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

Abstract

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.

Associations of physicians’ prescribing experience, work hours, and workload with prescription errors

“…29 These result in "alert fatigue," causing physicians to ignore these alerts altogether. 19,22,30 A probabilistic method for detecting prescription errors, such as the 1 used in this study, enables the use of an outlier detection mechanism to spot random errors that are hard to anticipate. It also reduces the alert burden and improves clinical relevance and accuracy by accounting for patient medical history, while personalizing the alerts.…”

“…This is evident by the high rate of false alerts, which results in "alert fatigue" and inevitably disrupts workflow. [19][20][21][22][23] A probabilistic method of error identification remains constant through all settings and is thus not biased by human error. This can enable considering objective aspects of the physician's condition at the time of prescribing, independent of his/her memory and perception and independent of pharmacists' interpretation.…”

Improving our reaction time – Using technology to identify transfusion reactions sooner

“…Although Villamin et al had some success increasing the frequency of transfusion reaction reporting with active surveillance, their hemovigilance model may be too expensive for other institutions to replicate. Many hospitals may not have resources available to recreate the action team model presented here, but the combination of an electronic alert with a response by an educated provider warrants our attention, as was highlighted by the SHOT conference report and verified recently by Rambiritch et al 19,20 Alerts on their own can be too easy to ignore 21 . The cost of transfusion reactions can be significant, 22–24 and while increased monitoring may not reduce the occurrence of reactions, the goal is to minimize the severity and harm to the patient.…”

“…Many hospitals may not have resources available to recreate the action team model presented here, but the combination of an electronic alert with a response by an educated provider warrants our attention, as was highlighted by the SHOT conference report and verified recently by Rambiritch et al 19,20 Alerts on their own can be too easy to ignore. 21 The cost of transfusion reactions can be significant, [22][23][24] and while increased monitoring may not reduce the occurrence of reactions, the goal is to minimize the severity and harm to the patient. The 2016 RED-III study reported a 1.1% rate of serious transfusion reactions, while a National Blood Collection and Utilization Survey (NBCUS) analysis found rates of lifethreatening reaction to be 9.4 per 100,000 transfusions in 2015 and 4.9 per 100,000 in 2017.…”

An Alternate Viewpoint on Information Sharing: There is no Paradox

“…The concerns expressed about alert fatigue reflect a sad and true reality, but the failures of modern alerting systems are not a result of data or data volume, but rather the failure of commercial EHRs to manage and present these data appropriately. [3][4][5] The authors are also correct in stating that the information blocking rule "amplifies the burden of information reconciliation on information-receiving health care entities," but that is not a paradox. That will always be true when a clinician receives new information.…”