trends in healthcare encounters and hospital charges before and during the COVID-19 pandemic at 26 tertiary hospitals. The analysis focused on the first wave of COVID-19 in the United States from February through June 2020.

The results are staggering. Compared with 2019, the authors found significant decreases in healthcare encounters for all children’s hospitals beginning in March 2020, with a nadir in mid-April (corresponding to the first peak in adult hospitalizations). Inpatient bed days, emergency department (ED) visits, and surgeries decreased by a median of 36%, 65%, and 77%, respectively, per hospital during the nadir. Charges from February 1 to June 30, 2020, decreased by a median 24% per children’s hospital as compared to 2019—corresponding to a median $276 million decrease in charges per hospital. A quarter of hospitals faced more than $400 million in lost charges.¹

Why do these trends matter? Large decreases in utilization and associated charges likely represent significant unmet demand for child healthcare for both acute and chronic disease management. For example, with limited in-person evaluation available at the onset of illness, caregivers are presenting to EDs with sicker children.² With a shift to virtual care, clinicians may miss signs of child abuse from violence in the home—which can escalate during isolation.³ Children with chronic conditions may also be left without surveillance mechanisms, which may partly explain the autumn 2020 surge in acute mental health-related ED presentations.⁴ Furthermore, telemedicine may exacerbate care inequities for vulnerable populations lacking resources and/or English proficiency.

There is also a larger policy perspective to consider in evaluating these data: Because children’s hospitals largely operate in a fee-for-service reimbursement model, they often rely on marginal revenues to support mission-driven programming. In other words, revenue streams from profitable care segments (eg, elective surgeries) often help sustain institutional platforms operating at a loss, such as community safety net programs. Consequently, threats to marginal revenues can place mission-driven programming in jeopardy of being reduced or terminated.

The Synhorst et al¹ study was limited to hospital charges, which likely overestimate revenue losses based on actual reimbursements. Yet, this is the first study to quantify COVID-19’s financial toll on children’s hospitals, and charges offer a reasonable proxy for balance sheet trends. Thus, it is safe to assume that most hospitals incurred substantial losses during the 2020 fiscal year. Unfortunately, as the authors highlight, these losses differentially impacted hospitals based on existing resources¹—so some hospitals were likely forced to cut programs or reduce staff in an effort to return to profitability. In this way, COVID-19 has exposed the fragility of the fee-for-service model that children’s hospitals rely on for both patients and staff.

Children’s hospitals and the services they provide are essential to the health and well-being of children. The critical losses sustained by children’s hospitals due to COVID-19 threaten their ability to promote child health in the near and long term, with the greatest risk to vulnerable populations. Policymakers must act now to preserve these essential services for children.

Since the onset of the COVID-19 pandemic, the proclivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for adults and its relative sparing of pediatric populations has been well characterized. Accordingly, policymakers have devoted significant attention to SARS-CoV-2’s impact on adult hospitals. Less consideration, however, has been given to children’s hospitals, which responded to the pandemic by suspending noncritical care encounters, conserving personal protective equipment, and implementing alternative care models.¹ While important, these strategic decisions may threaten the financial health of children’s hospitals.

In this issue of the Journal of Hospital Medicine, Synhorst et al¹ describe the impact of COVID-19 on US children’s hospitals.The authors utilized the Children’s Hospital Association’s PROSPECT database to compare year-over-year trends in healthcare encounters and hospital charges before and during the COVID-19 pandemic at 26 tertiary hospitals. The analysis focused on the first wave of COVID-19 in the United States from February through June 2020.

The results are staggering. Compared with 2019, the authors found significant decreases in healthcare encounters for all children’s hospitals beginning in March 2020, with a nadir in mid-April (corresponding to the first peak in adult hospitalizations). Inpatient bed days, emergency department (ED) visits, and surgeries decreased by a median of 36%, 65%, and 77%, respectively, per hospital during the nadir. Charges from February 1 to June 30, 2020, decreased by a median 24% per children’s hospital as compared to 2019—corresponding to a median $276 million decrease in charges per hospital. A quarter of hospitals faced more than $400 million in lost charges.¹

Why do these trends matter? Large decreases in utilization and associated charges likely represent significant unmet demand for child healthcare for both acute and chronic disease management. For example, with limited in-person evaluation available at the onset of illness, caregivers are presenting to EDs with sicker children.² With a shift to virtual care, clinicians may miss signs of child abuse from violence in the home—which can escalate during isolation.³ Children with chronic conditions may also be left without surveillance mechanisms, which may partly explain the autumn 2020 surge in acute mental health-related ED presentations.⁴ Furthermore, telemedicine may exacerbate care inequities for vulnerable populations lacking resources and/or English proficiency.

There is also a larger policy perspective to consider in evaluating these data: Because children’s hospitals largely operate in a fee-for-service reimbursement model, they often rely on marginal revenues to support mission-driven programming. In other words, revenue streams from profitable care segments (eg, elective surgeries) often help sustain institutional platforms operating at a loss, such as community safety net programs. Consequently, threats to marginal revenues can place mission-driven programming in jeopardy of being reduced or terminated.

The Synhorst et al¹ study was limited to hospital charges, which likely overestimate revenue losses based on actual reimbursements. Yet, this is the first study to quantify COVID-19’s financial toll on children’s hospitals, and charges offer a reasonable proxy for balance sheet trends. Thus, it is safe to assume that most hospitals incurred substantial losses during the 2020 fiscal year. Unfortunately, as the authors highlight, these losses differentially impacted hospitals based on existing resources¹—so some hospitals were likely forced to cut programs or reduce staff in an effort to return to profitability. In this way, COVID-19 has exposed the fragility of the fee-for-service model that children’s hospitals rely on for both patients and staff.

Children’s hospitals and the services they provide are essential to the health and well-being of children. The critical losses sustained by children’s hospitals due to COVID-19 threaten their ability to promote child health in the near and long term, with the greatest risk to vulnerable populations. Policymakers must act now to preserve these essential services for children.

Since the onset of the COVID-19 pandemic, the proclivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for adults and its relative sparing of pediatric populations has been well characterized. Accordingly, policymakers have devoted significant attention to SARS-CoV-2’s impact on adult hospitals. Less consideration, however, has been given to children’s hospitals, which responded to the pandemic by suspending noncritical care encounters, conserving personal protective equipment, and implementing alternative care models.¹ While important, these strategic decisions may threaten the financial health of children’s hospitals.

In this issue of the Journal of Hospital Medicine, Synhorst et al¹ describe the impact of COVID-19 on US children’s hospitals.The authors utilized the Children’s Hospital Association’s PROSPECT database to compare year-over-year trends in healthcare encounters and hospital charges before and during the COVID-19 pandemic at 26 tertiary hospitals. The analysis focused on the first wave of COVID-19 in the United States from February through June 2020.

The results are staggering. Compared with 2019, the authors found significant decreases in healthcare encounters for all children’s hospitals beginning in March 2020, with a nadir in mid-April (corresponding to the first peak in adult hospitalizations). Inpatient bed days, emergency department (ED) visits, and surgeries decreased by a median of 36%, 65%, and 77%, respectively, per hospital during the nadir. Charges from February 1 to June 30, 2020, decreased by a median 24% per children’s hospital as compared to 2019—corresponding to a median $276 million decrease in charges per hospital. A quarter of hospitals faced more than $400 million in lost charges.¹

Why do these trends matter? Large decreases in utilization and associated charges likely represent significant unmet demand for child healthcare for both acute and chronic disease management. For example, with limited in-person evaluation available at the onset of illness, caregivers are presenting to EDs with sicker children.² With a shift to virtual care, clinicians may miss signs of child abuse from violence in the home—which can escalate during isolation.³ Children with chronic conditions may also be left without surveillance mechanisms, which may partly explain the autumn 2020 surge in acute mental health-related ED presentations.⁴ Furthermore, telemedicine may exacerbate care inequities for vulnerable populations lacking resources and/or English proficiency.

There is also a larger policy perspective to consider in evaluating these data: Because children’s hospitals largely operate in a fee-for-service reimbursement model, they often rely on marginal revenues to support mission-driven programming. In other words, revenue streams from profitable care segments (eg, elective surgeries) often help sustain institutional platforms operating at a loss, such as community safety net programs. Consequently, threats to marginal revenues can place mission-driven programming in jeopardy of being reduced or terminated.

The Synhorst et al¹ study was limited to hospital charges, which likely overestimate revenue losses based on actual reimbursements. Yet, this is the first study to quantify COVID-19’s financial toll on children’s hospitals, and charges offer a reasonable proxy for balance sheet trends. Thus, it is safe to assume that most hospitals incurred substantial losses during the 2020 fiscal year. Unfortunately, as the authors highlight, these losses differentially impacted hospitals based on existing resources¹—so some hospitals were likely forced to cut programs or reduce staff in an effort to return to profitability. In this way, COVID-19 has exposed the fragility of the fee-for-service model that children’s hospitals rely on for both patients and staff.

Children’s hospitals and the services they provide are essential to the health and well-being of children. The critical losses sustained by children’s hospitals due to COVID-19 threaten their ability to promote child health in the near and long term, with the greatest risk to vulnerable populations. Policymakers must act now to preserve these essential services for children.

Continuous pulse oximetry monitoring (cSpO₂) in children with bronchiolitis is associated with increased rates of hospital admission, longer lengths of stay, more frequent treatment with supplemental oxygen, alarm fatigue, and higher hospital cost. There is no evidence that it improves clinical outcomes.^1-7 The safety of reducing cSpO₂ for stable bronchiolitis patients (ie, those who are clinically well and not requiring supplemental oxygen) has been assessed in quality improvement initiatives^8-10 and a randomized controlled trial.² These studies showed no increase in intensive care unit transfers, codes, or readmissions associated with reduced cSpO₂. Current national guidelines from the American Academy of Pediatrics⁵ and the Society of Hospital Medicine Choosing Wisely in Pediatric Hospital Medicine workgroup⁴ support limiting monitoring of children with bronchiolitis. Despite this, the practice of cSpO₂ in stable bronchiolitis patients off supplemental oxygen remains widespread.^11,12

Deimplementation, defined as reducing or stopping low-value or ineffective healthcare practices,^13,14 is a discrete focus area within implementation science. Deimplementation research involves the reduction of unnecessary and overused services for which there is potential for harm or no benefit.^15,16 In pediatrics, there are a number of potential targets for deimplementation,^4,17-20 including cSpO₂ for stable infants with bronchiolitis, but efforts to reduce low-value practices have met limited success to date. ^21,22

Implementation science offers rigorous methods for advancing the development and evaluation of strategies for deimplementation.²³ In particular, implementation science frameworks can facilitate our understanding of relevant contextual factors that may hinder or help efforts to deimplement low-value practices. To develop broadly applicable strategies to reduce monitoring overuse, it is important to understand the barriers, facilitators, and contextual factors (eg, clinical, political, interpersonal) that contribute to guideline-discordant cSpO₂ in hospitalized bronchiolitis patients. Further, the process by which one can develop a rigorous understanding of these factors and how they may impact deimplementation efforts could generalize to other scenarios in pediatrics where overuse remains an issue.

The goal of this study was to use semistructured interviews, informed by an established implementation science framework, specifically the Consolidated Framework for Implementation Research (CFIR),²⁴ to (1) identify barriers and facilitators to deimplementing unnecessary cSpO₂, and (2) develop strategies to deimplement cSpO₂ in a multicenter cohort of hospital-based clinician and administrative stakeholders.

Study Setting

This multicenter qualitative study using semistructured interviews took place within the Eliminating Monitor Overuse (EMO) SpO₂ study. The EMO SpO₂ study established rates of cSpO₂ in bronchiolitis patients not receiving supplemental oxygen or not receiving room air flow at 56 hospitals across the United States and in Canada from December 1, 2018, through March 31, 2019.¹² The study identified hospital-level risk-adjusted cSpO₂ rates ranging from 6% to 82%. A description of the EMO SpO₂ study methods²⁵ and its findings¹² have been published elsewhere.

Participants

We approached EMO study site principal investigators at 12 hospitals: the two highest- and two lowest-use hospitals within three hospital types (ie, freestanding children’s hospitals, children’s hospitals within large general hospitals, and community hospitals). We collaborated with the participating site principal investigators (n = 12), who were primarily hospitalist physicians in leadership roles, to recruit a purposive sample of additional stakeholders including bedside nurses (n = 12), hospitalist physicians (n = 15), respiratory therapists (n = 9), and hospital administrators (n = 8) to participate in semistructured interviews. Interviews were conducted until we achieved thematic saturation within each stakeholder group and within the high and low performing strata (total 56 interviews). Participants were asked to self-report basic demographic information (see Appendix, interview guide) as required by the study funder and to allow us to comment on the representativeness of the participant group.

Procedure

The interview guide was informed by the CFIR, a comprehensive framework detailing contextual factors that require consideration when planning for the implementation of a health service intervention. Table 1 details the CFIR domains with study-related examples. The interview guide (Appendix) provided limited clinical context apart from the age, diagnosis, and oxygen requirement for the population of interest to promote a broad array of responses and to avoid anchoring on specific clinical scenarios. Interviews were conducted by master’s degree or doctoral-level research coordinators with qualitative interviewing experience and supervised by a medical anthropologist and qualitative methods expert (F.K.B.). Prior to engaging in audio recorded phone interviews, the interviewer explained the risks and benefits of participating. Participants were compensated $50. Audio recordings were transcribed, deidentified, and uploaded to NVivo 12 Plus (QSR International) for data management.

The Institutional Review Boards of Children’s Hospital of Philadelphia, Pennsylvania, and the University of Pennsylvania in Philadelphia determined that the study met eligibility criteria for IRB exemption.

Data Analysis

Using an integrated approach to codebook development,²⁶ a priori codes were developed using constructs from the CFIR. Additional codes were added by the research team following a close reading of the first five transcripts.^27,28 Each code was defined, including decision rules for its application. Two research coordinators independently coded each transcript. Using the intercoder reliability function within NVivo, the coders established strong interrater reliability accordance scores (k > .8) by double coding 20% of the transcripts. Data were stratified by sites with low and high use of cSpO₂ to examine differences in barriers and facilitators to deimplementation. Each code was subcoded, summarized, and examined for patterns within and across participating disciplines, which yielded themes related to barriers and facilitators. We conducted member checking and reviewed our conclusions with a multidisciplinary group of clinical stakeholders (n = 13) to validate our analyses.

Barriers and facilitators to deimplementation were identified in multiple domains of the CFIR: outer setting, inner setting, characteristics of the individuals, and intervention characteristics (Table 1). Participants also suggested strategies to facilitate deimplementation in response to some identified barriers. See Table 2 for participant demographics and Table 3 for illustrative participant quotations.

Barriers

Outer Setting: Clinician Perceptions of Parental Discomfort With Discontinuing Monitoring

Participants mentioned parental preferences as a barrier to discontinuing cSpO₂, noting that parents seem to take comfort in watching the numbers on the monitor screen and are reluctant to have it withdrawn. Clinicians noted that parents sometimes put the monitor back on their child after a clinician removed it or have expressed concern that their unmonitored child was not receiving the same level of care as other patients who were being monitored. In these scenarios, clinicians reported they have found it helpful to educate caregivers about when cSpO₂ is and is not appropriate.

Inner Setting: Unclear or Nonexistent Guideline to Discontinue cSpO₂

Guidelines to discontinue cSpO₂ reportedly did not exist at all institutions. If a guideline did exist, lack of clarity or conflicting guidelines about when to use oxygen presented a barrier. Participants suggested that a clear guideline or additional oversight to ensure all clinicians are informed of the procedure for discontinuing cSpO₂ may help prevent miscommunication. Participants noted that their electronic health record (EHR) order sets commonly included cSpO₂ orders and that removing that option would facilitate deimplementation.

Inner Setting: Difficulty Educating All Staff

Participants noted difficulty with incorporating education about discontinuing cSpO₂ to all clinicians, particularly to those who are nightshift only or to rotating staff or trainees. This created barriers for frequent re-education because these staff are not familiar with the policies and procedures of the unit, which is crucial to developing a culture that supports the deimplementation of cSpO₂. Participants suggested that recurring education about procedures for discontinuing cSpO₂ should target trainees, new nurses, and overnight nurses. This would help to ensure that the guideline is uniformly followed.

Inner Setting: Culture of High cSpO₂ Use

Participants from high-use sites discussed a culture driven by readily available monitoring features or an expectation that monitoring indicates higher-quality care. Participants from low-use sites discussed increased cSpO₂ driven by clinicians who were accustomed to caring for higher-acuity patients, for whom continuous monitoring is likely appropriate, and were simultaneously caring for stable bronchiolitis patients.

Some suggested that visual cues would be useful to clinicians to sustain awareness about a cSpO₂ deimplementation guideline. It was also suggested that audit and feedback techniques like posting unit deimplementation statistics and creating a competition among units by posting unit performance could facilitate deimplementation. Additionally, some noted that visual aids in common spaces would be useful to remind clinicians and to engage caregivers about discontinuing cSpO₂.

Characteristics of Individuals: Clinician Discomfort Discontinuing cSpO₂

One frequently cited barrier across participants is that cSpO₂ provides “peace of mind” to alert clinicians to patients with low oxygen saturations that might otherwise be missed. Participants identified that clinician discomfort with reducing cSpO₂ may be driven by inexperienced clinicians less familiar with the bronchiolitis disease process, such as trainees, new nurses, or rotating clinicians unaccustomed to pediatric care. Trainees and new nurses were perceived as being more likely to work at night when there are fewer clinicians to provide patient care. Additionally, participants perceived that night shift clinicians favored cSpO₂ because they could measure vital signs without waking patients and families.

Clinicians discussed that discontinuing cSpO₂ would require alternative methods for assessing patient status, particularly for night shift nurses. Participants suggested strategies including changes to pulse oximetry assessment procedures to include more frequent “spot checks,” incorporation of assessments during sleep events (eg, naps) to ensure the patient does not experience desaturations during sleep, and training nurses to become more comfortable with suctioning patients. Suggestions also included education on the typical features of transient oxygen desaturations in otherwise stable patients with bronchiolitis² to bolster clinical confidence for clinicians unfamiliar with caring for bronchiolitis patients. Participants perceived that education about appropriate vs inappropriate use may help to empower clinicians to employ cSpO₂ appropriately.

Facilitators

Outer Setting: Standards and Evidence From Research, Professional Organizations, and Leaders in the Field

Many participants expressed the importance of consistent guidelines that are advocated by thought leaders in the field, supported by robust evidence, and consistent with approaches at peer hospitals. The more authoritative support a guideline has, the more comfortable people are adopting it and taking it seriously. Additionally, consistent education about guidelines was desired. Participants noted that all clinicians should be receiving education related to the American Academy of Pediatrics (AAP) Bronchiolitis and Choosing Wisely^® guidelines, ranging from a one-time update to annually. Continual updates and re-education sessions for clinicians who shared evidence about how cSpO₂ deimplementation could improve the quality of patient care by shortening hospital length of stay and lowering cost were suggested strategies.

Inner Setting: Leadership

Participants noted that successful deimplementation depends upon the presence of a champion or educator who will be able to lead the institutional charge in making practice change. This is typically an individual who is trusted at the institution, experienced in their field, or already doing implementation work. This could be either a single individual (champion) or a team. The most commonly noted clinician roles to engage in a leadership role or team were physicians and nurses.

Participants noted that a change in related clinical care pathways or EHR order sets would require cooperation from multiple clinical disciplines, administrators, and information technology leaders and explained that messaging and education about the value of the change would facilitate buy-in from those clinicians.

Inner Setting: EHR Support for Guidelines

Participants often endorsed the use of an order set within the EHR that supports guidelines and includes reminders to decrease cSpO₂. These reminders could come up when supplemental oxygen is discontinued or occur regularly throughout the patient’s stay to prompt the clinician to consider discontinuing cSpO₂.

Intervention Characteristics/Inner Setting: Clear Bronchiolitis Guidelines

The presence of a well-articulated hospital policy that delineates the appropriate and inappropriate use of cSpO₂ in bronchiolitis was mentioned as another facilitator of deimplementation.

Results of this qualitative study of stakeholders across hospitals with high and low cSpO₂ use illustrated the complexities involved with deimplementation of cSpO₂ in pediatric patients hospitalized with bronchiolitis. We identified numerous barriers spanning the CFIR constructs, including unclear or absent guidelines for stopping cSpO₂, clinician knowledge and comfort with bronchiolitis disease features, and unit culture. This suggests that multicomponent strategies that target various domains and a variety of stakeholders are needed to deimplement cSpO₂ use for stable bronchiolitis patients. Participants also identified facilitators, including clear cSpO₂ guidelines, supportive leaders and champions, and EHR modifications, that provide insight into strategies that may help sites reduce their use of cSpO₂. Additionally, participants also provided concrete, actionable suggestions for ways to reduce unnecessary monitoring that will be useful in informing promising deimplementation strategies for subsequent trials.

The importance of having specific and well-known guidelines from trusted sources, such as the AAP, about cSpO₂ and bronchiolitis treatment that are thoughtfully integrated in the EHR came through in multiple themes of our analysis. Prior studies on the effect of guidelines on clinical practice have suggested that rigorously designed guidelines can positively impact practice.²⁹ Participants also noted that cSpO₂ guidelines should be authoritative and that knowledge of guideline adoption by peer institutions was a facilitator of adoption. Usability issues negatively impact clinicians’ ability to follow guidelines.³⁰ Further, prior studies have demonstrated that EHR integration of guidelines can change practice.^31-33 Based on our findings, incorporating clear guidelines into commonly used formats, such as EHR order sets, could be an important deimplementation tool for cSpO₂ in stable bronchiolitis patients.

Education about and awareness of cSpO₂ guidelines was described as an important facilitator for appropriate cSpO₂ use and was suggested as a potential deimplementation strategy. Participants noted that educational need may vary by stakeholder group. For example, education may facilitate obtaining buy-in from hospital leaders, which is necessary to support changes to the EHR. Education incorporating information on the typical features of bronchiolitis and examples of appropriate and inappropriate cSpO₂ use was suggested for clinical team members. The limitations of education as a stand-alone deimplementation strategy were also noted, and participants highlighted challenges such as time needed for education and the need for ongoing education for rotating trainees. Inner and outer setting barriers, such as a perceived “culture of high pulse oximetryuse” and patient and family expectations, could also make education less effective as a stand-alone strategy. That—coupled with evidence that education and training alone are generally insufficient for producing reliable, sustained behavior change^34,35—suggests that a multifaceted approach will be important.

Our respondents consider parental perceptions and preference in their practice, which provides nuance to recent studies suggesting that parents prefer continuous monitors when their child is hospitalized with bronchiolitis. Chi et al described the impact of a brief educational intervention on parental preferences for monitoring children hospitalized for bronchiolitis.³⁶ This work suggests that educational interventions aimed at families should be considered in future (de)implementation studies because they may indirectly impact clinician behavior. Future studies should directly assess parental discomfort with discontinuing monitoring. Participants highlighted the link between knowledge and confidence in caring for typical bronchiolitis patients and monitoring practice, perceiving that less experienced clinicians are more likely to rely on cSpO₂. Participants at high-use sites emphasized the expectation that monitoring should occur during hospitalizations. This reflection is particularly pertinent for bronchiolitis, a disease characterized by frequent, self-resolving desaturations even after hospital discharge.³ This may reinforce a perceived need to capture and react to these desaturation events even though they are expected in bronchiolitis and can occur in healthy infants.³⁷ Some participants suggested that continuous monitoring be replaced with “nap tests” (ie, assessment for desaturations during a nap prior to discharge); however, like cSpO₂ in stable infants with bronchiolitis, this is another low-value practice. Otherwise healthy infants with mild to moderate disease are unlikely to subsequently worsen after showing signs of clinical improvement.³⁸ Nap tests are likely to lead to infants who are clinically improving being placed unnecessarily back on oxygen in reaction to the transient desaturations. Participants’ perception about the importance of cSpO₂ in bronchiolitis management, despite evidence suggesting it is a low-value practice, underscores the importance of not simply telling clinicians to stop cSpO₂. Employing strategies that replace continuous monitoring with another acceptable and feasible alternative (eg, regular clinician assessments including intermittent pulse oximetry checks) should be considered when planning for deimplementation.³⁹

Previous studies indicate that continuous monitoring can affect clinician decision-making, independent of other factors,^6,40 despite limited evidence that continuous monitors improve patient outcomes.^1-7 Studies have demonstrated noticeable increase in admissions based purely on pulse oximetry values,⁴⁰ with no evidence that this type of admission changes outcomes for bronchiolitis patients.⁶ One previous, single-center study identified inexperience as a potential driver for monitor use,⁴¹ and studies in adult populations have suggested that clinicians overestimate the value that continuous monitoring contributes to patient care,^42,43 which promotes guideline-discordant use. Our study provides novel insight into the issue of monitoring in bronchiolitis. Our results suggest that there is a need to shift organizational cultures around monitoring (which likely vary based on a range of factors) and that educational strategies addressing typical disease course, especially desaturations, in bronchiolitis will be an essential component in any deimplementation effort.

This study is strengthened by its sample of diverse stakeholder groups from multiple US health systems. Additionally, we interviewed individuals at sites with high cSpO₂ rates and at sites with low rates, as well as from community hospitals, children’s hospitals within general hospitals, and freestanding children’s hospitals, which allows us to understand barriers high-use sites encounter and facilitators of lower cSpO₂ rates at low-use sites. We also employed an interview approach informed by an established implementation science framework. Nonetheless, several limitations exist. First, participants at low-use sites did not necessarily have direct experience with a previous deimplementation effort to reduce cSpO₂. Additionally, participants were predominantly White and female; more diverse perspectives would strengthen confidence in the generalizability of our findings. While thematic saturation was achieved within each stakeholder group and within the high- and low-use strata, we interviewed fewer administrators and respiratory therapists relative to other stakeholder groups. Nevertheless, our conclusions were validated by our interdisciplinary stakeholder panel. As noted by participants, family preferences may influence clinician practice, and parents were not interviewed for this study. The information gleaned from the present study will inform the development of strategies to deimplement unnecessary cSpO₂ in pediatric hospitals, which we aim to rigorously evaluate in a future trial.

We identified barriers and facilitators to deimplementation of cSpO₂ for stable patients with bronchiolitis across children’s hospitals with high and low utilization of cSpO₂. These themes map to multiple CFIR domains and, along with participant-suggested strategies, can directly inform an approach to cSpO₂ deimplementation in a range of inpatient settings. Based on these data, future deimplementation efforts should focus on clear protocols for use and discontinuation of cSpO₂, EHR changes, and regular bronchiolitis education for hospital staff that emphasizes reducing unnecessary cSpO₂ utilization.

We acknowledge the NHLBI scientists who contributed their expertise to this project as part of the U01 Cooperative Agreement funding mechanism as federal employees conducting their official job duties: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD. We thank the Executive Council of the Pediatric Research in Inpatient Settings (PRIS) Network for their contributions to the early scientific development of this project. The Network assessed a Collaborative Support Fee for access to the hospitals and support of this project. We thank the PRIS Network collaborators for their major contributions to data collection measuring utilization to identify the hospitals we subsequently chose for this project. We thank Claire Bocage and the Mixed Methods Research Lab for major help in data management and data analysis.

Continuous pulse oximetry monitoring (cSpO₂) in children with bronchiolitis is associated with increased rates of hospital admission, longer lengths of stay, more frequent treatment with supplemental oxygen, alarm fatigue, and higher hospital cost. There is no evidence that it improves clinical outcomes.^1-7 The safety of reducing cSpO₂ for stable bronchiolitis patients (ie, those who are clinically well and not requiring supplemental oxygen) has been assessed in quality improvement initiatives^8-10 and a randomized controlled trial.² These studies showed no increase in intensive care unit transfers, codes, or readmissions associated with reduced cSpO₂. Current national guidelines from the American Academy of Pediatrics⁵ and the Society of Hospital Medicine Choosing Wisely in Pediatric Hospital Medicine workgroup⁴ support limiting monitoring of children with bronchiolitis. Despite this, the practice of cSpO₂ in stable bronchiolitis patients off supplemental oxygen remains widespread.^11,12

Deimplementation, defined as reducing or stopping low-value or ineffective healthcare practices,^13,14 is a discrete focus area within implementation science. Deimplementation research involves the reduction of unnecessary and overused services for which there is potential for harm or no benefit.^15,16 In pediatrics, there are a number of potential targets for deimplementation,^4,17-20 including cSpO₂ for stable infants with bronchiolitis, but efforts to reduce low-value practices have met limited success to date. ^21,22

Implementation science offers rigorous methods for advancing the development and evaluation of strategies for deimplementation.²³ In particular, implementation science frameworks can facilitate our understanding of relevant contextual factors that may hinder or help efforts to deimplement low-value practices. To develop broadly applicable strategies to reduce monitoring overuse, it is important to understand the barriers, facilitators, and contextual factors (eg, clinical, political, interpersonal) that contribute to guideline-discordant cSpO₂ in hospitalized bronchiolitis patients. Further, the process by which one can develop a rigorous understanding of these factors and how they may impact deimplementation efforts could generalize to other scenarios in pediatrics where overuse remains an issue.

The goal of this study was to use semistructured interviews, informed by an established implementation science framework, specifically the Consolidated Framework for Implementation Research (CFIR),²⁴ to (1) identify barriers and facilitators to deimplementing unnecessary cSpO₂, and (2) develop strategies to deimplement cSpO₂ in a multicenter cohort of hospital-based clinician and administrative stakeholders.

Study Setting

This multicenter qualitative study using semistructured interviews took place within the Eliminating Monitor Overuse (EMO) SpO₂ study. The EMO SpO₂ study established rates of cSpO₂ in bronchiolitis patients not receiving supplemental oxygen or not receiving room air flow at 56 hospitals across the United States and in Canada from December 1, 2018, through March 31, 2019.¹² The study identified hospital-level risk-adjusted cSpO₂ rates ranging from 6% to 82%. A description of the EMO SpO₂ study methods²⁵ and its findings¹² have been published elsewhere.

Participants

We approached EMO study site principal investigators at 12 hospitals: the two highest- and two lowest-use hospitals within three hospital types (ie, freestanding children’s hospitals, children’s hospitals within large general hospitals, and community hospitals). We collaborated with the participating site principal investigators (n = 12), who were primarily hospitalist physicians in leadership roles, to recruit a purposive sample of additional stakeholders including bedside nurses (n = 12), hospitalist physicians (n = 15), respiratory therapists (n = 9), and hospital administrators (n = 8) to participate in semistructured interviews. Interviews were conducted until we achieved thematic saturation within each stakeholder group and within the high and low performing strata (total 56 interviews). Participants were asked to self-report basic demographic information (see Appendix, interview guide) as required by the study funder and to allow us to comment on the representativeness of the participant group.

Procedure

The interview guide was informed by the CFIR, a comprehensive framework detailing contextual factors that require consideration when planning for the implementation of a health service intervention. Table 1 details the CFIR domains with study-related examples. The interview guide (Appendix) provided limited clinical context apart from the age, diagnosis, and oxygen requirement for the population of interest to promote a broad array of responses and to avoid anchoring on specific clinical scenarios. Interviews were conducted by master’s degree or doctoral-level research coordinators with qualitative interviewing experience and supervised by a medical anthropologist and qualitative methods expert (F.K.B.). Prior to engaging in audio recorded phone interviews, the interviewer explained the risks and benefits of participating. Participants were compensated $50. Audio recordings were transcribed, deidentified, and uploaded to NVivo 12 Plus (QSR International) for data management.

The Institutional Review Boards of Children’s Hospital of Philadelphia, Pennsylvania, and the University of Pennsylvania in Philadelphia determined that the study met eligibility criteria for IRB exemption.

Data Analysis

Using an integrated approach to codebook development,²⁶ a priori codes were developed using constructs from the CFIR. Additional codes were added by the research team following a close reading of the first five transcripts.^27,28 Each code was defined, including decision rules for its application. Two research coordinators independently coded each transcript. Using the intercoder reliability function within NVivo, the coders established strong interrater reliability accordance scores (k > .8) by double coding 20% of the transcripts. Data were stratified by sites with low and high use of cSpO₂ to examine differences in barriers and facilitators to deimplementation. Each code was subcoded, summarized, and examined for patterns within and across participating disciplines, which yielded themes related to barriers and facilitators. We conducted member checking and reviewed our conclusions with a multidisciplinary group of clinical stakeholders (n = 13) to validate our analyses.

Barriers and facilitators to deimplementation were identified in multiple domains of the CFIR: outer setting, inner setting, characteristics of the individuals, and intervention characteristics (Table 1). Participants also suggested strategies to facilitate deimplementation in response to some identified barriers. See Table 2 for participant demographics and Table 3 for illustrative participant quotations.

Barriers

Outer Setting: Clinician Perceptions of Parental Discomfort With Discontinuing Monitoring

Participants mentioned parental preferences as a barrier to discontinuing cSpO₂, noting that parents seem to take comfort in watching the numbers on the monitor screen and are reluctant to have it withdrawn. Clinicians noted that parents sometimes put the monitor back on their child after a clinician removed it or have expressed concern that their unmonitored child was not receiving the same level of care as other patients who were being monitored. In these scenarios, clinicians reported they have found it helpful to educate caregivers about when cSpO₂ is and is not appropriate.

Inner Setting: Unclear or Nonexistent Guideline to Discontinue cSpO₂

Guidelines to discontinue cSpO₂ reportedly did not exist at all institutions. If a guideline did exist, lack of clarity or conflicting guidelines about when to use oxygen presented a barrier. Participants suggested that a clear guideline or additional oversight to ensure all clinicians are informed of the procedure for discontinuing cSpO₂ may help prevent miscommunication. Participants noted that their electronic health record (EHR) order sets commonly included cSpO₂ orders and that removing that option would facilitate deimplementation.

Inner Setting: Difficulty Educating All Staff

Participants noted difficulty with incorporating education about discontinuing cSpO₂ to all clinicians, particularly to those who are nightshift only or to rotating staff or trainees. This created barriers for frequent re-education because these staff are not familiar with the policies and procedures of the unit, which is crucial to developing a culture that supports the deimplementation of cSpO₂. Participants suggested that recurring education about procedures for discontinuing cSpO₂ should target trainees, new nurses, and overnight nurses. This would help to ensure that the guideline is uniformly followed.

Inner Setting: Culture of High cSpO₂ Use

Participants from high-use sites discussed a culture driven by readily available monitoring features or an expectation that monitoring indicates higher-quality care. Participants from low-use sites discussed increased cSpO₂ driven by clinicians who were accustomed to caring for higher-acuity patients, for whom continuous monitoring is likely appropriate, and were simultaneously caring for stable bronchiolitis patients.

Some suggested that visual cues would be useful to clinicians to sustain awareness about a cSpO₂ deimplementation guideline. It was also suggested that audit and feedback techniques like posting unit deimplementation statistics and creating a competition among units by posting unit performance could facilitate deimplementation. Additionally, some noted that visual aids in common spaces would be useful to remind clinicians and to engage caregivers about discontinuing cSpO₂.

Characteristics of Individuals: Clinician Discomfort Discontinuing cSpO₂

One frequently cited barrier across participants is that cSpO₂ provides “peace of mind” to alert clinicians to patients with low oxygen saturations that might otherwise be missed. Participants identified that clinician discomfort with reducing cSpO₂ may be driven by inexperienced clinicians less familiar with the bronchiolitis disease process, such as trainees, new nurses, or rotating clinicians unaccustomed to pediatric care. Trainees and new nurses were perceived as being more likely to work at night when there are fewer clinicians to provide patient care. Additionally, participants perceived that night shift clinicians favored cSpO₂ because they could measure vital signs without waking patients and families.

Clinicians discussed that discontinuing cSpO₂ would require alternative methods for assessing patient status, particularly for night shift nurses. Participants suggested strategies including changes to pulse oximetry assessment procedures to include more frequent “spot checks,” incorporation of assessments during sleep events (eg, naps) to ensure the patient does not experience desaturations during sleep, and training nurses to become more comfortable with suctioning patients. Suggestions also included education on the typical features of transient oxygen desaturations in otherwise stable patients with bronchiolitis² to bolster clinical confidence for clinicians unfamiliar with caring for bronchiolitis patients. Participants perceived that education about appropriate vs inappropriate use may help to empower clinicians to employ cSpO₂ appropriately.

Facilitators

Outer Setting: Standards and Evidence From Research, Professional Organizations, and Leaders in the Field

Many participants expressed the importance of consistent guidelines that are advocated by thought leaders in the field, supported by robust evidence, and consistent with approaches at peer hospitals. The more authoritative support a guideline has, the more comfortable people are adopting it and taking it seriously. Additionally, consistent education about guidelines was desired. Participants noted that all clinicians should be receiving education related to the American Academy of Pediatrics (AAP) Bronchiolitis and Choosing Wisely^® guidelines, ranging from a one-time update to annually. Continual updates and re-education sessions for clinicians who shared evidence about how cSpO₂ deimplementation could improve the quality of patient care by shortening hospital length of stay and lowering cost were suggested strategies.

Inner Setting: Leadership

Participants noted that successful deimplementation depends upon the presence of a champion or educator who will be able to lead the institutional charge in making practice change. This is typically an individual who is trusted at the institution, experienced in their field, or already doing implementation work. This could be either a single individual (champion) or a team. The most commonly noted clinician roles to engage in a leadership role or team were physicians and nurses.

Participants noted that a change in related clinical care pathways or EHR order sets would require cooperation from multiple clinical disciplines, administrators, and information technology leaders and explained that messaging and education about the value of the change would facilitate buy-in from those clinicians.

Inner Setting: EHR Support for Guidelines

Participants often endorsed the use of an order set within the EHR that supports guidelines and includes reminders to decrease cSpO₂. These reminders could come up when supplemental oxygen is discontinued or occur regularly throughout the patient’s stay to prompt the clinician to consider discontinuing cSpO₂.

Intervention Characteristics/Inner Setting: Clear Bronchiolitis Guidelines

The presence of a well-articulated hospital policy that delineates the appropriate and inappropriate use of cSpO₂ in bronchiolitis was mentioned as another facilitator of deimplementation.

Results of this qualitative study of stakeholders across hospitals with high and low cSpO₂ use illustrated the complexities involved with deimplementation of cSpO₂ in pediatric patients hospitalized with bronchiolitis. We identified numerous barriers spanning the CFIR constructs, including unclear or absent guidelines for stopping cSpO₂, clinician knowledge and comfort with bronchiolitis disease features, and unit culture. This suggests that multicomponent strategies that target various domains and a variety of stakeholders are needed to deimplement cSpO₂ use for stable bronchiolitis patients. Participants also identified facilitators, including clear cSpO₂ guidelines, supportive leaders and champions, and EHR modifications, that provide insight into strategies that may help sites reduce their use of cSpO₂. Additionally, participants also provided concrete, actionable suggestions for ways to reduce unnecessary monitoring that will be useful in informing promising deimplementation strategies for subsequent trials.

The importance of having specific and well-known guidelines from trusted sources, such as the AAP, about cSpO₂ and bronchiolitis treatment that are thoughtfully integrated in the EHR came through in multiple themes of our analysis. Prior studies on the effect of guidelines on clinical practice have suggested that rigorously designed guidelines can positively impact practice.²⁹ Participants also noted that cSpO₂ guidelines should be authoritative and that knowledge of guideline adoption by peer institutions was a facilitator of adoption. Usability issues negatively impact clinicians’ ability to follow guidelines.³⁰ Further, prior studies have demonstrated that EHR integration of guidelines can change practice.^31-33 Based on our findings, incorporating clear guidelines into commonly used formats, such as EHR order sets, could be an important deimplementation tool for cSpO₂ in stable bronchiolitis patients.

Education about and awareness of cSpO₂ guidelines was described as an important facilitator for appropriate cSpO₂ use and was suggested as a potential deimplementation strategy. Participants noted that educational need may vary by stakeholder group. For example, education may facilitate obtaining buy-in from hospital leaders, which is necessary to support changes to the EHR. Education incorporating information on the typical features of bronchiolitis and examples of appropriate and inappropriate cSpO₂ use was suggested for clinical team members. The limitations of education as a stand-alone deimplementation strategy were also noted, and participants highlighted challenges such as time needed for education and the need for ongoing education for rotating trainees. Inner and outer setting barriers, such as a perceived “culture of high pulse oximetryuse” and patient and family expectations, could also make education less effective as a stand-alone strategy. That—coupled with evidence that education and training alone are generally insufficient for producing reliable, sustained behavior change^34,35—suggests that a multifaceted approach will be important.

Our respondents consider parental perceptions and preference in their practice, which provides nuance to recent studies suggesting that parents prefer continuous monitors when their child is hospitalized with bronchiolitis. Chi et al described the impact of a brief educational intervention on parental preferences for monitoring children hospitalized for bronchiolitis.³⁶ This work suggests that educational interventions aimed at families should be considered in future (de)implementation studies because they may indirectly impact clinician behavior. Future studies should directly assess parental discomfort with discontinuing monitoring. Participants highlighted the link between knowledge and confidence in caring for typical bronchiolitis patients and monitoring practice, perceiving that less experienced clinicians are more likely to rely on cSpO₂. Participants at high-use sites emphasized the expectation that monitoring should occur during hospitalizations. This reflection is particularly pertinent for bronchiolitis, a disease characterized by frequent, self-resolving desaturations even after hospital discharge.³ This may reinforce a perceived need to capture and react to these desaturation events even though they are expected in bronchiolitis and can occur in healthy infants.³⁷ Some participants suggested that continuous monitoring be replaced with “nap tests” (ie, assessment for desaturations during a nap prior to discharge); however, like cSpO₂ in stable infants with bronchiolitis, this is another low-value practice. Otherwise healthy infants with mild to moderate disease are unlikely to subsequently worsen after showing signs of clinical improvement.³⁸ Nap tests are likely to lead to infants who are clinically improving being placed unnecessarily back on oxygen in reaction to the transient desaturations. Participants’ perception about the importance of cSpO₂ in bronchiolitis management, despite evidence suggesting it is a low-value practice, underscores the importance of not simply telling clinicians to stop cSpO₂. Employing strategies that replace continuous monitoring with another acceptable and feasible alternative (eg, regular clinician assessments including intermittent pulse oximetry checks) should be considered when planning for deimplementation.³⁹

Previous studies indicate that continuous monitoring can affect clinician decision-making, independent of other factors,^6,40 despite limited evidence that continuous monitors improve patient outcomes.^1-7 Studies have demonstrated noticeable increase in admissions based purely on pulse oximetry values,⁴⁰ with no evidence that this type of admission changes outcomes for bronchiolitis patients.⁶ One previous, single-center study identified inexperience as a potential driver for monitor use,⁴¹ and studies in adult populations have suggested that clinicians overestimate the value that continuous monitoring contributes to patient care,^42,43 which promotes guideline-discordant use. Our study provides novel insight into the issue of monitoring in bronchiolitis. Our results suggest that there is a need to shift organizational cultures around monitoring (which likely vary based on a range of factors) and that educational strategies addressing typical disease course, especially desaturations, in bronchiolitis will be an essential component in any deimplementation effort.

This study is strengthened by its sample of diverse stakeholder groups from multiple US health systems. Additionally, we interviewed individuals at sites with high cSpO₂ rates and at sites with low rates, as well as from community hospitals, children’s hospitals within general hospitals, and freestanding children’s hospitals, which allows us to understand barriers high-use sites encounter and facilitators of lower cSpO₂ rates at low-use sites. We also employed an interview approach informed by an established implementation science framework. Nonetheless, several limitations exist. First, participants at low-use sites did not necessarily have direct experience with a previous deimplementation effort to reduce cSpO₂. Additionally, participants were predominantly White and female; more diverse perspectives would strengthen confidence in the generalizability of our findings. While thematic saturation was achieved within each stakeholder group and within the high- and low-use strata, we interviewed fewer administrators and respiratory therapists relative to other stakeholder groups. Nevertheless, our conclusions were validated by our interdisciplinary stakeholder panel. As noted by participants, family preferences may influence clinician practice, and parents were not interviewed for this study. The information gleaned from the present study will inform the development of strategies to deimplement unnecessary cSpO₂ in pediatric hospitals, which we aim to rigorously evaluate in a future trial.

We identified barriers and facilitators to deimplementation of cSpO₂ for stable patients with bronchiolitis across children’s hospitals with high and low utilization of cSpO₂. These themes map to multiple CFIR domains and, along with participant-suggested strategies, can directly inform an approach to cSpO₂ deimplementation in a range of inpatient settings. Based on these data, future deimplementation efforts should focus on clear protocols for use and discontinuation of cSpO₂, EHR changes, and regular bronchiolitis education for hospital staff that emphasizes reducing unnecessary cSpO₂ utilization.

We acknowledge the NHLBI scientists who contributed their expertise to this project as part of the U01 Cooperative Agreement funding mechanism as federal employees conducting their official job duties: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD. We thank the Executive Council of the Pediatric Research in Inpatient Settings (PRIS) Network for their contributions to the early scientific development of this project. The Network assessed a Collaborative Support Fee for access to the hospitals and support of this project. We thank the PRIS Network collaborators for their major contributions to data collection measuring utilization to identify the hospitals we subsequently chose for this project. We thank Claire Bocage and the Mixed Methods Research Lab for major help in data management and data analysis.

Continuous pulse oximetry monitoring (cSpO₂) in children with bronchiolitis is associated with increased rates of hospital admission, longer lengths of stay, more frequent treatment with supplemental oxygen, alarm fatigue, and higher hospital cost. There is no evidence that it improves clinical outcomes.^1-7 The safety of reducing cSpO₂ for stable bronchiolitis patients (ie, those who are clinically well and not requiring supplemental oxygen) has been assessed in quality improvement initiatives^8-10 and a randomized controlled trial.² These studies showed no increase in intensive care unit transfers, codes, or readmissions associated with reduced cSpO₂. Current national guidelines from the American Academy of Pediatrics⁵ and the Society of Hospital Medicine Choosing Wisely in Pediatric Hospital Medicine workgroup⁴ support limiting monitoring of children with bronchiolitis. Despite this, the practice of cSpO₂ in stable bronchiolitis patients off supplemental oxygen remains widespread.^11,12

Deimplementation, defined as reducing or stopping low-value or ineffective healthcare practices,^13,14 is a discrete focus area within implementation science. Deimplementation research involves the reduction of unnecessary and overused services for which there is potential for harm or no benefit.^15,16 In pediatrics, there are a number of potential targets for deimplementation,^4,17-20 including cSpO₂ for stable infants with bronchiolitis, but efforts to reduce low-value practices have met limited success to date. ^21,22

Implementation science offers rigorous methods for advancing the development and evaluation of strategies for deimplementation.²³ In particular, implementation science frameworks can facilitate our understanding of relevant contextual factors that may hinder or help efforts to deimplement low-value practices. To develop broadly applicable strategies to reduce monitoring overuse, it is important to understand the barriers, facilitators, and contextual factors (eg, clinical, political, interpersonal) that contribute to guideline-discordant cSpO₂ in hospitalized bronchiolitis patients. Further, the process by which one can develop a rigorous understanding of these factors and how they may impact deimplementation efforts could generalize to other scenarios in pediatrics where overuse remains an issue.

The goal of this study was to use semistructured interviews, informed by an established implementation science framework, specifically the Consolidated Framework for Implementation Research (CFIR),²⁴ to (1) identify barriers and facilitators to deimplementing unnecessary cSpO₂, and (2) develop strategies to deimplement cSpO₂ in a multicenter cohort of hospital-based clinician and administrative stakeholders.

Study Setting

This multicenter qualitative study using semistructured interviews took place within the Eliminating Monitor Overuse (EMO) SpO₂ study. The EMO SpO₂ study established rates of cSpO₂ in bronchiolitis patients not receiving supplemental oxygen or not receiving room air flow at 56 hospitals across the United States and in Canada from December 1, 2018, through March 31, 2019.¹² The study identified hospital-level risk-adjusted cSpO₂ rates ranging from 6% to 82%. A description of the EMO SpO₂ study methods²⁵ and its findings¹² have been published elsewhere.

Participants

We approached EMO study site principal investigators at 12 hospitals: the two highest- and two lowest-use hospitals within three hospital types (ie, freestanding children’s hospitals, children’s hospitals within large general hospitals, and community hospitals). We collaborated with the participating site principal investigators (n = 12), who were primarily hospitalist physicians in leadership roles, to recruit a purposive sample of additional stakeholders including bedside nurses (n = 12), hospitalist physicians (n = 15), respiratory therapists (n = 9), and hospital administrators (n = 8) to participate in semistructured interviews. Interviews were conducted until we achieved thematic saturation within each stakeholder group and within the high and low performing strata (total 56 interviews). Participants were asked to self-report basic demographic information (see Appendix, interview guide) as required by the study funder and to allow us to comment on the representativeness of the participant group.

Procedure

The interview guide was informed by the CFIR, a comprehensive framework detailing contextual factors that require consideration when planning for the implementation of a health service intervention. Table 1 details the CFIR domains with study-related examples. The interview guide (Appendix) provided limited clinical context apart from the age, diagnosis, and oxygen requirement for the population of interest to promote a broad array of responses and to avoid anchoring on specific clinical scenarios. Interviews were conducted by master’s degree or doctoral-level research coordinators with qualitative interviewing experience and supervised by a medical anthropologist and qualitative methods expert (F.K.B.). Prior to engaging in audio recorded phone interviews, the interviewer explained the risks and benefits of participating. Participants were compensated $50. Audio recordings were transcribed, deidentified, and uploaded to NVivo 12 Plus (QSR International) for data management.

The Institutional Review Boards of Children’s Hospital of Philadelphia, Pennsylvania, and the University of Pennsylvania in Philadelphia determined that the study met eligibility criteria for IRB exemption.

Data Analysis

Using an integrated approach to codebook development,²⁶ a priori codes were developed using constructs from the CFIR. Additional codes were added by the research team following a close reading of the first five transcripts.^27,28 Each code was defined, including decision rules for its application. Two research coordinators independently coded each transcript. Using the intercoder reliability function within NVivo, the coders established strong interrater reliability accordance scores (k > .8) by double coding 20% of the transcripts. Data were stratified by sites with low and high use of cSpO₂ to examine differences in barriers and facilitators to deimplementation. Each code was subcoded, summarized, and examined for patterns within and across participating disciplines, which yielded themes related to barriers and facilitators. We conducted member checking and reviewed our conclusions with a multidisciplinary group of clinical stakeholders (n = 13) to validate our analyses.

Barriers and facilitators to deimplementation were identified in multiple domains of the CFIR: outer setting, inner setting, characteristics of the individuals, and intervention characteristics (Table 1). Participants also suggested strategies to facilitate deimplementation in response to some identified barriers. See Table 2 for participant demographics and Table 3 for illustrative participant quotations.

Barriers

Outer Setting: Clinician Perceptions of Parental Discomfort With Discontinuing Monitoring

Participants mentioned parental preferences as a barrier to discontinuing cSpO₂, noting that parents seem to take comfort in watching the numbers on the monitor screen and are reluctant to have it withdrawn. Clinicians noted that parents sometimes put the monitor back on their child after a clinician removed it or have expressed concern that their unmonitored child was not receiving the same level of care as other patients who were being monitored. In these scenarios, clinicians reported they have found it helpful to educate caregivers about when cSpO₂ is and is not appropriate.

Inner Setting: Unclear or Nonexistent Guideline to Discontinue cSpO₂

Guidelines to discontinue cSpO₂ reportedly did not exist at all institutions. If a guideline did exist, lack of clarity or conflicting guidelines about when to use oxygen presented a barrier. Participants suggested that a clear guideline or additional oversight to ensure all clinicians are informed of the procedure for discontinuing cSpO₂ may help prevent miscommunication. Participants noted that their electronic health record (EHR) order sets commonly included cSpO₂ orders and that removing that option would facilitate deimplementation.

Inner Setting: Difficulty Educating All Staff

Participants noted difficulty with incorporating education about discontinuing cSpO₂ to all clinicians, particularly to those who are nightshift only or to rotating staff or trainees. This created barriers for frequent re-education because these staff are not familiar with the policies and procedures of the unit, which is crucial to developing a culture that supports the deimplementation of cSpO₂. Participants suggested that recurring education about procedures for discontinuing cSpO₂ should target trainees, new nurses, and overnight nurses. This would help to ensure that the guideline is uniformly followed.

Inner Setting: Culture of High cSpO₂ Use

Participants from high-use sites discussed a culture driven by readily available monitoring features or an expectation that monitoring indicates higher-quality care. Participants from low-use sites discussed increased cSpO₂ driven by clinicians who were accustomed to caring for higher-acuity patients, for whom continuous monitoring is likely appropriate, and were simultaneously caring for stable bronchiolitis patients.

Some suggested that visual cues would be useful to clinicians to sustain awareness about a cSpO₂ deimplementation guideline. It was also suggested that audit and feedback techniques like posting unit deimplementation statistics and creating a competition among units by posting unit performance could facilitate deimplementation. Additionally, some noted that visual aids in common spaces would be useful to remind clinicians and to engage caregivers about discontinuing cSpO₂.

Characteristics of Individuals: Clinician Discomfort Discontinuing cSpO₂

One frequently cited barrier across participants is that cSpO₂ provides “peace of mind” to alert clinicians to patients with low oxygen saturations that might otherwise be missed. Participants identified that clinician discomfort with reducing cSpO₂ may be driven by inexperienced clinicians less familiar with the bronchiolitis disease process, such as trainees, new nurses, or rotating clinicians unaccustomed to pediatric care. Trainees and new nurses were perceived as being more likely to work at night when there are fewer clinicians to provide patient care. Additionally, participants perceived that night shift clinicians favored cSpO₂ because they could measure vital signs without waking patients and families.

Clinicians discussed that discontinuing cSpO₂ would require alternative methods for assessing patient status, particularly for night shift nurses. Participants suggested strategies including changes to pulse oximetry assessment procedures to include more frequent “spot checks,” incorporation of assessments during sleep events (eg, naps) to ensure the patient does not experience desaturations during sleep, and training nurses to become more comfortable with suctioning patients. Suggestions also included education on the typical features of transient oxygen desaturations in otherwise stable patients with bronchiolitis² to bolster clinical confidence for clinicians unfamiliar with caring for bronchiolitis patients. Participants perceived that education about appropriate vs inappropriate use may help to empower clinicians to employ cSpO₂ appropriately.

Facilitators

Outer Setting: Standards and Evidence From Research, Professional Organizations, and Leaders in the Field

Many participants expressed the importance of consistent guidelines that are advocated by thought leaders in the field, supported by robust evidence, and consistent with approaches at peer hospitals. The more authoritative support a guideline has, the more comfortable people are adopting it and taking it seriously. Additionally, consistent education about guidelines was desired. Participants noted that all clinicians should be receiving education related to the American Academy of Pediatrics (AAP) Bronchiolitis and Choosing Wisely^® guidelines, ranging from a one-time update to annually. Continual updates and re-education sessions for clinicians who shared evidence about how cSpO₂ deimplementation could improve the quality of patient care by shortening hospital length of stay and lowering cost were suggested strategies.

Inner Setting: Leadership

Participants noted that successful deimplementation depends upon the presence of a champion or educator who will be able to lead the institutional charge in making practice change. This is typically an individual who is trusted at the institution, experienced in their field, or already doing implementation work. This could be either a single individual (champion) or a team. The most commonly noted clinician roles to engage in a leadership role or team were physicians and nurses.

Participants noted that a change in related clinical care pathways or EHR order sets would require cooperation from multiple clinical disciplines, administrators, and information technology leaders and explained that messaging and education about the value of the change would facilitate buy-in from those clinicians.

Inner Setting: EHR Support for Guidelines

Participants often endorsed the use of an order set within the EHR that supports guidelines and includes reminders to decrease cSpO₂. These reminders could come up when supplemental oxygen is discontinued or occur regularly throughout the patient’s stay to prompt the clinician to consider discontinuing cSpO₂.

Intervention Characteristics/Inner Setting: Clear Bronchiolitis Guidelines

The presence of a well-articulated hospital policy that delineates the appropriate and inappropriate use of cSpO₂ in bronchiolitis was mentioned as another facilitator of deimplementation.

Results of this qualitative study of stakeholders across hospitals with high and low cSpO₂ use illustrated the complexities involved with deimplementation of cSpO₂ in pediatric patients hospitalized with bronchiolitis. We identified numerous barriers spanning the CFIR constructs, including unclear or absent guidelines for stopping cSpO₂, clinician knowledge and comfort with bronchiolitis disease features, and unit culture. This suggests that multicomponent strategies that target various domains and a variety of stakeholders are needed to deimplement cSpO₂ use for stable bronchiolitis patients. Participants also identified facilitators, including clear cSpO₂ guidelines, supportive leaders and champions, and EHR modifications, that provide insight into strategies that may help sites reduce their use of cSpO₂. Additionally, participants also provided concrete, actionable suggestions for ways to reduce unnecessary monitoring that will be useful in informing promising deimplementation strategies for subsequent trials.

The importance of having specific and well-known guidelines from trusted sources, such as the AAP, about cSpO₂ and bronchiolitis treatment that are thoughtfully integrated in the EHR came through in multiple themes of our analysis. Prior studies on the effect of guidelines on clinical practice have suggested that rigorously designed guidelines can positively impact practice.²⁹ Participants also noted that cSpO₂ guidelines should be authoritative and that knowledge of guideline adoption by peer institutions was a facilitator of adoption. Usability issues negatively impact clinicians’ ability to follow guidelines.³⁰ Further, prior studies have demonstrated that EHR integration of guidelines can change practice.^31-33 Based on our findings, incorporating clear guidelines into commonly used formats, such as EHR order sets, could be an important deimplementation tool for cSpO₂ in stable bronchiolitis patients.

Education about and awareness of cSpO₂ guidelines was described as an important facilitator for appropriate cSpO₂ use and was suggested as a potential deimplementation strategy. Participants noted that educational need may vary by stakeholder group. For example, education may facilitate obtaining buy-in from hospital leaders, which is necessary to support changes to the EHR. Education incorporating information on the typical features of bronchiolitis and examples of appropriate and inappropriate cSpO₂ use was suggested for clinical team members. The limitations of education as a stand-alone deimplementation strategy were also noted, and participants highlighted challenges such as time needed for education and the need for ongoing education for rotating trainees. Inner and outer setting barriers, such as a perceived “culture of high pulse oximetryuse” and patient and family expectations, could also make education less effective as a stand-alone strategy. That—coupled with evidence that education and training alone are generally insufficient for producing reliable, sustained behavior change^34,35—suggests that a multifaceted approach will be important.

Our respondents consider parental perceptions and preference in their practice, which provides nuance to recent studies suggesting that parents prefer continuous monitors when their child is hospitalized with bronchiolitis. Chi et al described the impact of a brief educational intervention on parental preferences for monitoring children hospitalized for bronchiolitis.³⁶ This work suggests that educational interventions aimed at families should be considered in future (de)implementation studies because they may indirectly impact clinician behavior. Future studies should directly assess parental discomfort with discontinuing monitoring. Participants highlighted the link between knowledge and confidence in caring for typical bronchiolitis patients and monitoring practice, perceiving that less experienced clinicians are more likely to rely on cSpO₂. Participants at high-use sites emphasized the expectation that monitoring should occur during hospitalizations. This reflection is particularly pertinent for bronchiolitis, a disease characterized by frequent, self-resolving desaturations even after hospital discharge.³ This may reinforce a perceived need to capture and react to these desaturation events even though they are expected in bronchiolitis and can occur in healthy infants.³⁷ Some participants suggested that continuous monitoring be replaced with “nap tests” (ie, assessment for desaturations during a nap prior to discharge); however, like cSpO₂ in stable infants with bronchiolitis, this is another low-value practice. Otherwise healthy infants with mild to moderate disease are unlikely to subsequently worsen after showing signs of clinical improvement.³⁸ Nap tests are likely to lead to infants who are clinically improving being placed unnecessarily back on oxygen in reaction to the transient desaturations. Participants’ perception about the importance of cSpO₂ in bronchiolitis management, despite evidence suggesting it is a low-value practice, underscores the importance of not simply telling clinicians to stop cSpO₂. Employing strategies that replace continuous monitoring with another acceptable and feasible alternative (eg, regular clinician assessments including intermittent pulse oximetry checks) should be considered when planning for deimplementation.³⁹

Previous studies indicate that continuous monitoring can affect clinician decision-making, independent of other factors,^6,40 despite limited evidence that continuous monitors improve patient outcomes.^1-7 Studies have demonstrated noticeable increase in admissions based purely on pulse oximetry values,⁴⁰ with no evidence that this type of admission changes outcomes for bronchiolitis patients.⁶ One previous, single-center study identified inexperience as a potential driver for monitor use,⁴¹ and studies in adult populations have suggested that clinicians overestimate the value that continuous monitoring contributes to patient care,^42,43 which promotes guideline-discordant use. Our study provides novel insight into the issue of monitoring in bronchiolitis. Our results suggest that there is a need to shift organizational cultures around monitoring (which likely vary based on a range of factors) and that educational strategies addressing typical disease course, especially desaturations, in bronchiolitis will be an essential component in any deimplementation effort.

This study is strengthened by its sample of diverse stakeholder groups from multiple US health systems. Additionally, we interviewed individuals at sites with high cSpO₂ rates and at sites with low rates, as well as from community hospitals, children’s hospitals within general hospitals, and freestanding children’s hospitals, which allows us to understand barriers high-use sites encounter and facilitators of lower cSpO₂ rates at low-use sites. We also employed an interview approach informed by an established implementation science framework. Nonetheless, several limitations exist. First, participants at low-use sites did not necessarily have direct experience with a previous deimplementation effort to reduce cSpO₂. Additionally, participants were predominantly White and female; more diverse perspectives would strengthen confidence in the generalizability of our findings. While thematic saturation was achieved within each stakeholder group and within the high- and low-use strata, we interviewed fewer administrators and respiratory therapists relative to other stakeholder groups. Nevertheless, our conclusions were validated by our interdisciplinary stakeholder panel. As noted by participants, family preferences may influence clinician practice, and parents were not interviewed for this study. The information gleaned from the present study will inform the development of strategies to deimplement unnecessary cSpO₂ in pediatric hospitals, which we aim to rigorously evaluate in a future trial.

We identified barriers and facilitators to deimplementation of cSpO₂ for stable patients with bronchiolitis across children’s hospitals with high and low utilization of cSpO₂. These themes map to multiple CFIR domains and, along with participant-suggested strategies, can directly inform an approach to cSpO₂ deimplementation in a range of inpatient settings. Based on these data, future deimplementation efforts should focus on clear protocols for use and discontinuation of cSpO₂, EHR changes, and regular bronchiolitis education for hospital staff that emphasizes reducing unnecessary cSpO₂ utilization.

We acknowledge the NHLBI scientists who contributed their expertise to this project as part of the U01 Cooperative Agreement funding mechanism as federal employees conducting their official job duties: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD. We thank the Executive Council of the Pediatric Research in Inpatient Settings (PRIS) Network for their contributions to the early scientific development of this project. The Network assessed a Collaborative Support Fee for access to the hospitals and support of this project. We thank the PRIS Network collaborators for their major contributions to data collection measuring utilization to identify the hospitals we subsequently chose for this project. We thank Claire Bocage and the Mixed Methods Research Lab for major help in data management and data analysis.

Continuous pulse oximetry monitoring in children with bronchiolitis who don’t require supplemental oxygen is discouraged by practice guidelines and is recognized as a form of medical overuse.^1-3 This practice can be associated with negative outcomes, including prolonged length of stay,^4-6 increased cost of hospitalization,⁷ and alarm fatigue among nurses.⁸ Despite initiatives to reduce continuous pulse oximetry monitoring in stable patients with bronchiolitis,^1,2 wide practice variation exists between hospitals.^9,10 Previous studies have shown that higher prevalence of inpatient bronchiolitis admissions is associated with decreased utilization of unnecessary interventions.¹¹ However, the relationship between pulse oximetry use and bronchiolitis prevalence has not been studied. The objective of this study is to test the hypothesis that hospital units with lower proportions of patients admitted for bronchiolitis and those with fewer general pediatrics patients relative to subspecialty patients would have higher rates of pulse oximetry overuse.

Study Design

We conducted a substudy of the Pediatric Research in Inpatient Settings (PRIS) Network’s Eliminating Monitoring Overuse (EMO) pulse oximetry study,^10,12 a 56-hospital cross-sectional study that used direct observation to measure the prevalence of continuous pulse oximetry monitoring in hospitalized infants with bronchiolitis who did not require supplemental oxygen between December 1, 2018, through March 31, 2019. This substudy was not included as part of the original aims of the project and was proposed as a separate analysis during data collection. For US sites, the Institutional Review Board (IRB) at Children’s Hospital of Philadelphia approved the study and served as the central IRB. The Research Ethics Board at University of Calgary also approved the study.

Site Selection

Hospitals with at least 60 observations were eligible for inclusion. Of the 32 hospitals that conducted the minimum observations, 25 agreed to participate (21 free-standing children’s hospitals, 3 children’s hospitals within general hospitals, and 1 community hospital).

Patient Population

The parent study included patients aged 8 weeks through 23 months with a primary diagnosis of bronchiolitis. Patients were included only if they were not receiving supplemental oxygen or nasal cannula flow at the time of data collection. The inclusion and exclusion criteria were for both the parent study and the substudy. Further inclusion and exclusion criteria have been described previously.^10,12

Data Collection

In order to ascertain continuous pulse oximetry monitoring status, staff at each hospital performed observational rounds by walking to the bedside of each patient who met inclusion criteria. Additional methodology for the parent study has been published elsewhere.^10,12

Bronchiolitis Admission Volume by Unit

Collaborators at each hospital gathered bronchiolitis census data from each unit that admitted patients with bronchiolitis. Units were identified prior to data collection and were characterized at the institution level based on previous local definitions. Each site was responsible for using institution-specific data collection methods for determining bronchiolitis and total admissions on each unit (eg, departmental reports or directly querying admissions data using International Classification of Diseases, Tenth Revision, diagnosis codes for bronchiolitis) over the same period as the parent study. Following data analysis, bronchiolitis admission burden was classified into five categories, based on less than 10%, 10% to less than 20%, 20% to less than 30%, 30% to less than 40%, or 40% or more of total admissions having a primary discharge diagnosis of bronchiolitis during the study period. This categorization allowed investigators to determine whether there was a dose-dependent response among categories.

Unit Composition

Site investigators also completed a survey identifying which patients were admitted to each unit (eg, general pediatrics only, medical subspecialty, surgical). Based on these results, units were further classified into seven types (Appendix Table). For the final analysis, units caring exclusively for general pediatrics patients were compared to all other unit types.

Analysis

Bronchiolitis admission burden and unit composition data were combined with observations of pulse oximetry monitoring use of patients not requiring supplemental oxygen from the parent study. We determined unadjusted observed monitoring proportions for each unit’s bronchiolitis admission burden category across all 25 hospitals. This was calculated as a simple proportion of the total number of observations during which patients were continuously monitored divided by the total number of observations performed within each unit’s admission category. We then calculated unadjusted odds ratios using the 40% and higher bronchiolitis admission burden category as a reference. We calculated similar proportions and odds ratios for the dichotomous unit composition variable. Next, we used mixed-effects logistic regression with a random intercept for each hospital to allow for differences in baseline monitoring rates, which varied widely between hospitals (2% to 92%),¹⁰ to calculate adjusted odds ratios for the unit’s admission category and unit’s composition. We also adjusted for the same covariates used in the primary study’s analysis (Table).¹⁰

We analyzed 2,366 observations of bronchiolitis patients from 25 hospitals. Most observations were concentrated in freestanding children’s hospitals (89%), and 50% were from hospitals with more than 250 pediatric beds. Observations were well distributed among the five categories of admit burden (Table).

In unadjusted regression, the relationship between admission burden and rate of pulse oximetry use did not appear to be dose-dependent, and 95% CIs were wide. We then analyzed the data accounting for baseline differences in hospital monitoring rates and adjusted for the covariates significantly associated with continuous pulse oximetry monitoring in the primary study’s analysis with use of a mixed-effects model. As shown in the Table, low-burden units in which bronchiolitis constituted less than 10% of total admissions had a 2.16-fold increased odds of unnecessary pulse oximetry monitoring compared to high-burden units in which bronchiolitis constituted 40% or more of total admissions (95% CI, 1.27-3.69; P = .01).

In examining the subspecialty unit composition, 596 observations (25.2%) were conducted on units exclusively caring for general pediatrics patients. In the mixed-effects model adjusted for bronchiolitis admission burden and the covariates used in the study’s primary analysis, units exclusively caring for general pediatrics patients did not have significantly different independent odds of pulse oximetry monitoring use compared to units with a mixed patient population (OR 1.01; 95% CI, 0.71-1.45; P = .95) (Appendix Table).

In this multicenter observational study of children hospitalized with bronchiolitis not concurrently receiving supplemental oxygen, units that only occasionally cared for bronchiolitis patients appeared to be more likely to overuse continuous pulse oximetry during bronchiolitis hospitalizations.

This finding was not immediately apparent when examining the raw data because of wide hospital-level variation in continuous pulse oximetry monitoring use. However, when the high degree of hospital-level variation in baseline overuse was accounted for with use of a random intercept for each hospital in the mixed-effects model, units that cared for higher proportions of bronchiolitis patients had significantly lower odds of continuous pulse oximetry monitoring use compared to units that cared for these infants infrequently.

As many institutions have subspecialized units to cultivate nursing expertise for care of certain diseases and patient populations, we hypothesized that units caring primarily for children on general pediatrics units would also have lower rates of monitoring overuse compared to mixed units. Interestingly, these units did not perform better, likely because potential cultural factors that might contribute to differences in monitoring are accounted for by bronchiolitis admission burden.

Our findings build on prior literature by demonstrating that unit-level, as well as hospital-level, factors appear to drive overuse in healthcare. A prior single-site retrospective cohort study demonstrated an association between higher prevalence of inpatient bronchiolitis and decreased use of unnecessary interventions such as laboratory and radiographic testing, as well as steroid and antibiotic administration.¹¹ Although study of the relationship between volume and quality is not new to healthcare, to our knowledge, this study is the first to examine the relationship between pulse oximetry overuse in bronchiolitis and unit-level factors like admission burden and subspecialty composition.

There are several limitations. First, because the study population included only children not receiving supplemental oxygen, both the parent study and this substudy assumed that all observed use of pulse oximetry monitoring was overuse. In some cases, however, there may have been other compelling clinical reasons, institutional policies, or differences in pulse oximetry availability that were not captured during data collection or in our adjusted model. Second, hospitals used convenience sampling. It is possible this resulted in samples that were not representative of each unit’s underlying patient population or monitoring practice. In addition, not all of the 32 eligible sites were able to provide data related to hospital admissions at the unit level and thus are not included in our analysis. This remains a potential source of hospital-level selection bias.

These findings demonstrate that high bronchiolitis admission burden correlates with lower rates of unnecessary pulse oximetry monitoring in bronchiolitis. We speculate that these outcomes might reflect differing degrees of nursing comfort, expertise, and unit-level norms in caring for bronchiolitis patients, although our study was not designed to establish underlying causes. Identification of operating principles that underpin low pulse oximetry monitoring on high-burden units will provide guidance for decreasing unnecessary monitoring and will inform future studies seeking ways to discourage continuous pulse oximetry monitoring in low-risk infants. Given the institutional variation in monitoring rates, future studies examining both institution-wide and unit-level interventions will be necessary to decrease unnecessary pulse oximetry monitoring in bronchiolitis. Furthermore, these findings may be relevant to studying care quality in other disease processes, with bronchiolitis serving as a model illness for overuse.

The authors acknowledge the National Heart, Lung, and Blood Institute of the National Institutes of Health scientists who contributed their expertise to this project as part of the U01 Cooperative Agreement funding mechanism as federal employees conducting their official job duties: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD. The authors thank the executive council of the Pediatric Research in Inpatient Settings Network for their contributions to the early scientific development of this project. The network assessed a Collaborative Support Fee for access to the hospitals and support of this project.

The authors thank the PRIS Network collaborators for their major contributions to data collection (see Appendix).

Continuous pulse oximetry monitoring in children with bronchiolitis who don’t require supplemental oxygen is discouraged by practice guidelines and is recognized as a form of medical overuse.^1-3 This practice can be associated with negative outcomes, including prolonged length of stay,^4-6 increased cost of hospitalization,⁷ and alarm fatigue among nurses.⁸ Despite initiatives to reduce continuous pulse oximetry monitoring in stable patients with bronchiolitis,^1,2 wide practice variation exists between hospitals.^9,10 Previous studies have shown that higher prevalence of inpatient bronchiolitis admissions is associated with decreased utilization of unnecessary interventions.¹¹ However, the relationship between pulse oximetry use and bronchiolitis prevalence has not been studied. The objective of this study is to test the hypothesis that hospital units with lower proportions of patients admitted for bronchiolitis and those with fewer general pediatrics patients relative to subspecialty patients would have higher rates of pulse oximetry overuse.

Study Design

We conducted a substudy of the Pediatric Research in Inpatient Settings (PRIS) Network’s Eliminating Monitoring Overuse (EMO) pulse oximetry study,^10,12 a 56-hospital cross-sectional study that used direct observation to measure the prevalence of continuous pulse oximetry monitoring in hospitalized infants with bronchiolitis who did not require supplemental oxygen between December 1, 2018, through March 31, 2019. This substudy was not included as part of the original aims of the project and was proposed as a separate analysis during data collection. For US sites, the Institutional Review Board (IRB) at Children’s Hospital of Philadelphia approved the study and served as the central IRB. The Research Ethics Board at University of Calgary also approved the study.

Site Selection

Hospitals with at least 60 observations were eligible for inclusion. Of the 32 hospitals that conducted the minimum observations, 25 agreed to participate (21 free-standing children’s hospitals, 3 children’s hospitals within general hospitals, and 1 community hospital).

Patient Population

The parent study included patients aged 8 weeks through 23 months with a primary diagnosis of bronchiolitis. Patients were included only if they were not receiving supplemental oxygen or nasal cannula flow at the time of data collection. The inclusion and exclusion criteria were for both the parent study and the substudy. Further inclusion and exclusion criteria have been described previously.^10,12

Data Collection

In order to ascertain continuous pulse oximetry monitoring status, staff at each hospital performed observational rounds by walking to the bedside of each patient who met inclusion criteria. Additional methodology for the parent study has been published elsewhere.^10,12

Bronchiolitis Admission Volume by Unit

Collaborators at each hospital gathered bronchiolitis census data from each unit that admitted patients with bronchiolitis. Units were identified prior to data collection and were characterized at the institution level based on previous local definitions. Each site was responsible for using institution-specific data collection methods for determining bronchiolitis and total admissions on each unit (eg, departmental reports or directly querying admissions data using International Classification of Diseases, Tenth Revision, diagnosis codes for bronchiolitis) over the same period as the parent study. Following data analysis, bronchiolitis admission burden was classified into five categories, based on less than 10%, 10% to less than 20%, 20% to less than 30%, 30% to less than 40%, or 40% or more of total admissions having a primary discharge diagnosis of bronchiolitis during the study period. This categorization allowed investigators to determine whether there was a dose-dependent response among categories.

Unit Composition

Site investigators also completed a survey identifying which patients were admitted to each unit (eg, general pediatrics only, medical subspecialty, surgical). Based on these results, units were further classified into seven types (Appendix Table). For the final analysis, units caring exclusively for general pediatrics patients were compared to all other unit types.

Analysis

Bronchiolitis admission burden and unit composition data were combined with observations of pulse oximetry monitoring use of patients not requiring supplemental oxygen from the parent study. We determined unadjusted observed monitoring proportions for each unit’s bronchiolitis admission burden category across all 25 hospitals. This was calculated as a simple proportion of the total number of observations during which patients were continuously monitored divided by the total number of observations performed within each unit’s admission category. We then calculated unadjusted odds ratios using the 40% and higher bronchiolitis admission burden category as a reference. We calculated similar proportions and odds ratios for the dichotomous unit composition variable. Next, we used mixed-effects logistic regression with a random intercept for each hospital to allow for differences in baseline monitoring rates, which varied widely between hospitals (2% to 92%),¹⁰ to calculate adjusted odds ratios for the unit’s admission category and unit’s composition. We also adjusted for the same covariates used in the primary study’s analysis (Table).¹⁰

We analyzed 2,366 observations of bronchiolitis patients from 25 hospitals. Most observations were concentrated in freestanding children’s hospitals (89%), and 50% were from hospitals with more than 250 pediatric beds. Observations were well distributed among the five categories of admit burden (Table).

In unadjusted regression, the relationship between admission burden and rate of pulse oximetry use did not appear to be dose-dependent, and 95% CIs were wide. We then analyzed the data accounting for baseline differences in hospital monitoring rates and adjusted for the covariates significantly associated with continuous pulse oximetry monitoring in the primary study’s analysis with use of a mixed-effects model. As shown in the Table, low-burden units in which bronchiolitis constituted less than 10% of total admissions had a 2.16-fold increased odds of unnecessary pulse oximetry monitoring compared to high-burden units in which bronchiolitis constituted 40% or more of total admissions (95% CI, 1.27-3.69; P = .01).

In examining the subspecialty unit composition, 596 observations (25.2%) were conducted on units exclusively caring for general pediatrics patients. In the mixed-effects model adjusted for bronchiolitis admission burden and the covariates used in the study’s primary analysis, units exclusively caring for general pediatrics patients did not have significantly different independent odds of pulse oximetry monitoring use compared to units with a mixed patient population (OR 1.01; 95% CI, 0.71-1.45; P = .95) (Appendix Table).

In this multicenter observational study of children hospitalized with bronchiolitis not concurrently receiving supplemental oxygen, units that only occasionally cared for bronchiolitis patients appeared to be more likely to overuse continuous pulse oximetry during bronchiolitis hospitalizations.

This finding was not immediately apparent when examining the raw data because of wide hospital-level variation in continuous pulse oximetry monitoring use. However, when the high degree of hospital-level variation in baseline overuse was accounted for with use of a random intercept for each hospital in the mixed-effects model, units that cared for higher proportions of bronchiolitis patients had significantly lower odds of continuous pulse oximetry monitoring use compared to units that cared for these infants infrequently.

As many institutions have subspecialized units to cultivate nursing expertise for care of certain diseases and patient populations, we hypothesized that units caring primarily for children on general pediatrics units would also have lower rates of monitoring overuse compared to mixed units. Interestingly, these units did not perform better, likely because potential cultural factors that might contribute to differences in monitoring are accounted for by bronchiolitis admission burden.

Our findings build on prior literature by demonstrating that unit-level, as well as hospital-level, factors appear to drive overuse in healthcare. A prior single-site retrospective cohort study demonstrated an association between higher prevalence of inpatient bronchiolitis and decreased use of unnecessary interventions such as laboratory and radiographic testing, as well as steroid and antibiotic administration.¹¹ Although study of the relationship between volume and quality is not new to healthcare, to our knowledge, this study is the first to examine the relationship between pulse oximetry overuse in bronchiolitis and unit-level factors like admission burden and subspecialty composition.

There are several limitations. First, because the study population included only children not receiving supplemental oxygen, both the parent study and this substudy assumed that all observed use of pulse oximetry monitoring was overuse. In some cases, however, there may have been other compelling clinical reasons, institutional policies, or differences in pulse oximetry availability that were not captured during data collection or in our adjusted model. Second, hospitals used convenience sampling. It is possible this resulted in samples that were not representative of each unit’s underlying patient population or monitoring practice. In addition, not all of the 32 eligible sites were able to provide data related to hospital admissions at the unit level and thus are not included in our analysis. This remains a potential source of hospital-level selection bias.

These findings demonstrate that high bronchiolitis admission burden correlates with lower rates of unnecessary pulse oximetry monitoring in bronchiolitis. We speculate that these outcomes might reflect differing degrees of nursing comfort, expertise, and unit-level norms in caring for bronchiolitis patients, although our study was not designed to establish underlying causes. Identification of operating principles that underpin low pulse oximetry monitoring on high-burden units will provide guidance for decreasing unnecessary monitoring and will inform future studies seeking ways to discourage continuous pulse oximetry monitoring in low-risk infants. Given the institutional variation in monitoring rates, future studies examining both institution-wide and unit-level interventions will be necessary to decrease unnecessary pulse oximetry monitoring in bronchiolitis. Furthermore, these findings may be relevant to studying care quality in other disease processes, with bronchiolitis serving as a model illness for overuse.

The authors acknowledge the National Heart, Lung, and Blood Institute of the National Institutes of Health scientists who contributed their expertise to this project as part of the U01 Cooperative Agreement funding mechanism as federal employees conducting their official job duties: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD. The authors thank the executive council of the Pediatric Research in Inpatient Settings Network for their contributions to the early scientific development of this project. The network assessed a Collaborative Support Fee for access to the hospitals and support of this project.

The authors thank the PRIS Network collaborators for their major contributions to data collection (see Appendix).

Continuous pulse oximetry monitoring in children with bronchiolitis who don’t require supplemental oxygen is discouraged by practice guidelines and is recognized as a form of medical overuse.^1-3 This practice can be associated with negative outcomes, including prolonged length of stay,^4-6 increased cost of hospitalization,⁷ and alarm fatigue among nurses.⁸ Despite initiatives to reduce continuous pulse oximetry monitoring in stable patients with bronchiolitis,^1,2 wide practice variation exists between hospitals.^9,10 Previous studies have shown that higher prevalence of inpatient bronchiolitis admissions is associated with decreased utilization of unnecessary interventions.¹¹ However, the relationship between pulse oximetry use and bronchiolitis prevalence has not been studied. The objective of this study is to test the hypothesis that hospital units with lower proportions of patients admitted for bronchiolitis and those with fewer general pediatrics patients relative to subspecialty patients would have higher rates of pulse oximetry overuse.

Study Design

We conducted a substudy of the Pediatric Research in Inpatient Settings (PRIS) Network’s Eliminating Monitoring Overuse (EMO) pulse oximetry study,^10,12 a 56-hospital cross-sectional study that used direct observation to measure the prevalence of continuous pulse oximetry monitoring in hospitalized infants with bronchiolitis who did not require supplemental oxygen between December 1, 2018, through March 31, 2019. This substudy was not included as part of the original aims of the project and was proposed as a separate analysis during data collection. For US sites, the Institutional Review Board (IRB) at Children’s Hospital of Philadelphia approved the study and served as the central IRB. The Research Ethics Board at University of Calgary also approved the study.

Site Selection

Hospitals with at least 60 observations were eligible for inclusion. Of the 32 hospitals that conducted the minimum observations, 25 agreed to participate (21 free-standing children’s hospitals, 3 children’s hospitals within general hospitals, and 1 community hospital).

Patient Population

The parent study included patients aged 8 weeks through 23 months with a primary diagnosis of bronchiolitis. Patients were included only if they were not receiving supplemental oxygen or nasal cannula flow at the time of data collection. The inclusion and exclusion criteria were for both the parent study and the substudy. Further inclusion and exclusion criteria have been described previously.^10,12

Data Collection

In order to ascertain continuous pulse oximetry monitoring status, staff at each hospital performed observational rounds by walking to the bedside of each patient who met inclusion criteria. Additional methodology for the parent study has been published elsewhere.^10,12

Bronchiolitis Admission Volume by Unit

Collaborators at each hospital gathered bronchiolitis census data from each unit that admitted patients with bronchiolitis. Units were identified prior to data collection and were characterized at the institution level based on previous local definitions. Each site was responsible for using institution-specific data collection methods for determining bronchiolitis and total admissions on each unit (eg, departmental reports or directly querying admissions data using International Classification of Diseases, Tenth Revision, diagnosis codes for bronchiolitis) over the same period as the parent study. Following data analysis, bronchiolitis admission burden was classified into five categories, based on less than 10%, 10% to less than 20%, 20% to less than 30%, 30% to less than 40%, or 40% or more of total admissions having a primary discharge diagnosis of bronchiolitis during the study period. This categorization allowed investigators to determine whether there was a dose-dependent response among categories.

Unit Composition

Site investigators also completed a survey identifying which patients were admitted to each unit (eg, general pediatrics only, medical subspecialty, surgical). Based on these results, units were further classified into seven types (Appendix Table). For the final analysis, units caring exclusively for general pediatrics patients were compared to all other unit types.

Analysis

Bronchiolitis admission burden and unit composition data were combined with observations of pulse oximetry monitoring use of patients not requiring supplemental oxygen from the parent study. We determined unadjusted observed monitoring proportions for each unit’s bronchiolitis admission burden category across all 25 hospitals. This was calculated as a simple proportion of the total number of observations during which patients were continuously monitored divided by the total number of observations performed within each unit’s admission category. We then calculated unadjusted odds ratios using the 40% and higher bronchiolitis admission burden category as a reference. We calculated similar proportions and odds ratios for the dichotomous unit composition variable. Next, we used mixed-effects logistic regression with a random intercept for each hospital to allow for differences in baseline monitoring rates, which varied widely between hospitals (2% to 92%),¹⁰ to calculate adjusted odds ratios for the unit’s admission category and unit’s composition. We also adjusted for the same covariates used in the primary study’s analysis (Table).¹⁰

We analyzed 2,366 observations of bronchiolitis patients from 25 hospitals. Most observations were concentrated in freestanding children’s hospitals (89%), and 50% were from hospitals with more than 250 pediatric beds. Observations were well distributed among the five categories of admit burden (Table).

In unadjusted regression, the relationship between admission burden and rate of pulse oximetry use did not appear to be dose-dependent, and 95% CIs were wide. We then analyzed the data accounting for baseline differences in hospital monitoring rates and adjusted for the covariates significantly associated with continuous pulse oximetry monitoring in the primary study’s analysis with use of a mixed-effects model. As shown in the Table, low-burden units in which bronchiolitis constituted less than 10% of total admissions had a 2.16-fold increased odds of unnecessary pulse oximetry monitoring compared to high-burden units in which bronchiolitis constituted 40% or more of total admissions (95% CI, 1.27-3.69; P = .01).

In examining the subspecialty unit composition, 596 observations (25.2%) were conducted on units exclusively caring for general pediatrics patients. In the mixed-effects model adjusted for bronchiolitis admission burden and the covariates used in the study’s primary analysis, units exclusively caring for general pediatrics patients did not have significantly different independent odds of pulse oximetry monitoring use compared to units with a mixed patient population (OR 1.01; 95% CI, 0.71-1.45; P = .95) (Appendix Table).

In this multicenter observational study of children hospitalized with bronchiolitis not concurrently receiving supplemental oxygen, units that only occasionally cared for bronchiolitis patients appeared to be more likely to overuse continuous pulse oximetry during bronchiolitis hospitalizations.

This finding was not immediately apparent when examining the raw data because of wide hospital-level variation in continuous pulse oximetry monitoring use. However, when the high degree of hospital-level variation in baseline overuse was accounted for with use of a random intercept for each hospital in the mixed-effects model, units that cared for higher proportions of bronchiolitis patients had significantly lower odds of continuous pulse oximetry monitoring use compared to units that cared for these infants infrequently.

As many institutions have subspecialized units to cultivate nursing expertise for care of certain diseases and patient populations, we hypothesized that units caring primarily for children on general pediatrics units would also have lower rates of monitoring overuse compared to mixed units. Interestingly, these units did not perform better, likely because potential cultural factors that might contribute to differences in monitoring are accounted for by bronchiolitis admission burden.

Our findings build on prior literature by demonstrating that unit-level, as well as hospital-level, factors appear to drive overuse in healthcare. A prior single-site retrospective cohort study demonstrated an association between higher prevalence of inpatient bronchiolitis and decreased use of unnecessary interventions such as laboratory and radiographic testing, as well as steroid and antibiotic administration.¹¹ Although study of the relationship between volume and quality is not new to healthcare, to our knowledge, this study is the first to examine the relationship between pulse oximetry overuse in bronchiolitis and unit-level factors like admission burden and subspecialty composition.

There are several limitations. First, because the study population included only children not receiving supplemental oxygen, both the parent study and this substudy assumed that all observed use of pulse oximetry monitoring was overuse. In some cases, however, there may have been other compelling clinical reasons, institutional policies, or differences in pulse oximetry availability that were not captured during data collection or in our adjusted model. Second, hospitals used convenience sampling. It is possible this resulted in samples that were not representative of each unit’s underlying patient population or monitoring practice. In addition, not all of the 32 eligible sites were able to provide data related to hospital admissions at the unit level and thus are not included in our analysis. This remains a potential source of hospital-level selection bias.

These findings demonstrate that high bronchiolitis admission burden correlates with lower rates of unnecessary pulse oximetry monitoring in bronchiolitis. We speculate that these outcomes might reflect differing degrees of nursing comfort, expertise, and unit-level norms in caring for bronchiolitis patients, although our study was not designed to establish underlying causes. Identification of operating principles that underpin low pulse oximetry monitoring on high-burden units will provide guidance for decreasing unnecessary monitoring and will inform future studies seeking ways to discourage continuous pulse oximetry monitoring in low-risk infants. Given the institutional variation in monitoring rates, future studies examining both institution-wide and unit-level interventions will be necessary to decrease unnecessary pulse oximetry monitoring in bronchiolitis. Furthermore, these findings may be relevant to studying care quality in other disease processes, with bronchiolitis serving as a model illness for overuse.

The authors acknowledge the National Heart, Lung, and Blood Institute of the National Institutes of Health scientists who contributed their expertise to this project as part of the U01 Cooperative Agreement funding mechanism as federal employees conducting their official job duties: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD. The authors thank the executive council of the Pediatric Research in Inpatient Settings Network for their contributions to the early scientific development of this project. The network assessed a Collaborative Support Fee for access to the hospitals and support of this project.

The authors thank the PRIS Network collaborators for their major contributions to data collection (see Appendix).

As part of improvement collaboratives that aimed to reduce overuse of continuous pulse oximetry in children hospitalized with bronchiolitis, researchers used the presence of an active order for it as a proxy for the actual use of such monitoring.^1,2 With use of this proxy, investigators on a national study documented a high burden of continuous oximetry overuse (86.5% before quality improvement interventions and 45.5% after),¹ but the validity of orders in representing actual monitoring practice is unknown. If the presence of an active pulse oximetry order accurately identifies infants on monitors, electronic health record data could inform epidemiologic estimates of monitoring overuse and measure the success of quality improvement and deimplementation interventions. Alternatively, if nurses commonly begin and/or discontinue pulse oximetry without updated orders, a pulse oximetry order would not be an accurate proxy, and additional data capture methods (eg, bedside observation or data capture from bedside monitors) would be needed.

Understanding the validity of orders for detection of actual use is critical because continuous pulse oximetry monitoring is considered an overused practice in pediatric acute viral bronchiolitis,³ and national guidelines recommend against its use in low-risk hospitalized children.^4,5 Continuous monitoring may identify trivial, self-resolving oxygen desaturation and its use is not associated with improved outcomes.^6-9 When self-resolving desaturations are treated with additional supplemental oxygen, hospital stays may be unnecessarily prolonged.¹⁰ In order to reduce unnecessary continuous pulse oximetry use, measurement of the extent of the overused practice is necessary. In this 56-hospital study,¹¹ we aimed to determine the validity of using active continuous pulse oximetry orders instead of bedside observation of actual monitor use.

Design

In this multicenter, repeated cross-sectional study, investigators used direct bedside observation to determine continuous pulse oximetry monitor use and then assessed whether an active continuous monitoring order was present in the electronic health record. The study took place during one bronchiolitis season, December 1, 2018, through March 31, 2019.

Setting and Patients

Investigators at 56 freestanding children’s hospitals, children’s hospitals within general hospitals, and community hospitals in the Pediatric Research in Inpatient Settings (PRIS) Network collected data on infants aged 8 weeks to 23 months who were hospitalized with bronchiolitis. As this work was a substudy of the larger Eliminating Monitor Overuse study, only infants not currently receiving supplemental oxygen were included.¹¹ Investigators observed eligible infants outside of the intensive care unit on general hospital medicine units. We excluded infants born premature (documented prematurity of <28 weeks’ gestation or documented “premature” without a gestational age listed), as well as those with a home oxygen requirement, cyanotic congenital heart disease, pulmonary hypertension, tracheostomy, primary neuromuscular disease, immunodeficiency, or cancer.

Data Collection

Investigators used the electronic health record to identify eligible infants. Investigators entered patient rooms to confirm the infant was not on supplemental oxygen (hence confirming eligibility for the study) and determine if continuous pulse oximetry was actively in use by examining the monitor display for a pulse oximetry waveform. Investigators then confirmed if active orders for pulse oximetry were present in the patient’s chart. Per study design, site investigators aimed to observe approximately half of eligible infants during the day (10 am to 5 pm) and the other half during the night (11 pm to 7 am).

Analysis

We excluded patients with conditional orders (eg, monitored only when certain conditions exist, such as when asleep) because of the time-varying and wide range of conditions that could be specified. Furthermore, conditional orders would not be useful as proxies to measure oximetry use because investigators would still need additional data (eg, bedside observation) to determine current monitoring status.

We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of active orders using the reference standard of direct bedside observation, as well as corresponding 95% CIs that accounted for within-hospital clustering. We calculated these test characteristics overall and as stratified across four age groups: 8 weeks to 5 months, 6 months to 11 months, 12 months to 17 months, and 18 months to 23 months. We also calculated the test characteristics for each hospital. We decided a priori that a PPV and NPV of 80% would represent a reasonable threshold to use active orders as a proxy in multicenter research. For hospital-level analyses we included only hospitals with 60 or more total observations and more than 15 observations with active orders for PPV and more than 15 observations without active orders for NPV. We used Stata (StataCorp LLC, College Station, Texas) version 15.1 for analysis.

For US sites, the Institutional Review Board (IRB) at Children’s Hospital of Philadelphia approved the study as the single reviewing IRB, and the remaining US sites established reliance agreements with the reviewing IRB. Research Ethics Boards at the Canadian sites (University of Calgary and The Hospital for Sick Children) also reviewed and approved the study. All sites granted waivers of consent, assent, parental permission, and HIPAA authorization.

Investigators completed 3,612 observations in 56 hospitals. This included 33 freestanding children’s hospitals, 14 hospitals within large general hospitals, and 9 community hospitals. Of 3,612 completed observations, on 631 occasions (17%) patients had conditional orders (eg, continuous monitoring only when sleeping) and were excluded from further analysis.

Most pulse oximetry–monitored infants did not have an active monitoring order (670 out of 1,309; sensitivity of 49%). Test characteristics, stratified by age group, are presented in the Table. Across all observations, the overall PPV was 77% (95% CI, 72-82), and the overall NPV was 69% (95% CI, 61-77). Variation of all test characteristics across age group was small (eg, the sensitivity ranged from 43% to 51%).

With inclusion of only those hospitals with sufficient observations, hospital-level variation in the PPV and NPV of using active orders was substantial (PPV range of 48% to 96% and NPV range of 30% to 98%). Only two hospitals had both a PPV and NPV for using monitor orders that exceeded the 80% threshold.

Active continuous pulse oximetry orders did not accurately represent actual monitoring status in this study. Monitoring orders alone frequently misrepresent true monitoring status and, as such, should be interpreted with caution in research or quality improvement activities. If more valid estimates of monitoring use and overuse are needed, potential measurement options include direct observation, as used in our study, as well as the use of more complex data streams such as the output of monitoring devices or pulse oximetry data in the electronic health record. In only two of the hospitals, using active continuous monitoring orders was a reasonable proxy for detecting actual monitor use. Monitoring orders could potentially be validly used for deimplementation efforts at those centers; other hospitals could consider targeted improvement efforts (eg, morning huddles examining the discordance between monitoring orders and monitoring status) to improve the accuracy of using continuous pulse oximetry orders.

We acknowledge several limitations of this study. Site investigators employed a convenience sampling approach, so it is possible that some investigators observed sicker or less sick infants. Although the PRIS network includes a geographically diverse group of North American hospitals, community hospitals were underrepresented in this study. Our results hence generalize more precisely to freestanding children’s hospitals than to community hospitals. We did not observe infants currently on supplemental oxygen, so we do not know to what degree using orders is valid in that context. We did not collect data on why actual monitoring status differed from monitoring orders and hence cannot quantify to what extent different factors (eg, nurse belief that monitors are a safety net or infants inadvertently left on monitors after a spot check pulse oximetry reading) contributed to this discordance. Finally, our study only examined one electronic health record variable—the presence of an active order. It may be that other variables in the health record (eg, minute-by-minute pulse oximetry values in a vital sign flowsheet) are much better proxies of actual continuous monitor use.

Using an active order for continuous pulse oximetry has poor sensitivity, PPV, and NPV for detecting true monitoring status at the bedside. Teams intending to measure the actual use of pulse oximetry should be aware of the limitations of using active orders alone as an accurate measure of pulse oximetry monitoring.

We thank the NHLBI scientists who contributed to this project as part of the U01 Cooperative Agreement funding mechanism: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD.

We thank the Executive Council of the PRIS Network for their contributions to the early scientific development of this project. We thank the PRIS site investigators for their major contributions to the Eliminating Monitor Overuse (EMO) Study data collection. Each listed collaborator is a group author for the PRIS Network in this manuscript. Their names can be found in the online supplemental information.

As part of improvement collaboratives that aimed to reduce overuse of continuous pulse oximetry in children hospitalized with bronchiolitis, researchers used the presence of an active order for it as a proxy for the actual use of such monitoring.^1,2 With use of this proxy, investigators on a national study documented a high burden of continuous oximetry overuse (86.5% before quality improvement interventions and 45.5% after),¹ but the validity of orders in representing actual monitoring practice is unknown. If the presence of an active pulse oximetry order accurately identifies infants on monitors, electronic health record data could inform epidemiologic estimates of monitoring overuse and measure the success of quality improvement and deimplementation interventions. Alternatively, if nurses commonly begin and/or discontinue pulse oximetry without updated orders, a pulse oximetry order would not be an accurate proxy, and additional data capture methods (eg, bedside observation or data capture from bedside monitors) would be needed.

Understanding the validity of orders for detection of actual use is critical because continuous pulse oximetry monitoring is considered an overused practice in pediatric acute viral bronchiolitis,³ and national guidelines recommend against its use in low-risk hospitalized children.^4,5 Continuous monitoring may identify trivial, self-resolving oxygen desaturation and its use is not associated with improved outcomes.^6-9 When self-resolving desaturations are treated with additional supplemental oxygen, hospital stays may be unnecessarily prolonged.¹⁰ In order to reduce unnecessary continuous pulse oximetry use, measurement of the extent of the overused practice is necessary. In this 56-hospital study,¹¹ we aimed to determine the validity of using active continuous pulse oximetry orders instead of bedside observation of actual monitor use.

Design

In this multicenter, repeated cross-sectional study, investigators used direct bedside observation to determine continuous pulse oximetry monitor use and then assessed whether an active continuous monitoring order was present in the electronic health record. The study took place during one bronchiolitis season, December 1, 2018, through March 31, 2019.

Setting and Patients

Investigators at 56 freestanding children’s hospitals, children’s hospitals within general hospitals, and community hospitals in the Pediatric Research in Inpatient Settings (PRIS) Network collected data on infants aged 8 weeks to 23 months who were hospitalized with bronchiolitis. As this work was a substudy of the larger Eliminating Monitor Overuse study, only infants not currently receiving supplemental oxygen were included.¹¹ Investigators observed eligible infants outside of the intensive care unit on general hospital medicine units. We excluded infants born premature (documented prematurity of <28 weeks’ gestation or documented “premature” without a gestational age listed), as well as those with a home oxygen requirement, cyanotic congenital heart disease, pulmonary hypertension, tracheostomy, primary neuromuscular disease, immunodeficiency, or cancer.

Data Collection

Investigators used the electronic health record to identify eligible infants. Investigators entered patient rooms to confirm the infant was not on supplemental oxygen (hence confirming eligibility for the study) and determine if continuous pulse oximetry was actively in use by examining the monitor display for a pulse oximetry waveform. Investigators then confirmed if active orders for pulse oximetry were present in the patient’s chart. Per study design, site investigators aimed to observe approximately half of eligible infants during the day (10 am to 5 pm) and the other half during the night (11 pm to 7 am).

Analysis

We excluded patients with conditional orders (eg, monitored only when certain conditions exist, such as when asleep) because of the time-varying and wide range of conditions that could be specified. Furthermore, conditional orders would not be useful as proxies to measure oximetry use because investigators would still need additional data (eg, bedside observation) to determine current monitoring status.

We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of active orders using the reference standard of direct bedside observation, as well as corresponding 95% CIs that accounted for within-hospital clustering. We calculated these test characteristics overall and as stratified across four age groups: 8 weeks to 5 months, 6 months to 11 months, 12 months to 17 months, and 18 months to 23 months. We also calculated the test characteristics for each hospital. We decided a priori that a PPV and NPV of 80% would represent a reasonable threshold to use active orders as a proxy in multicenter research. For hospital-level analyses we included only hospitals with 60 or more total observations and more than 15 observations with active orders for PPV and more than 15 observations without active orders for NPV. We used Stata (StataCorp LLC, College Station, Texas) version 15.1 for analysis.

For US sites, the Institutional Review Board (IRB) at Children’s Hospital of Philadelphia approved the study as the single reviewing IRB, and the remaining US sites established reliance agreements with the reviewing IRB. Research Ethics Boards at the Canadian sites (University of Calgary and The Hospital for Sick Children) also reviewed and approved the study. All sites granted waivers of consent, assent, parental permission, and HIPAA authorization.

Investigators completed 3,612 observations in 56 hospitals. This included 33 freestanding children’s hospitals, 14 hospitals within large general hospitals, and 9 community hospitals. Of 3,612 completed observations, on 631 occasions (17%) patients had conditional orders (eg, continuous monitoring only when sleeping) and were excluded from further analysis.

Most pulse oximetry–monitored infants did not have an active monitoring order (670 out of 1,309; sensitivity of 49%). Test characteristics, stratified by age group, are presented in the Table. Across all observations, the overall PPV was 77% (95% CI, 72-82), and the overall NPV was 69% (95% CI, 61-77). Variation of all test characteristics across age group was small (eg, the sensitivity ranged from 43% to 51%).

With inclusion of only those hospitals with sufficient observations, hospital-level variation in the PPV and NPV of using active orders was substantial (PPV range of 48% to 96% and NPV range of 30% to 98%). Only two hospitals had both a PPV and NPV for using monitor orders that exceeded the 80% threshold.

Active continuous pulse oximetry orders did not accurately represent actual monitoring status in this study. Monitoring orders alone frequently misrepresent true monitoring status and, as such, should be interpreted with caution in research or quality improvement activities. If more valid estimates of monitoring use and overuse are needed, potential measurement options include direct observation, as used in our study, as well as the use of more complex data streams such as the output of monitoring devices or pulse oximetry data in the electronic health record. In only two of the hospitals, using active continuous monitoring orders was a reasonable proxy for detecting actual monitor use. Monitoring orders could potentially be validly used for deimplementation efforts at those centers; other hospitals could consider targeted improvement efforts (eg, morning huddles examining the discordance between monitoring orders and monitoring status) to improve the accuracy of using continuous pulse oximetry orders.

We acknowledge several limitations of this study. Site investigators employed a convenience sampling approach, so it is possible that some investigators observed sicker or less sick infants. Although the PRIS network includes a geographically diverse group of North American hospitals, community hospitals were underrepresented in this study. Our results hence generalize more precisely to freestanding children’s hospitals than to community hospitals. We did not observe infants currently on supplemental oxygen, so we do not know to what degree using orders is valid in that context. We did not collect data on why actual monitoring status differed from monitoring orders and hence cannot quantify to what extent different factors (eg, nurse belief that monitors are a safety net or infants inadvertently left on monitors after a spot check pulse oximetry reading) contributed to this discordance. Finally, our study only examined one electronic health record variable—the presence of an active order. It may be that other variables in the health record (eg, minute-by-minute pulse oximetry values in a vital sign flowsheet) are much better proxies of actual continuous monitor use.

Using an active order for continuous pulse oximetry has poor sensitivity, PPV, and NPV for detecting true monitoring status at the bedside. Teams intending to measure the actual use of pulse oximetry should be aware of the limitations of using active orders alone as an accurate measure of pulse oximetry monitoring.

We thank the NHLBI scientists who contributed to this project as part of the U01 Cooperative Agreement funding mechanism: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD.

We thank the Executive Council of the PRIS Network for their contributions to the early scientific development of this project. We thank the PRIS site investigators for their major contributions to the Eliminating Monitor Overuse (EMO) Study data collection. Each listed collaborator is a group author for the PRIS Network in this manuscript. Their names can be found in the online supplemental information.

As part of improvement collaboratives that aimed to reduce overuse of continuous pulse oximetry in children hospitalized with bronchiolitis, researchers used the presence of an active order for it as a proxy for the actual use of such monitoring.^1,2 With use of this proxy, investigators on a national study documented a high burden of continuous oximetry overuse (86.5% before quality improvement interventions and 45.5% after),¹ but the validity of orders in representing actual monitoring practice is unknown. If the presence of an active pulse oximetry order accurately identifies infants on monitors, electronic health record data could inform epidemiologic estimates of monitoring overuse and measure the success of quality improvement and deimplementation interventions. Alternatively, if nurses commonly begin and/or discontinue pulse oximetry without updated orders, a pulse oximetry order would not be an accurate proxy, and additional data capture methods (eg, bedside observation or data capture from bedside monitors) would be needed.

Understanding the validity of orders for detection of actual use is critical because continuous pulse oximetry monitoring is considered an overused practice in pediatric acute viral bronchiolitis,³ and national guidelines recommend against its use in low-risk hospitalized children.^4,5 Continuous monitoring may identify trivial, self-resolving oxygen desaturation and its use is not associated with improved outcomes.^6-9 When self-resolving desaturations are treated with additional supplemental oxygen, hospital stays may be unnecessarily prolonged.¹⁰ In order to reduce unnecessary continuous pulse oximetry use, measurement of the extent of the overused practice is necessary. In this 56-hospital study,¹¹ we aimed to determine the validity of using active continuous pulse oximetry orders instead of bedside observation of actual monitor use.

Design

In this multicenter, repeated cross-sectional study, investigators used direct bedside observation to determine continuous pulse oximetry monitor use and then assessed whether an active continuous monitoring order was present in the electronic health record. The study took place during one bronchiolitis season, December 1, 2018, through March 31, 2019.

Setting and Patients

Investigators at 56 freestanding children’s hospitals, children’s hospitals within general hospitals, and community hospitals in the Pediatric Research in Inpatient Settings (PRIS) Network collected data on infants aged 8 weeks to 23 months who were hospitalized with bronchiolitis. As this work was a substudy of the larger Eliminating Monitor Overuse study, only infants not currently receiving supplemental oxygen were included.¹¹ Investigators observed eligible infants outside of the intensive care unit on general hospital medicine units. We excluded infants born premature (documented prematurity of <28 weeks’ gestation or documented “premature” without a gestational age listed), as well as those with a home oxygen requirement, cyanotic congenital heart disease, pulmonary hypertension, tracheostomy, primary neuromuscular disease, immunodeficiency, or cancer.

Data Collection

Investigators used the electronic health record to identify eligible infants. Investigators entered patient rooms to confirm the infant was not on supplemental oxygen (hence confirming eligibility for the study) and determine if continuous pulse oximetry was actively in use by examining the monitor display for a pulse oximetry waveform. Investigators then confirmed if active orders for pulse oximetry were present in the patient’s chart. Per study design, site investigators aimed to observe approximately half of eligible infants during the day (10 am to 5 pm) and the other half during the night (11 pm to 7 am).

Analysis

We excluded patients with conditional orders (eg, monitored only when certain conditions exist, such as when asleep) because of the time-varying and wide range of conditions that could be specified. Furthermore, conditional orders would not be useful as proxies to measure oximetry use because investigators would still need additional data (eg, bedside observation) to determine current monitoring status.

We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of active orders using the reference standard of direct bedside observation, as well as corresponding 95% CIs that accounted for within-hospital clustering. We calculated these test characteristics overall and as stratified across four age groups: 8 weeks to 5 months, 6 months to 11 months, 12 months to 17 months, and 18 months to 23 months. We also calculated the test characteristics for each hospital. We decided a priori that a PPV and NPV of 80% would represent a reasonable threshold to use active orders as a proxy in multicenter research. For hospital-level analyses we included only hospitals with 60 or more total observations and more than 15 observations with active orders for PPV and more than 15 observations without active orders for NPV. We used Stata (StataCorp LLC, College Station, Texas) version 15.1 for analysis.

For US sites, the Institutional Review Board (IRB) at Children’s Hospital of Philadelphia approved the study as the single reviewing IRB, and the remaining US sites established reliance agreements with the reviewing IRB. Research Ethics Boards at the Canadian sites (University of Calgary and The Hospital for Sick Children) also reviewed and approved the study. All sites granted waivers of consent, assent, parental permission, and HIPAA authorization.

Investigators completed 3,612 observations in 56 hospitals. This included 33 freestanding children’s hospitals, 14 hospitals within large general hospitals, and 9 community hospitals. Of 3,612 completed observations, on 631 occasions (17%) patients had conditional orders (eg, continuous monitoring only when sleeping) and were excluded from further analysis.

Most pulse oximetry–monitored infants did not have an active monitoring order (670 out of 1,309; sensitivity of 49%). Test characteristics, stratified by age group, are presented in the Table. Across all observations, the overall PPV was 77% (95% CI, 72-82), and the overall NPV was 69% (95% CI, 61-77). Variation of all test characteristics across age group was small (eg, the sensitivity ranged from 43% to 51%).

With inclusion of only those hospitals with sufficient observations, hospital-level variation in the PPV and NPV of using active orders was substantial (PPV range of 48% to 96% and NPV range of 30% to 98%). Only two hospitals had both a PPV and NPV for using monitor orders that exceeded the 80% threshold.

Active continuous pulse oximetry orders did not accurately represent actual monitoring status in this study. Monitoring orders alone frequently misrepresent true monitoring status and, as such, should be interpreted with caution in research or quality improvement activities. If more valid estimates of monitoring use and overuse are needed, potential measurement options include direct observation, as used in our study, as well as the use of more complex data streams such as the output of monitoring devices or pulse oximetry data in the electronic health record. In only two of the hospitals, using active continuous monitoring orders was a reasonable proxy for detecting actual monitor use. Monitoring orders could potentially be validly used for deimplementation efforts at those centers; other hospitals could consider targeted improvement efforts (eg, morning huddles examining the discordance between monitoring orders and monitoring status) to improve the accuracy of using continuous pulse oximetry orders.

We acknowledge several limitations of this study. Site investigators employed a convenience sampling approach, so it is possible that some investigators observed sicker or less sick infants. Although the PRIS network includes a geographically diverse group of North American hospitals, community hospitals were underrepresented in this study. Our results hence generalize more precisely to freestanding children’s hospitals than to community hospitals. We did not observe infants currently on supplemental oxygen, so we do not know to what degree using orders is valid in that context. We did not collect data on why actual monitoring status differed from monitoring orders and hence cannot quantify to what extent different factors (eg, nurse belief that monitors are a safety net or infants inadvertently left on monitors after a spot check pulse oximetry reading) contributed to this discordance. Finally, our study only examined one electronic health record variable—the presence of an active order. It may be that other variables in the health record (eg, minute-by-minute pulse oximetry values in a vital sign flowsheet) are much better proxies of actual continuous monitor use.

Using an active order for continuous pulse oximetry has poor sensitivity, PPV, and NPV for detecting true monitoring status at the bedside. Teams intending to measure the actual use of pulse oximetry should be aware of the limitations of using active orders alone as an accurate measure of pulse oximetry monitoring.

We thank the NHLBI scientists who contributed to this project as part of the U01 Cooperative Agreement funding mechanism: Lora Reineck, MD, MS, Karen Bienstock, MS, and Cheryl Boyce, PhD.

We thank the Executive Council of the PRIS Network for their contributions to the early scientific development of this project. We thank the PRIS site investigators for their major contributions to the Eliminating Monitor Overuse (EMO) Study data collection. Each listed collaborator is a group author for the PRIS Network in this manuscript. Their names can be found in the online supplemental information.

Over the past two decades, physicians and nurses practicing in hospital settings have faced an onslaught of challenges in communication, an area frequently cited as critical to providing safe and effective care to patients.^1-3 Communication needs have increased significantly as hospitalized patients have become more acute, complex, and technology-dependent, requiring larger healthcare teams comprising subspecialists across multiple disciplines spread across increasingly larger inpatient facilities.⁴ During this same period, the evolution of mobile phones has led to dramatic shifts in personal communication patterns, with asynchronous text messaging replacing verbal communication.^5-7

In response to both the changing communication needs of clinicians and shifting cultural conventions, healthcare systems and providers alike have viewed text messaging as a solution to these growing communication problems. In fact, an entire industry has developed around “secure” and “Health Insurance Portability and Accountability Act (HIPAA)-compliant” text messaging platforms, which we will refer to below as secure text messaging systems (STMS). These systems offer benefits over carrier-based text messaging given their focus on the healthcare environment and HIPAA compliance. However, hospitals’ rapid adoption of these systems has outpaced our abilities to surveil, recognize, and understand the unintended consequences of transitioning to STMS communication in the hospital setting where failures in communication can be catastrophic. Below, we highlight three critical areas of concern encountered at our institutions and offer five potential mitigating strategies (Table).

Text Messaging is a Form of Alarm Fatigue

Text messaging renders clinicians vulnerable to a unique form of alarm fatigue. The burden of alarm fatigue has been well described in the literature and applies to interruptions to workflow in the electronic medical record and sensory alerts in clinical settings.^8,9 Text messaging serves as yet another interruption for healthcare providers. Without a framework to triage urgent versus nonurgent messages, a clinician can become inundated with information and miss critical messages. This can lead to delayed or incorrect responses and impede patient care. System design and implementation can also contribute to this phenomenon. For example, a text message analysis at one center identified how system and workflow design resulted in all messages to an intensive care unit team being routed to a single physician’s phone.¹⁰ This design left the singular physician at risk of information and task overload and at the mercy of endless interruptive alerts. Although this can occur with any communication system, it has been well demonstrated that adopting STMS correlates with an increased frequency of messaging, leading to an increase in interruptive alerts, which may have implications for patient safety.¹¹ This type of systems failure is silent unless proactively identified or revealed through a retrospective review of a resulting safety event.

Text Messaging Inappropriately Replaces Critical Communications that Should Happen in Person or by Phone

Text messaging has de-emphasized interpersonal communication skills and behaviors critical for quality and safety in hospital-based care. This concern emerges alongside evidence suggesting that new generations of physician trainees have profoundly different communication habits, preferences, and skillsets based on their experience in a text-heavy, asynchronous world of communication.¹² There is reason to worry that reliance on text messaging in healthcare leads to similar alterations in relationships and collaboration as it has in our broader cultural context.¹³ Academic medical centers in particular should attempt to mitigate the loss of profound and formative learning that occurs during face-to-face encounters between providers of different disciplines, experience levels, and specialties.

Text Messaging Increases the Risk of Communication Error

Finally, text messaging appears to be highly vulnerable to communication errors in the healthcare setting. Prior work emphasizes the importance of nonverbal communication in face-to-face and even voice-to-voice interactions, highlighting the loss of fidelity when using text-only methods to communicate.¹ Furthermore, the asynchronous nature of text messaging grants little room for clarification of minor misunderstandings that often arise in text-only communication through minor alterations in punctuation or automatic spelling corrections, a frequent occurrence when using medical terminology. Although a seasoned physician may be able to piece together the issues that deserve further clarification, young residents may be more hesitant to ask clarifying questions and determine the right course of action due to clinical inexperience.

Deliberate Design and Implementation

A recent systematic review identified a lack of high-quality evidence evaluating the impact of mobile technologies on communication and teamwork in hospital settings.¹⁴ This paucity of understanding renders communication via STMS in the healthcare setting uniquely vulnerable to latent safety threats unless the design and implementation of these systems are purposeful and proactive.

These concerns led us to postulate that deliberate and proactive implementation of these systems, rather than passive adoption, is needed in the healthcare environment. We propose a number of approaches and interventions that may guide institutions as they seek to implement STMS or redesign communication in the inpatient setting. At the core of these proposals lies an important tension: can implementation of STMS occur in isolation or should the arrival of these systems prompt an overhaul of an institution’s clinical communication system and culture?¹⁵

Proactive Surveillance

Surveillance is one proactive method for healthcare systems to understand where and how the implementation of STMS might lead to safety threats. From a quantitative standpoint, understanding the burden of messaging for each user across the system can reveal the clinical roles in the system that are particularly vulnerable to alert fatigue or information overload. Quality assurance monitoring of critical roles in the hospital (ie, airway emergency team, rapid response teams) could be conducted to ensure accurate directory listings at all times. Associating conversations with events, from serious safety events to near misses, could help leaders understand when and how text messaging contributes to safety events and create actionable learnings for safety learning systems.

Standardized Communication

A standardized language eliminates the burden of individuals to parse and translate each individual text message. A standardized algorithm for language, urgency, and expectations (ie, response before escalation) would help define the interaction in the clinical setting.¹⁶ Moving toward standardized, meaningful “quick messages,” one of our centers has implemented a campaign to “stick to the FACS,” where the following four standard quick messages are available for users: (1) “FYI no response needed,” (2) “ACTION needed within X min,” (3) “CONCERN can we talk or meet,” and (4) “STAT immediate response required.” These quick messages, developed with frontline stakeholders, represent the majority of requests exchanged by providers, and help standardize expectations and task prioritization.

Targeted Training

Targeted training and culture change efforts might help institutions counteract the broader impact of asynchronous messaging on communication skills and behaviors. Highlighting the contrast between clinical and casual communication with an emphasis on examples, scenarios, or role-playing has the potential to emphasize why and how clinical communication with STMS requires a careful, deliberate approach. For instance, safety culture training at one of our institutions features a scenario that illustrates the potential for miscommunication and missed connection between a nurse and a physician on the wards. The scenario gives way to discussion between participants about the shortcomings of text messaging and allows the facilitator to segue into the “dos and don’ts” of text messaging and when a phone call might be more appropriate.

Innovate

Finally, creatively harnessing the technology and data underlying these STMS may uncover methods to identify and mitigate communication errors in real time. For instance, using trigger methods to create a “ripple in the pond,” whereby a floor nurse reaching out with an urgent text automatically loops in the charge nurse of the unit. Building a chatbot or a virtual assistant functionality by leveraging user behavior patterns and natural language processing to provide text-based guidance to users might help busy clinicians connect to the key decision-makers on their team. For example, in response to an unanswered text, a virtual assistant might reach out to the waiting provider as follows: “you texted the resident 20 minutes ago and they haven’t replied, would you like to call the fellow instead?” The data-rich nature of these systems implies that they are ripe for automated solutions that can respond to behavioral- or text-based patterns to augment the existing operation and safety infrastructure.

The transition of healthcare communication systems toward STMS is already well underway. These systems, despite their flaws, are undoubtedly an improvement over legacy paging systems and, if properly implemented, offer several benefits to large healthcare systems. However, the communication needs in the healthcare setting are vastly different from the personal communication needs in everyday text messaging. As clinicians at the forefront of these transitions, we have the opportunity to critically assess the unique communication requirements in our hospital settings and help shape the way STMS are implemented in our hospitals. Pausing to deliberate about the limitations and the vulnerabilities of the current messaging systems for our acute clinical needs, including how they impact training and education, will allow us to proactively design and implement better communication systems that improve patient safety.

Over the past two decades, physicians and nurses practicing in hospital settings have faced an onslaught of challenges in communication, an area frequently cited as critical to providing safe and effective care to patients.^1-3 Communication needs have increased significantly as hospitalized patients have become more acute, complex, and technology-dependent, requiring larger healthcare teams comprising subspecialists across multiple disciplines spread across increasingly larger inpatient facilities.⁴ During this same period, the evolution of mobile phones has led to dramatic shifts in personal communication patterns, with asynchronous text messaging replacing verbal communication.^5-7

In response to both the changing communication needs of clinicians and shifting cultural conventions, healthcare systems and providers alike have viewed text messaging as a solution to these growing communication problems. In fact, an entire industry has developed around “secure” and “Health Insurance Portability and Accountability Act (HIPAA)-compliant” text messaging platforms, which we will refer to below as secure text messaging systems (STMS). These systems offer benefits over carrier-based text messaging given their focus on the healthcare environment and HIPAA compliance. However, hospitals’ rapid adoption of these systems has outpaced our abilities to surveil, recognize, and understand the unintended consequences of transitioning to STMS communication in the hospital setting where failures in communication can be catastrophic. Below, we highlight three critical areas of concern encountered at our institutions and offer five potential mitigating strategies (Table).

Text Messaging is a Form of Alarm Fatigue

Text messaging renders clinicians vulnerable to a unique form of alarm fatigue. The burden of alarm fatigue has been well described in the literature and applies to interruptions to workflow in the electronic medical record and sensory alerts in clinical settings.^8,9 Text messaging serves as yet another interruption for healthcare providers. Without a framework to triage urgent versus nonurgent messages, a clinician can become inundated with information and miss critical messages. This can lead to delayed or incorrect responses and impede patient care. System design and implementation can also contribute to this phenomenon. For example, a text message analysis at one center identified how system and workflow design resulted in all messages to an intensive care unit team being routed to a single physician’s phone.¹⁰ This design left the singular physician at risk of information and task overload and at the mercy of endless interruptive alerts. Although this can occur with any communication system, it has been well demonstrated that adopting STMS correlates with an increased frequency of messaging, leading to an increase in interruptive alerts, which may have implications for patient safety.¹¹ This type of systems failure is silent unless proactively identified or revealed through a retrospective review of a resulting safety event.

Text Messaging Inappropriately Replaces Critical Communications that Should Happen in Person or by Phone

Text messaging has de-emphasized interpersonal communication skills and behaviors critical for quality and safety in hospital-based care. This concern emerges alongside evidence suggesting that new generations of physician trainees have profoundly different communication habits, preferences, and skillsets based on their experience in a text-heavy, asynchronous world of communication.¹² There is reason to worry that reliance on text messaging in healthcare leads to similar alterations in relationships and collaboration as it has in our broader cultural context.¹³ Academic medical centers in particular should attempt to mitigate the loss of profound and formative learning that occurs during face-to-face encounters between providers of different disciplines, experience levels, and specialties.

Text Messaging Increases the Risk of Communication Error

Finally, text messaging appears to be highly vulnerable to communication errors in the healthcare setting. Prior work emphasizes the importance of nonverbal communication in face-to-face and even voice-to-voice interactions, highlighting the loss of fidelity when using text-only methods to communicate.¹ Furthermore, the asynchronous nature of text messaging grants little room for clarification of minor misunderstandings that often arise in text-only communication through minor alterations in punctuation or automatic spelling corrections, a frequent occurrence when using medical terminology. Although a seasoned physician may be able to piece together the issues that deserve further clarification, young residents may be more hesitant to ask clarifying questions and determine the right course of action due to clinical inexperience.

Deliberate Design and Implementation

A recent systematic review identified a lack of high-quality evidence evaluating the impact of mobile technologies on communication and teamwork in hospital settings.¹⁴ This paucity of understanding renders communication via STMS in the healthcare setting uniquely vulnerable to latent safety threats unless the design and implementation of these systems are purposeful and proactive.

These concerns led us to postulate that deliberate and proactive implementation of these systems, rather than passive adoption, is needed in the healthcare environment. We propose a number of approaches and interventions that may guide institutions as they seek to implement STMS or redesign communication in the inpatient setting. At the core of these proposals lies an important tension: can implementation of STMS occur in isolation or should the arrival of these systems prompt an overhaul of an institution’s clinical communication system and culture?¹⁵

Proactive Surveillance

Surveillance is one proactive method for healthcare systems to understand where and how the implementation of STMS might lead to safety threats. From a quantitative standpoint, understanding the burden of messaging for each user across the system can reveal the clinical roles in the system that are particularly vulnerable to alert fatigue or information overload. Quality assurance monitoring of critical roles in the hospital (ie, airway emergency team, rapid response teams) could be conducted to ensure accurate directory listings at all times. Associating conversations with events, from serious safety events to near misses, could help leaders understand when and how text messaging contributes to safety events and create actionable learnings for safety learning systems.

Standardized Communication

A standardized language eliminates the burden of individuals to parse and translate each individual text message. A standardized algorithm for language, urgency, and expectations (ie, response before escalation) would help define the interaction in the clinical setting.¹⁶ Moving toward standardized, meaningful “quick messages,” one of our centers has implemented a campaign to “stick to the FACS,” where the following four standard quick messages are available for users: (1) “FYI no response needed,” (2) “ACTION needed within X min,” (3) “CONCERN can we talk or meet,” and (4) “STAT immediate response required.” These quick messages, developed with frontline stakeholders, represent the majority of requests exchanged by providers, and help standardize expectations and task prioritization.

Targeted Training

Targeted training and culture change efforts might help institutions counteract the broader impact of asynchronous messaging on communication skills and behaviors. Highlighting the contrast between clinical and casual communication with an emphasis on examples, scenarios, or role-playing has the potential to emphasize why and how clinical communication with STMS requires a careful, deliberate approach. For instance, safety culture training at one of our institutions features a scenario that illustrates the potential for miscommunication and missed connection between a nurse and a physician on the wards. The scenario gives way to discussion between participants about the shortcomings of text messaging and allows the facilitator to segue into the “dos and don’ts” of text messaging and when a phone call might be more appropriate.

Innovate

Finally, creatively harnessing the technology and data underlying these STMS may uncover methods to identify and mitigate communication errors in real time. For instance, using trigger methods to create a “ripple in the pond,” whereby a floor nurse reaching out with an urgent text automatically loops in the charge nurse of the unit. Building a chatbot or a virtual assistant functionality by leveraging user behavior patterns and natural language processing to provide text-based guidance to users might help busy clinicians connect to the key decision-makers on their team. For example, in response to an unanswered text, a virtual assistant might reach out to the waiting provider as follows: “you texted the resident 20 minutes ago and they haven’t replied, would you like to call the fellow instead?” The data-rich nature of these systems implies that they are ripe for automated solutions that can respond to behavioral- or text-based patterns to augment the existing operation and safety infrastructure.

The transition of healthcare communication systems toward STMS is already well underway. These systems, despite their flaws, are undoubtedly an improvement over legacy paging systems and, if properly implemented, offer several benefits to large healthcare systems. However, the communication needs in the healthcare setting are vastly different from the personal communication needs in everyday text messaging. As clinicians at the forefront of these transitions, we have the opportunity to critically assess the unique communication requirements in our hospital settings and help shape the way STMS are implemented in our hospitals. Pausing to deliberate about the limitations and the vulnerabilities of the current messaging systems for our acute clinical needs, including how they impact training and education, will allow us to proactively design and implement better communication systems that improve patient safety.

Over the past two decades, physicians and nurses practicing in hospital settings have faced an onslaught of challenges in communication, an area frequently cited as critical to providing safe and effective care to patients.^1-3 Communication needs have increased significantly as hospitalized patients have become more acute, complex, and technology-dependent, requiring larger healthcare teams comprising subspecialists across multiple disciplines spread across increasingly larger inpatient facilities.⁴ During this same period, the evolution of mobile phones has led to dramatic shifts in personal communication patterns, with asynchronous text messaging replacing verbal communication.^5-7

In response to both the changing communication needs of clinicians and shifting cultural conventions, healthcare systems and providers alike have viewed text messaging as a solution to these growing communication problems. In fact, an entire industry has developed around “secure” and “Health Insurance Portability and Accountability Act (HIPAA)-compliant” text messaging platforms, which we will refer to below as secure text messaging systems (STMS). These systems offer benefits over carrier-based text messaging given their focus on the healthcare environment and HIPAA compliance. However, hospitals’ rapid adoption of these systems has outpaced our abilities to surveil, recognize, and understand the unintended consequences of transitioning to STMS communication in the hospital setting where failures in communication can be catastrophic. Below, we highlight three critical areas of concern encountered at our institutions and offer five potential mitigating strategies (Table).

Text Messaging is a Form of Alarm Fatigue

Text messaging renders clinicians vulnerable to a unique form of alarm fatigue. The burden of alarm fatigue has been well described in the literature and applies to interruptions to workflow in the electronic medical record and sensory alerts in clinical settings.^8,9 Text messaging serves as yet another interruption for healthcare providers. Without a framework to triage urgent versus nonurgent messages, a clinician can become inundated with information and miss critical messages. This can lead to delayed or incorrect responses and impede patient care. System design and implementation can also contribute to this phenomenon. For example, a text message analysis at one center identified how system and workflow design resulted in all messages to an intensive care unit team being routed to a single physician’s phone.¹⁰ This design left the singular physician at risk of information and task overload and at the mercy of endless interruptive alerts. Although this can occur with any communication system, it has been well demonstrated that adopting STMS correlates with an increased frequency of messaging, leading to an increase in interruptive alerts, which may have implications for patient safety.¹¹ This type of systems failure is silent unless proactively identified or revealed through a retrospective review of a resulting safety event.

Text Messaging Inappropriately Replaces Critical Communications that Should Happen in Person or by Phone

Text messaging has de-emphasized interpersonal communication skills and behaviors critical for quality and safety in hospital-based care. This concern emerges alongside evidence suggesting that new generations of physician trainees have profoundly different communication habits, preferences, and skillsets based on their experience in a text-heavy, asynchronous world of communication.¹² There is reason to worry that reliance on text messaging in healthcare leads to similar alterations in relationships and collaboration as it has in our broader cultural context.¹³ Academic medical centers in particular should attempt to mitigate the loss of profound and formative learning that occurs during face-to-face encounters between providers of different disciplines, experience levels, and specialties.

Text Messaging Increases the Risk of Communication Error

Finally, text messaging appears to be highly vulnerable to communication errors in the healthcare setting. Prior work emphasizes the importance of nonverbal communication in face-to-face and even voice-to-voice interactions, highlighting the loss of fidelity when using text-only methods to communicate.¹ Furthermore, the asynchronous nature of text messaging grants little room for clarification of minor misunderstandings that often arise in text-only communication through minor alterations in punctuation or automatic spelling corrections, a frequent occurrence when using medical terminology. Although a seasoned physician may be able to piece together the issues that deserve further clarification, young residents may be more hesitant to ask clarifying questions and determine the right course of action due to clinical inexperience.

Deliberate Design and Implementation

A recent systematic review identified a lack of high-quality evidence evaluating the impact of mobile technologies on communication and teamwork in hospital settings.¹⁴ This paucity of understanding renders communication via STMS in the healthcare setting uniquely vulnerable to latent safety threats unless the design and implementation of these systems are purposeful and proactive.

These concerns led us to postulate that deliberate and proactive implementation of these systems, rather than passive adoption, is needed in the healthcare environment. We propose a number of approaches and interventions that may guide institutions as they seek to implement STMS or redesign communication in the inpatient setting. At the core of these proposals lies an important tension: can implementation of STMS occur in isolation or should the arrival of these systems prompt an overhaul of an institution’s clinical communication system and culture?¹⁵

Proactive Surveillance

Surveillance is one proactive method for healthcare systems to understand where and how the implementation of STMS might lead to safety threats. From a quantitative standpoint, understanding the burden of messaging for each user across the system can reveal the clinical roles in the system that are particularly vulnerable to alert fatigue or information overload. Quality assurance monitoring of critical roles in the hospital (ie, airway emergency team, rapid response teams) could be conducted to ensure accurate directory listings at all times. Associating conversations with events, from serious safety events to near misses, could help leaders understand when and how text messaging contributes to safety events and create actionable learnings for safety learning systems.

Standardized Communication

A standardized language eliminates the burden of individuals to parse and translate each individual text message. A standardized algorithm for language, urgency, and expectations (ie, response before escalation) would help define the interaction in the clinical setting.¹⁶ Moving toward standardized, meaningful “quick messages,” one of our centers has implemented a campaign to “stick to the FACS,” where the following four standard quick messages are available for users: (1) “FYI no response needed,” (2) “ACTION needed within X min,” (3) “CONCERN can we talk or meet,” and (4) “STAT immediate response required.” These quick messages, developed with frontline stakeholders, represent the majority of requests exchanged by providers, and help standardize expectations and task prioritization.

Targeted Training

Targeted training and culture change efforts might help institutions counteract the broader impact of asynchronous messaging on communication skills and behaviors. Highlighting the contrast between clinical and casual communication with an emphasis on examples, scenarios, or role-playing has the potential to emphasize why and how clinical communication with STMS requires a careful, deliberate approach. For instance, safety culture training at one of our institutions features a scenario that illustrates the potential for miscommunication and missed connection between a nurse and a physician on the wards. The scenario gives way to discussion between participants about the shortcomings of text messaging and allows the facilitator to segue into the “dos and don’ts” of text messaging and when a phone call might be more appropriate.

Innovate

Finally, creatively harnessing the technology and data underlying these STMS may uncover methods to identify and mitigate communication errors in real time. For instance, using trigger methods to create a “ripple in the pond,” whereby a floor nurse reaching out with an urgent text automatically loops in the charge nurse of the unit. Building a chatbot or a virtual assistant functionality by leveraging user behavior patterns and natural language processing to provide text-based guidance to users might help busy clinicians connect to the key decision-makers on their team. For example, in response to an unanswered text, a virtual assistant might reach out to the waiting provider as follows: “you texted the resident 20 minutes ago and they haven’t replied, would you like to call the fellow instead?” The data-rich nature of these systems implies that they are ripe for automated solutions that can respond to behavioral- or text-based patterns to augment the existing operation and safety infrastructure.

The transition of healthcare communication systems toward STMS is already well underway. These systems, despite their flaws, are undoubtedly an improvement over legacy paging systems and, if properly implemented, offer several benefits to large healthcare systems. However, the communication needs in the healthcare setting are vastly different from the personal communication needs in everyday text messaging. As clinicians at the forefront of these transitions, we have the opportunity to critically assess the unique communication requirements in our hospital settings and help shape the way STMS are implemented in our hospitals. Pausing to deliberate about the limitations and the vulnerabilities of the current messaging systems for our acute clinical needs, including how they impact training and education, will allow us to proactively design and implement better communication systems that improve patient safety.

In the past five years, it has become increasingly apparent that hospital physiologic monitoring systems are not functioning optimally for children. On pediatric wards, 26%-48% of children are continuously monitored, and these children generate between 42 and 155 alarms per day.¹ Just 1% or fewer are considered actionable or informative, slowing nurses’ response times and placing patients at risk of delayed recognition of life-threatening events.^2,3 While some factors associated with alarm response times have been elucidated,³ in order to design safe and effective monitoring systems, further work is needed to understand the complex decision-making process that nurses face when encountering alarms outside a patient’s room. It is in this area that Schondelmeyer and colleagues strive to enhance our understanding in this issue of the Journal of Hospital Medicine.⁴

Schondelmeyer et al. conducted a single-center, observational study using mixed methods in a general pediatric unit. Trained observers shadowed nine nurses one to four times each, during which nurses were asked to “think aloud” as they managed physiologic monitor alarms, rationalizing their decisions about how and why they might respond for the observer to document. Observers accumulated 61 patient-hours of observation before investigators halted data collection because new insights about alarm responses were no longer emerging from the data (thematic saturation).

Nurses thought aloud about 207 alarms during the study, which the investigators estimated comprised about one third of the alarms that occurred during observation periods. Most of the 207 occurred while the nurse was already in the patient’s room, where a response decision is uncomplicated. More interesting were the 45 alarms heard while outside the patient’s room, where nurses face the complex decision of whether to interrupt their current tasks and respond or delay their response and assume the associated risk of nonresponse to a potentially deteriorating patient. Of the 45 alarms, nurses went into the room to evaluate the patient 15 times and, after doing so, reported that five of the 15 warranted in-person responses to address technical issues with the monitor, clinical issues, or patients’ comfort. Reassuring clinical contexts—such as presence of the medical team or family in the room and recent patient assessments—were the reasons most commonly provided to explain alarm nonresponse.

This study has two key limitations. First, the authors designed the study to observe nurses’ responses until thematic saturation was achieved. However, the small sample size (nine nurses, 45 out-of-room alarms) could raise questions about whether sufficient data were captured to make broadly generalizable conclusions, given the diverse range of patients, families, and clinical scenarios nurses encounter on an inpatient unit. Second, by instructing nurse participants to verbalize their rationale for response or nonresponse, investigators essentially asked nurses to override the “Type 1”, heuristic-based reasoning⁵ that research suggests regulates nursing responses to alarms when adapting to circumstances requiring high cognitive demand or a heavy workload.³ While innovative, it is possible that this approach prevented the investigators from fully achieving their stated objective of describing how bedside nurses think about and act upon alarms.

Nonetheless, the findings by Schondelmeyer and colleagues extend our emerging understanding of why alarm responses are disconcertingly slow. Nursing staff’s dismissal of monitor alarms that are discordant with a reassuring patient evaluation underscores the imperative to reduce nuisance alarms. Furthermore, the explicit statements justifying alarm nonresponse because of the presence of family members build upon prior findings of longer response times when family members are at the bedside³ and invite a provocative question: how would family members feel if they knew that they were being entrusted as a foundational component of safety monitoring in the hospital? In their recently published study conducted at the same hospital,⁶ Schondelmeyer’s team elicited perceptions that families are deeply concerned about staff nonresponse to alarms—as one nurse stated, parents “wonder what’s going on when no one comes in.” While there is a valuable role for integrating families into efforts to overcome threats to patient safety, as has been achieved with family error reporting⁷ and communication on family-centered rounds,⁸ this must occur in a structured, explicit, and deliberate manner, with families engaged as key stakeholders.

In summary, while Schondelmeyer and colleagues may not have exposed the depth of implicit thinking that governs nurses’ responses to alarms, they have highlighted the high-stakes decisions that nurses confront on a daily basis in an environment with exceedingly high alarm rates and low alarm actionability. The authors cite staff education among potential solutions to improve the safety of continuous monitoring, but such an intervention cannot be effective in a system that places impossible burdens on nurses. An openly family centered and multidisciplinary approach to reengineering the system for monitoring hospitalized children is needed to enable nurses to respond quickly and accurately to patients at risk of clinical deterioration.

Disclosures

The authors report no conflicts of interest.

In the past five years, it has become increasingly apparent that hospital physiologic monitoring systems are not functioning optimally for children. On pediatric wards, 26%-48% of children are continuously monitored, and these children generate between 42 and 155 alarms per day.¹ Just 1% or fewer are considered actionable or informative, slowing nurses’ response times and placing patients at risk of delayed recognition of life-threatening events.^2,3 While some factors associated with alarm response times have been elucidated,³ in order to design safe and effective monitoring systems, further work is needed to understand the complex decision-making process that nurses face when encountering alarms outside a patient’s room. It is in this area that Schondelmeyer and colleagues strive to enhance our understanding in this issue of the Journal of Hospital Medicine.⁴

Schondelmeyer et al. conducted a single-center, observational study using mixed methods in a general pediatric unit. Trained observers shadowed nine nurses one to four times each, during which nurses were asked to “think aloud” as they managed physiologic monitor alarms, rationalizing their decisions about how and why they might respond for the observer to document. Observers accumulated 61 patient-hours of observation before investigators halted data collection because new insights about alarm responses were no longer emerging from the data (thematic saturation).

Nurses thought aloud about 207 alarms during the study, which the investigators estimated comprised about one third of the alarms that occurred during observation periods. Most of the 207 occurred while the nurse was already in the patient’s room, where a response decision is uncomplicated. More interesting were the 45 alarms heard while outside the patient’s room, where nurses face the complex decision of whether to interrupt their current tasks and respond or delay their response and assume the associated risk of nonresponse to a potentially deteriorating patient. Of the 45 alarms, nurses went into the room to evaluate the patient 15 times and, after doing so, reported that five of the 15 warranted in-person responses to address technical issues with the monitor, clinical issues, or patients’ comfort. Reassuring clinical contexts—such as presence of the medical team or family in the room and recent patient assessments—were the reasons most commonly provided to explain alarm nonresponse.

This study has two key limitations. First, the authors designed the study to observe nurses’ responses until thematic saturation was achieved. However, the small sample size (nine nurses, 45 out-of-room alarms) could raise questions about whether sufficient data were captured to make broadly generalizable conclusions, given the diverse range of patients, families, and clinical scenarios nurses encounter on an inpatient unit. Second, by instructing nurse participants to verbalize their rationale for response or nonresponse, investigators essentially asked nurses to override the “Type 1”, heuristic-based reasoning⁵ that research suggests regulates nursing responses to alarms when adapting to circumstances requiring high cognitive demand or a heavy workload.³ While innovative, it is possible that this approach prevented the investigators from fully achieving their stated objective of describing how bedside nurses think about and act upon alarms.

Nonetheless, the findings by Schondelmeyer and colleagues extend our emerging understanding of why alarm responses are disconcertingly slow. Nursing staff’s dismissal of monitor alarms that are discordant with a reassuring patient evaluation underscores the imperative to reduce nuisance alarms. Furthermore, the explicit statements justifying alarm nonresponse because of the presence of family members build upon prior findings of longer response times when family members are at the bedside³ and invite a provocative question: how would family members feel if they knew that they were being entrusted as a foundational component of safety monitoring in the hospital? In their recently published study conducted at the same hospital,⁶ Schondelmeyer’s team elicited perceptions that families are deeply concerned about staff nonresponse to alarms—as one nurse stated, parents “wonder what’s going on when no one comes in.” While there is a valuable role for integrating families into efforts to overcome threats to patient safety, as has been achieved with family error reporting⁷ and communication on family-centered rounds,⁸ this must occur in a structured, explicit, and deliberate manner, with families engaged as key stakeholders.

In summary, while Schondelmeyer and colleagues may not have exposed the depth of implicit thinking that governs nurses’ responses to alarms, they have highlighted the high-stakes decisions that nurses confront on a daily basis in an environment with exceedingly high alarm rates and low alarm actionability. The authors cite staff education among potential solutions to improve the safety of continuous monitoring, but such an intervention cannot be effective in a system that places impossible burdens on nurses. An openly family centered and multidisciplinary approach to reengineering the system for monitoring hospitalized children is needed to enable nurses to respond quickly and accurately to patients at risk of clinical deterioration.

Disclosures

The authors report no conflicts of interest.

In the past five years, it has become increasingly apparent that hospital physiologic monitoring systems are not functioning optimally for children. On pediatric wards, 26%-48% of children are continuously monitored, and these children generate between 42 and 155 alarms per day.¹ Just 1% or fewer are considered actionable or informative, slowing nurses’ response times and placing patients at risk of delayed recognition of life-threatening events.^2,3 While some factors associated with alarm response times have been elucidated,³ in order to design safe and effective monitoring systems, further work is needed to understand the complex decision-making process that nurses face when encountering alarms outside a patient’s room. It is in this area that Schondelmeyer and colleagues strive to enhance our understanding in this issue of the Journal of Hospital Medicine.⁴

Schondelmeyer et al. conducted a single-center, observational study using mixed methods in a general pediatric unit. Trained observers shadowed nine nurses one to four times each, during which nurses were asked to “think aloud” as they managed physiologic monitor alarms, rationalizing their decisions about how and why they might respond for the observer to document. Observers accumulated 61 patient-hours of observation before investigators halted data collection because new insights about alarm responses were no longer emerging from the data (thematic saturation).

Nurses thought aloud about 207 alarms during the study, which the investigators estimated comprised about one third of the alarms that occurred during observation periods. Most of the 207 occurred while the nurse was already in the patient’s room, where a response decision is uncomplicated. More interesting were the 45 alarms heard while outside the patient’s room, where nurses face the complex decision of whether to interrupt their current tasks and respond or delay their response and assume the associated risk of nonresponse to a potentially deteriorating patient. Of the 45 alarms, nurses went into the room to evaluate the patient 15 times and, after doing so, reported that five of the 15 warranted in-person responses to address technical issues with the monitor, clinical issues, or patients’ comfort. Reassuring clinical contexts—such as presence of the medical team or family in the room and recent patient assessments—were the reasons most commonly provided to explain alarm nonresponse.

This study has two key limitations. First, the authors designed the study to observe nurses’ responses until thematic saturation was achieved. However, the small sample size (nine nurses, 45 out-of-room alarms) could raise questions about whether sufficient data were captured to make broadly generalizable conclusions, given the diverse range of patients, families, and clinical scenarios nurses encounter on an inpatient unit. Second, by instructing nurse participants to verbalize their rationale for response or nonresponse, investigators essentially asked nurses to override the “Type 1”, heuristic-based reasoning⁵ that research suggests regulates nursing responses to alarms when adapting to circumstances requiring high cognitive demand or a heavy workload.³ While innovative, it is possible that this approach prevented the investigators from fully achieving their stated objective of describing how bedside nurses think about and act upon alarms.

Nonetheless, the findings by Schondelmeyer and colleagues extend our emerging understanding of why alarm responses are disconcertingly slow. Nursing staff’s dismissal of monitor alarms that are discordant with a reassuring patient evaluation underscores the imperative to reduce nuisance alarms. Furthermore, the explicit statements justifying alarm nonresponse because of the presence of family members build upon prior findings of longer response times when family members are at the bedside³ and invite a provocative question: how would family members feel if they knew that they were being entrusted as a foundational component of safety monitoring in the hospital? In their recently published study conducted at the same hospital,⁶ Schondelmeyer’s team elicited perceptions that families are deeply concerned about staff nonresponse to alarms—as one nurse stated, parents “wonder what’s going on when no one comes in.” While there is a valuable role for integrating families into efforts to overcome threats to patient safety, as has been achieved with family error reporting⁷ and communication on family-centered rounds,⁸ this must occur in a structured, explicit, and deliberate manner, with families engaged as key stakeholders.

In summary, while Schondelmeyer and colleagues may not have exposed the depth of implicit thinking that governs nurses’ responses to alarms, they have highlighted the high-stakes decisions that nurses confront on a daily basis in an environment with exceedingly high alarm rates and low alarm actionability. The authors cite staff education among potential solutions to improve the safety of continuous monitoring, but such an intervention cannot be effective in a system that places impossible burdens on nurses. An openly family centered and multidisciplinary approach to reengineering the system for monitoring hospitalized children is needed to enable nurses to respond quickly and accurately to patients at risk of clinical deterioration.

Disclosures

The authors report no conflicts of interest.