Dataset Information

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

ABSTRACT: Objective:To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods:We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results:We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion:Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion:CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

SUBMITTER: Wright A

PROVIDER: S-EPMC6019061 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

Wright Adam A Ai Angela A Ash Joan J Wiesen Jane F JF Hickman Thu-Trang T TT Aaron Skye S McEvoy Dustin D Borkowsky Shane S Dissanayake Pavithra I PI Embi Peter P Galanter William W Harper Jeremy J Kassakian Steve Z SZ Ramoni Rachel R Schreiber Richard R Sirajuddin Anwar A Bates David W DW Sittig Dean F DF

Journal of the American Medical Informatics Association : JAMIA 20180501 5

<h4>Objective</h4>To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions.<h4>Materials and methods</h4>We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round ...[more]

PMID: 29045651

Similar Datasets

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

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

Publications

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets