The Effects of Medication Alerts on Prescriber Response in a Pediatric Hospital

Dexheimer, Judith W.; Kirkendall, Eric S.; Kouril, Michal; Hagedorn, Philip; Minich, Thomas; Duan, Leo L.; Mahdi, Cecilia M.; Szczesniak, Rhonda; Spooner, S. Andrew

doi:10.4338/aci-2016-10-ra-0168

Cited by 22 publications

(25 citation statements)

References 40 publications

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“…Rather, alert fatigue may be more related to distraction from other tasks and the interruption from workflow. [13][14][15][16][17] Interruptions in workflow are known to be a significant driver in orders being placed on incorrect patients and other patient safety events. [18][19][20] While the cumulative time burden on providers for alerts may underestimate providers' perceived burden of alerts, assessing the time providers spend with alerts may lead to important insights about the potential effectiveness of an alert.…”

Section: Discussionmentioning

confidence: 99%

“…It is likely that a large proportion of alerts closed in <2 seconds are due to habituated responses with limited conscious intention. 15 If this is the case, the frequency of interruption from these alerts, and resulting frustration by providers who are interrupted, may not be leading to any net clinical benefit. Dwell time may correspond to the clinical importance of the alerts.…”

Section: Discussionmentioning

confidence: 99%

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Evaluating the Impact of Interruptive Alerts within a Health System: Use, Response Time, and Cumulative Time Burden

et al. 2019

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Background Health systems often employ interruptive alerts through the electronic health record to improve patient care. However, concerns of “alert fatigue” have been raised, highlighting the importance of understanding the time burden and impact of these alerts on providers. Objectives Our main objective was to determine the total time providers spent on interruptive alerts in both inpatient and outpatient settings. Our secondary objectives were to analyze dwell time for individual alerts and examine both provider and alert-related factors associated with dwell time variance. Methods We retrospectively evaluated use and response to the 75 most common interruptive (“popup”) alerts between June 1st, 2015 and November 1st, 2016 in a large academic health care system. Alert “dwell time” was calculated as the time between the alert appearing on a provider's screen until it was closed. The total number of alerts and dwell times per provider per month was calculated for inpatient and outpatient alerts and compared across alert type. Results The median number of alerts seen by a provider was 12 per month (IQR 4–34). Overall, 67% of inpatient and 39% of outpatient alerts were closed in under 3 seconds. Alerts related to patient safety and those requiring more than a single click to proceed had significantly longer median dwell times of 5.2 and 6.7 seconds, respectively. The median total monthly time spent by providers viewing alerts was 49 seconds on inpatient alerts and 28 seconds on outpatient alerts. Conclusion Most alerts were closed in under 3 seconds and a provider's total time spent on alerts was less than 1 min/mo. Alert fatigue may lie in their interruptive and noncritical nature rather than time burden. Monitoring alert interaction time can function as a valuable metric to assess the impact of alerts on workflow and potentially identify routinely ignored alerts.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluating the Impact of Interruptive Alerts within a Health System: Use, Response Time, and Cumulative Time Burden

et al. 2019

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“…11 In a retrospective study at a pediatric hospital medical center, fewer alerts (49 alerts/day/ per provider) were associated with increasing the number of prescribers taking corrective action on the order that generated an alert. 12 Next, a health care system needs to have a strategy to consider all categories of drug alerts available. While some generalized principles of CDS can be easily applies to drug alerting, some strategies for drug alerting CDS are unique.…”

Section: Discussionmentioning

confidence: 99%

Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems

2019

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Background Concerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts. Objectives This article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts. Methods Standardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary. Results Drug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting. Conclusion A team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts.

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“…Another limitation (which is broader than our project) is the lack of alert metrics to easily identify potentially inappropriate DDI alerts. 23,24,30 Although the frequency and override rate can be easily calculated, their ability to reveal alert appropriateness without further clinician review is poor. 9,10 Alert frequency and override rate do not provide insight into an alert's clinical use because clinicians may still value some alerts that occur frequently but are still overridden.…”

Section: Figurementioning

confidence: 99%

Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach

Daniels¹,

Burlison²,

Baker

et al. 2019

Pediatrics

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OBJECTIVES: Excessive alerts are a common concern associated with clinical decision support systems that monitor drug-drug interactions (DDIs). To reduce the number of low-value interruptive DDI alerts at our hospital, we implemented an iterative, multidimensional quality improvement effort, which included an interdisciplinary advisory group, alert metrics, and measurement of perceived clinical value.

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The Effects of Medication Alerts on Prescriber Response in a Pediatric Hospital

Cited by 22 publications

References 40 publications

Evaluating the Impact of Interruptive Alerts within a Health System: Use, Response Time, and Cumulative Time Burden

Evaluating the Impact of Interruptive Alerts within a Health System: Use, Response Time, and Cumulative Time Burden

Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems

Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach

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