Dataset Information

Distinct components of alert fatigue in physicians' responses to a noninterruptive clinical decision support alert.

ABSTRACT:

Objective

Clinical decision support (CDS) alerts may improve health care quality but "alert fatigue" can reduce provider responsiveness. We analyzed how the introduction of competing alerts affected provider adherence to a single depression screening alert.

Materials and methods

We analyzed the audit data from all occurrences of a CDS alert at a large academic health system. For patients who screen positive for depression during ambulatory visits, a noninterruptive alert was presented, offering a number of relevant documentation actions. Alert adherence was defined as the selection of any option offered within the alert. We assessed the effect of competing clinical guidance alerts presented during the same encounter and the total of all CDS alerts that the same provider had seen in the prior 90 days, on the probability of depression screen alert adherence, adjusting for physician and patient characteristics.

Results

The depression alert fired during 55 649 office visits involving 418 physicians and 40 474 patients over 41 months. After adjustment, physicians who had seen the most alerts in the prior 90 days were much less likely to respond (adjusted OR highest-lowest quartile, 0.38; 95% CI 0.35-0.42; P < .001). Competing alerts in the same visit further reduced the likelihood of adherence only among physicians in the middle two quartiles of alert exposure in the prior 90 days.

Conclusions

Adherence to a noninterruptive depression alert was strongly associated with the provider's cumulative alert exposure over the past quarter. Health systems should monitor providers' recent alert exposure as a measure of alert fatigue.

SUBMITTER: Murad DA

PROVIDER: S-EPMC9748542 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Distinct components of alert fatigue in physicians' responses to a noninterruptive clinical decision support alert.

Murad Douglas A DA Tsugawa Yusuke Y Elashoff David A DA Baldwin Kevin M KM Bell Douglas S DS

Journal of the American Medical Informatics Association : JAMIA 20221201 1

<h4>Objective</h4>Clinical decision support (CDS) alerts may improve health care quality but "alert fatigue" can reduce provider responsiveness. We analyzed how the introduction of competing alerts affected provider adherence to a single depression screening alert.<h4>Materials and methods</h4>We analyzed the audit data from all occurrences of a CDS alert at a large academic health system. For patients who screen positive for depression during ambulatory visits, a noninterruptive alert was prese ...[more]

PMID: 36264258

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Project description:BackgroundRecognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas.ObjectivesOur dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders.MethodsIterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts.ResultsAt baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to -1.3 minutes.ConclusionCDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.

Project description:BackgroundChronic kidney disease (CKD) is common and associated with adverse clinical outcomes. Most care for early CKD is provided in primary care, including hypertension (HTN) management. Computerized clinical decision support (CDS) can improve the quality of care for CKD but can also cause alert fatigue for primary care physicians (PCPs). Computable phenotypes (CPs) are algorithms to identify disease populations using, for example, specific laboratory data criteria.ObjectivesOur objective was to determine the feasibility of implementation of CDS alerts by developing CPs and estimating potential alert burden.MethodsWe utilized clinical guidelines to develop a set of five CPs for patients with stage 3 to 4 CKD, uncontrolled HTN, and indications for initiation or titration of guideline-recommended antihypertensive agents. We then conducted an iterative data analytic process consisting of database queries, data validation, and subject matter expert discussion, to make iterative changes to the CPs. We estimated the potential alert burden to make final decisions about the scope of the CDS alerts. Specifically, the number of times that each alert could fire was limited to once per patient.ResultsIn our primary care network, there were 239,339 encounters for 105,992 primary care patients between April 1, 2018 and April 1, 2019. Of these patients, 9,081 (8.6%) had stage 3 and 4 CKD. Almost half of the CKD patients, 4,191 patients, also had uncontrolled HTN. The majority of CKD patients were female, elderly, white, and English-speaking. We estimated that 5,369 alerts would fire if alerts were triggered multiple times per patient, with a mean number of alerts shown to each PCP ranging from 0.07-to 0.17 alerts per week.ConclusionDevelopment of CPs and estimation of alert burden allows researchers to iteratively fine-tune CDS prior to implementation. This method of assessment can help organizations balance the tradeoff between standardization of care and alert fatigue.

Project description:BackgroundComputerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far.ObjectiveOur objective was to develop machine learning prediction models to predict physicians' responses in order to reduce alert fatigue from disease medication-related CDSSs.MethodsWe collected data from a disease medication-related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets.ResultsA total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively.ConclusionsIn this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication-related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

Dataset Information

Distinct components of alert fatigue in physicians' responses to a noninterruptive clinical decision support alert.

Objective

Materials and methods

Results

Conclusions

Publications

Distinct components of alert fatigue in physicians' responses to a noninterruptive clinical decision support alert.

Similar Datasets

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