OBJECTIVE: State agencies have developed reporting systems of safety events that include events related to health information technology (HIT). These data come from hospital reporting systems where staff submit safety reports and nurses, in the role of safety managers, review, and code events. Safety managers may have varying degrees of experience with identifying events related to HIT. Our objective was to review events potentially involving HIT and compare those with what was reported to the state.

METHODS: We performed a structured review of 1 year of safety events from an academic pediatric healthcare system. We reviewed the free-text description of each event and applied a classification scheme derived from the AHRQ Health IT Hazard Manager and compared the results with events reported to the state as involving HIT.

RESULTS: Of 33,218 safety events for a 1-year period, 1247 included key words related to HIT and/or were indicated by safety managers as involving HIT. Of the 1247 events, the structured review identified 769 as involving HIT. In comparison, safety managers only identified 194 of the 769 events (25%) as involving HIT. Most events, 353 (46%), not identified by safety managers were documentation issues. Of the 1247 events, the structured review identified 478 as not involving HIT while safety managers identified and reported 81 of these 478 events (17%) as involving HIT.

CONCLUSIONS: The current process of reporting safety events lacks standardization in identifying health technology contributions to safety events, which may minimize the effectiveness of safety initiatives.

<p><strong>BACKGROUND: </strong>Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.</p>

<p><strong>OBJECTIVE: </strong>(1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.</p>

<p><strong>MATERIALS AND METHODS: </strong>We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric.</p>

<p><strong>RESULTS: </strong>Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.</p>

<p><strong>CONCLUSION: </strong>Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.</p>

<p><strong>INTRODUCTION: </strong>Pulse oximetry monitoring is prescribed to children receiving home oxygen for chronic medical conditions associated with hypoxemia. Although home pediatric pulse oximetry is supported by national organizations, there are a lack of guidelines outlining indications and prescribing parameters.</p>

<p><strong>METHODS: </strong>A mixed-methods analysis of pediatric home pulse oximetry orders prescribed through the institutional home health care provider at a large US children's hospital 6/2018-7/2019 were retrospectively reviewed to determine prescribed alarm parameter limits and recommended interventions. Semi-structured qualitative interviews with pediatric providers managing patients receiving home oxygen and pulse oximetry were conducted to identify opportunities to improve home pulse oximetry prescribing practices. Interviews were analyzed using a modified content analysis approach to identify recurring themes.</p>

<p><strong>RESULTS: </strong>368 children received home pulse oximetry orders. Orders were most frequently prescribed on non-cardiac medical floors (32%). Attending physicians were the most frequent ordering providers (52%). Frequency of use was prescribed in 96% of orders, however just 70% were provided with specific instructions for interventions when alarms occurred. Provider role and clinical setting were significantly associated with the presence of a care plan. Provider interviews identified opportunities for improvement with the device, management of alarm parameter limits, and access to home monitor data.</p>

<p><strong>DISCUSSION: </strong>This study demonstrated significant variability in home pulse oximetry prescribing practices. Provider interviews highlighted the importance of the provider-patient relationship and areas for improvement. There is an opportunity to create standardized guidelines that optimize the use of home monitoring devices for patients, families, and pulmonary providers. This article is protected by copyright. All rights reserved.</p>

<p><strong>BACKGROUND AND OBJECTIVES: </strong>Physiologic monitor alarms occur at high rates in children's hospitals; ≤1% are actionable. The burden of alarms has implications for patient safety and is challenging to measure directly. Nurse workload, measured by using a version of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) validated among nurses, is a useful indicator of work burden that has been associated with patient outcomes. A recent study revealed that 5-point increases in the NASA-TLX score were associated with a 22% increased risk in missed nursing care. Our objective was to measure the relationship between alarm count and nurse workload by using the NASA-TLX.</p>

<p><strong>METHODS: </strong>We conducted a repeated cross-sectional study of pediatric nurses in a tertiary care children's hospital to measure the association between NASA-TLX workload evaluations (using the nurse-validated scale) and alarm count in the 2 hours preceding NASA-TLX administration. Using a multivariable mixed-effects regression accounting for nurse-level clustering, we modeled the adjusted association of alarm count with workload.</p>

<p><strong>RESULTS: </strong>The NASA-TLX score was assessed in 26 nurses during 394 nursing shifts over a 2-month period. In adjusted regression models, experiencing &gt;40 alarms in the preceding 2 hours was associated with a 5.5 point increase (95% confidence interval 5.2 to 5.7; &lt; .001) in subjective workload.</p>

<p><strong>CONCLUSION: </strong>Alarm count in the preceding 2 hours is associated with a significant increase in subjective nurse workload that exceeds the threshold associated with increased risk of missed nursing care and potential patient harm.</p>

<p><strong>Importance: </strong>Incoming text messages and calls on nurses' mobile telephones may interrupt medication administration, but whether such interruptions are associated with errors has not been established.</p>

<p><strong>Objective: </strong>To assess whether a temporal association exists between mobile telephone interruptions and subsequent errors by pediatric intensive care unit (PICU) nurses during medication administration.</p>

<p><strong>Design, Setting, and Participants: </strong>A retrospective cohort study was performed using telecommunications and electronic health record data from a PICU in a children's hospital. Data were collected from August 1, 2016, through September 30, 2017. Participants included 257 nurses and the 3308 patients to whom they administered medications.</p>

<p><strong>Exposures: </strong>Primary exposures were incoming telephone calls and text messages received on the institutional mobile telephone assigned to the nurse in the 10 minutes leading up to a medication administration attempt. Secondary exposures were the nurse's PICU experience, work shift (day vs night), nurse to patient ratio, and level of patient care required.</p>

<p><strong>Main Outcomes and Measures: </strong>Primary outcome, errors during medication administration, was a composite of reported medication administration errors and bar code medication administration error alerts generated when nurses attempted to give medications without active orders for the patient whose bar code they scanned.</p>

<p><strong>Results: </strong>Participants included 257 nurses, of whom 168 (65.4%) had 6 months or more of PICU experience; and 3308 patients, of whom 1839 (55.6%) were male, 1539 (46.5%) were white, and 2880 (87.1%) were non-Hispanic. The overall rate of errors during 238 540 medication administration attempts was 3.1% (95% CI, 3.0%-3.3%) when nurses were uninterrupted by incoming telephone calls and 3.7% (95% CI, 3.4%-4.0%) when they were interrupted by such calls. During day shift, the odds ratios (ORs) for error when interrupted by calls (compared with uninterrupted) were 1.02 (95% CI, 0.92-1.13; P = .73) among nurses with 6 months or more of PICU experience and 1.22 (95% CI, 1.00-1.47; P = .046) among nurses with less than 6 months of experience. During night shift, the ORs for error when interrupted by calls were 1.35 (95% CI, 1.16-1.57; P &lt; .001) among nurses with 6 months or more of PICU experience and 1.53 (95% CI, 1.16-2.03; P = .003) among nurses with less than 6 months of experience. Nurses administering medications to 1 or more patients receiving mechanical ventilation and arterial catheterization while caring for at least 1 other patient had an increased risk of error (OR, 1.21; 95% CI, 1.03-1.42; P = .02). Incoming text messages were not associated with error (OR, 0.97; 95% CI, 0.92-1.02; P = .22).</p>

<p><strong>Conclusions and Relevance: </strong>This study's findings suggest that incoming telephone call interruptions may be temporally associated with medication administration errors among PICU nurses. Risk of error varied by shift, experience, nurse to patient ratio, and level of patient care required.</p>