Search bioRxivSearch

bioRxiv · 10.1101/640318

Reducing Unintentional Duplicate Orders using Passive Inline Just-in-Time Decision Support

Abstract

ImportanceElectronic health records (EHRs) allow teams of clinicians to simultaneously care for patients but an unintended consequence could result in duplicate ordering of tests and medications.\n\nObjectiveWe asked if a simple visual aid would reduce duplicate ordering of tests and medications for busy teams of clinicians in our emergency department by placing a red highlight around the checkbox of a computer-based order if previously ordered.\n\nDesignWe performed an interrupted time series to analyze all patient visits 1 year before and 1 year after the intervention. Significance testing was performed using a negative binomial regression with Newey-West standard errors, correcting for patient level variables and environmental variables that might be associated with duplicate orders.\n\nSettingThe emergency department of an academic hospital in Boston, MA with 55,000 visits annually.\n\nParticipants184,722 consecutive emergency department patients.\n\nExposureIf an order had previously been placed during that ED visit, we cue the user by showing a red highlight around the checkbox of that order.\n\nMain OutcomeNumber of unintentional duplicate orders.\n\nResultsAfter deployment of the non-interrupting nudge, the rate of unintentional duplicates for laboratory orders decreased 49% (incidence rate ratio 0.51, 95% CI 0.45-0.59) and for radiology orders decreased with an incidence rate ratio of 0.60 (0.44-0.82). There was no change in unintentional medication duplicate orders. We estimated that the nudge eliminated 17,936 clicks in our EHR.\n\nConclusions and RelevancePassive visual queues that provide just-in-time decision support are effective, not disruptive of workflow, and may decrease alert fatigue in busy clinical environments.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSCan a simple visual aid reduce duplicate ordering in an electronic health record?\n\nFindingsIn this interrupted time series, the rate of unintentional duplicates for laboratory orders decreased 49% and for radiology orders decreased 40%. There was no change in unintentional medication duplicate orders. We estimated that the nudge eliminated 17,936 clicks in our EHR.\n\nMeaningQuality improvement often relies on changing clinician behavior. We believe guiding clinicians to a right action is better than telling the clinician they have already made an error. Our approach will help reduce alert fatigue and lessen clinician complaints about EHRs.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Horng, S., Joseph, J., Calder, S., Stevens, J. P., O'Donoghue, A. L., Safran, C., Nathanson, L. A., Leventhal, E.. 2019-05-17. Reducing Unintentional Duplicate Orders using Passive Inline Just-in-Time Decision Support. https://doi.org/10.1101/640318

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics