Alert override as a habitual behavior – a new perspective on a persistent problem

Baysari, Melissa T.; Tariq, Amina; Day, Richard O.; Westbrook, Johanna I.

doi:10.1093/jamia/ocw072

Cited by 54 publications

(49 citation statements)

References 24 publications

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“…For example, in this study the cat-▶Table 2 Drug interaction pairs with at least one severe rating and a range that differed by 2 or more categories. [90]. Automation bias may also occur, with some prescribers becoming over-reliant on the drug interaction database program to detect potential DDI at the exclusion of clinical judgement [91].…”

Section: Discussionmentioning

confidence: 99%

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

Monteith

Glenn²,

Gitlin

et al. 2020

Pharmacopsychiatry

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Background Patients with bipolar disorder frequently experience polypharmacy, putting them at risk for clinically significant drug-drug interactions (DDI). Online drug interaction database programs are used to alert physicians, but there are no internationally recognized standards to define DDI. This study compared the category of potential DDI returned by 6 commercial drug interaction database programs for drug interaction pairs involving drugs commonly prescribed for bipolar disorder. Methods The category of potential DDI provided by 6 drug interaction database programs (3 subscription, 3 open access) was obtained for 125 drug interaction pairs. The pairs involved 103 drugs (38 psychiatric, 65 nonpsychiatric); 88 pairs included a psychiatric and nonpsychiatric drug; 37 pairs included 2 psychiatric drugs. Every pair contained at least 1 mood stabilizer or antidepressant. The category provided by 6 drug interaction database programs was compared using percent agreement and Fleiss kappa statistic of interrater reliability. Results For the 125 drug pairs, the overall percent agreement among the 6 drug interaction database programs was 60%; the Fleiss kappa agreement was slight. For drug interaction pairs with any category rating of severe (contraindicated), the kappa agreement was moderate. For drug interaction pairs with any category rating of major, the kappa agreement was slight. Conclusion There is poor agreement among drug interaction database programs for the category of potential DDI involving psychiatric drugs. Drug interaction database programs provide valuable information, but the lack of consistency should be recognized as a limitation. When assistance is needed, physicians should check more than 1 drug interaction database program.

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Section: Discussionmentioning

confidence: 99%

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

Monteith

Glenn²,

Gitlin

et al. 2020

Pharmacopsychiatry

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“…High-alert volume can lead to alert fatigue which contributes to increased mental workload, potential patient harm via workarounds, and mistakes in ordering and treatment. 17,57,58 There are no nationally-developed, endorsed standards for which alerts are appropriate and which are not, which ones should be interruptive and which ones should be passive. Some proposals are gaining acceptance, such as the list of DDIs that should be interruptive.…”

Section: Discussionmentioning

confidence: 99%

“…The more alerts one experiences, the more likely one is to ignore them. 17 Clinicians overrode drug allergy alerts appearing two or more times more frequently than drug allergy alerts that appeared only once. 11 Reminder alerts to perform certain tasks were also less likely to be heeded with increasing numbers of alerts presented 16 as were responses to clinical trial recruitment alerts.…”

Section: Background and Significancementioning

confidence: 99%

“…Although feedback, after the alert is live, can lead to adjustments that improve an alert's performance, this is only after the potentially poorly performing alert has been live in the system with the associated risks that it could cause unintended patient harm and negative impressions on clinicians. 17 Finally, the task of correcting a problematic alert postdeployment can be time sensitive and is less likely to enable a thoughtful work environment for informaticists relative to the more planned and deliberative conditions predeployment. Is there a better way to test alerts?…”

Section: Alert Testingmentioning

confidence: 99%

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Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems

et al. 2020

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Background Electronic health record (EHR) alert fatigue, while widely recognized as a concern nationally, lacks a corresponding comprehensive mitigation plan. Objectives The goal of this manuscript is to provide practical guidance to clinical informaticists and other health care leaders who are considering creating a program to manage EHR alerts. Methods This manuscript synthesizes several approaches and recommendations for better alert management derived from four U.S. health care institutions that presented their experiences and recommendations at the American Medical Informatics Association 2019 Clinical Informatics Conference in Atlanta, Georgia, United States. The assembled health care institution leaders represent academic, pediatric, community, and specialized care domains. We describe governance and management, structural concepts and components, and human–computer interactions with alerts, and make recommendations regarding these domains based on our experience supplemented with literature review. This paper focuses on alerts that impact bedside clinicians. Results The manuscript addresses the range of considerations relevant to alert management including a summary of the background literature about alerts, alert governance, alert metrics, starting an alert management program, approaches to evaluating alerts prior to deployment, and optimization of existing alerts. The manuscript includes examples of alert optimization successes at two of the represented institutions. In addition, we review limitations on the ability to evaluate alerts in the current state and identify opportunities for further scholarship. Conclusion Ultimately, alert management programs must strive to meet common goals of improving patient care, while at the same time decreasing the alert burden on clinicians. In so doing, organizations have an opportunity to promote the wellness of patients, clinicians, and EHRs themselves.

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“…2,3 Moreover, when alerts fire too often, they cause alert fatigue, decreasing the effectiveness of these alerts with time. [3][4][5] Nevertheless, utilization of EHR-based alerts is increasing as a means of EHR decision support. 3,[6][7][8][9] Objectives To date, studies of interruptive alerts have generally focused on the overall frequency of the alert and/or response (acceptance vs. rejection) to them.…”

Section: Background and Significancementioning

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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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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Alert override as a habitual behavior – a new perspective on a persistent problem

Cited by 54 publications

References 24 publications

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems

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

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