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Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review.

ABSTRACT: OBJECTIVE:Alert fatigue limits the effectiveness of medication safety alerts, a type of computerized clinical decision support (CDS). Researchers have suggested alternative interactive designs, as well as tailoring alerts to clinical roles. As examples, alerts may be tiered to convey risk, and certain alerts may be sent to pharmacists. We aimed to evaluate which variants elicit less alert fatigue. MATERIALS AND METHODS:We searched for articles published between 2007 and 2017 using the PubMed, Embase, CINAHL, and Cochrane databases. We included articles documenting peer-reviewed empirical research that described the interactive design of a CDS system, to which clinical role it was presented, and how often prescribers accepted the resultant advice. Next, we compared the acceptance rates of conventional CDS-presenting prescribers with interruptive modal dialogs (ie, "pop-ups")-with alternative designs, such as role-tailored alerts. RESULTS:Of 1011 articles returned by the search, we included 39. We found different methods for measuring acceptance rates; these produced incomparable results. The most common type of CDS-in which modals interrupted prescribers-was accepted the least often. Tiering by risk, providing shortcuts for common corrections, requiring a reason to override, and tailoring CDS to match the roles of pharmacists and prescribers were the most common alternatives. Only 1 alternative appeared to increase prescriber acceptance: role tailoring. Possible reasons include the importance of etiquette in delivering advice, the cognitive benefits of delegation, and the difficulties of computing "relevance." CONCLUSIONS:Alert fatigue may be mitigated by redesigning the interactive behavior of CDS and tailoring CDS to clinical roles. Further research is needed to develop alternative designs, and to standardize measurement methods to enable meta-analyses.

SUBMITTER: Hussain MI

PROVIDER: S-EPMC6748819 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review.

Hussain Mustafa I MI Reynolds Tera L TL Zheng Kai K

Journal of the American Medical Informatics Association : JAMIA 20191001 10

<h4>Objective</h4>Alert fatigue limits the effectiveness of medication safety alerts, a type of computerized clinical decision support (CDS). Researchers have suggested alternative interactive designs, as well as tailoring alerts to clinical roles. As examples, alerts may be tiered to convey risk, and certain alerts may be sent to pharmacists. We aimed to evaluate which variants elicit less alert fatigue.<h4>Materials and methods</h4>We searched for articles published between 2007 and 2017 using ...[more]

PMID: 31206159

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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.

Project description:BackgroundIntegrated reminders within clinical systems have become more prevalent due to the use of electronic health records and evidence demonstrating an increase in compliance within practice. Clinical reminders are assessed for effectiveness on an individual basis, rather than in combination with existing prompts for other conditions. The growing number of prompts may be counter-productive as healthcare professionals are increasingly suffering from "reminder fatigue" meaning many reminders are ignored. This work will review the qualitative evidence to identify barriers and enablers of existing prompts found within computerised decision support systems. Our focus will be on primary care where clinicians have to negotiate a plethora of reminders as they deal with increasingly complex patients and sophisticated treatment regimes. The review will provide a greater understanding of existing systems and the way clinicians interact with them to inform the development of more effective and targeted clinical reminders.MethodsA comprehensive search using piloted terms will be used to identify relevant literature from 1960 (or commencement of database) to 2017. MEDLINE, MEDLINE In Process, EMBASE, HMIC, PsycINFO, CDSR DARE, HTA, CINAHL and CPCI, will be searched, as well as grey literature and references and citations of included papers. Manuscripts will be assessed for eligibility, bias and quality using the CASP tool with narrative data being included and questionnaire based studies excluded. Inductive thematic analysis will be performed in order to produce a conceptual framework defining the key barriers around integrated clinical reminders.DiscussionIndications of alert and reminder fatigue are found throughout the current literature. However, this has not been fully investigated using a robust qualitative approach, particularly in a rapidly growing body of evidence. This review will aid people forming new clinical systems so that alerts can be incorporated appropriately.Systematic review registrationPROSPERO: CRD42016029418.

Dataset Information

Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review.

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Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review.

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