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  1. Article ; Online: Atypical symptoms in emergency department patients with urosepsis challenge current urinary tract infection management guidelines.

    Biebelberg, Brett / Kehoe, Iain E / Zheng, Hui / O'Connell, Abigail / Filbin, Michael R / Heldt, Thomas / Reisner, Andrew T

    Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

    2024  

    Language English
    Publishing date 2024-04-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1329813-6
    ISSN 1553-2712 ; 1069-6563
    ISSN (online) 1553-2712
    ISSN 1069-6563
    DOI 10.1111/acem.14914
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4479: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: End-user evaluation of an interface for clinical decision support using predictive algorithms.

    Kehoe, Iain E / Pepino, Jeremy A / Lee, Jarone / Hahn, Jin-Oh / Reisner, Andrew T

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2022  Volume 2022, Page(s) 1149–1151

    Abstract: There have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user- ... ...

    Abstract There have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user-friendly interface that integrates into clinical workflows. Complicated or non-intuitive displays may confuse users and may even increase patient management errors. We recently developed a graphical user interface (GUI) intended to integrate a predictive hemodynamic model into the workflow of nurses caring for patients on vasopressors in the intensive care unit (ICU). Here, we evaluated user perceptions of the usability of this system. The software was installed in the room of an ICU patient, running for at least 4 hours with the display hidden. Afterward, we showed nurses a video recording of the session and surveyed their perceptions about the software's potential safety and usefulness. We collected data for nine patients. Overall, nurses expressed reasonable enthusiasm that the software would be useful and without serious safety concerns. However, there was a wide diversity of opinions about what specific aspects of the software would be useful and what aspects were confusing. In several instances, the same elements of the GUI were cited as most useful by some nurses and most confusing by others. Our findings validate that it is possible to develop GUIs for CDSS that are perceived as potentially useful and without substantial risk but also reinforce the diversity of user perceptions about novel CDSS technology. Clinical Relevance- This end-user evaluation of a novel CDSS highlights the importance of end-user experience in the workflow integration of advanced computational algorithms for bedside decision support during critical care.
    MeSH term(s) Algorithms ; Decision Support Systems, Clinical ; Humans ; Intensive Care Units ; Software ; Workflow
    Language English
    Publishing date 2022-09-09
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC48229.2022.9871939
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis.

    Horiguchi, Daisuke / Shin, Sungtae / Pepino, Jeremy A / Peterson, Jeffrey T / Kehoe, Iain E / Goldstein, Joshua N / Lee, Jarone / Kwon, Brian K / Hahn, Jin-Oh / Reisner, Andrew T

    Journal of intensive care medicine

    2024  , Page(s) 8850666241226893

    Abstract: Background: ...

    Abstract Background:
    Language English
    Publishing date 2024-01-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 632828-3
    ISSN 1525-1489 ; 0885-0666
    ISSN (online) 1525-1489
    ISSN 0885-0666
    DOI 10.1177/08850666241226893
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2158: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article ; Online: Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection.

    Prasad, Varesh / Aydemir, Baturay / Kehoe, Iain E / Kotturesh, Chaya / O'Connell, Abigail / Biebelberg, Brett / Wang, Yang / Lynch, James C / Pepino, Jeremy A / Filbin, Michael R / Heldt, Thomas / Reisner, Andrew T

    PLOS digital health

    2023  Volume 2, Issue 11, Page(s) e0000365

    Abstract: Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle ... ...

    Abstract Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle missing data, they could even indicate "low risk" simply because the testing data were never ordered. We considered predictive methodologies to identify sepsis at triage, before diagnostic tests are ordered, in a busy Emergency Department (ED). One algorithm used "bland clinical data" (data available at triage for nearly every patient). The second algorithm added three yes/no questions to be answered after the triage interview. Retrospectively, we studied adult patients from a single ED between 2014-16, separated into training (70%) and testing (30%) cohorts, and a final validation cohort of patients from four EDs between 2016-2018. Sepsis was defined per the Rhee criteria. Investigational predictors were demographics and triage vital signs (downloaded from the hospital EMR); past medical history; and the auxiliary queries (answered by chart reviewers who were blinded to all data except the triage note and initial HPI). We developed L2-regularized logistic regression models using a greedy forward feature selection. There were 1164, 499, and 784 patients in the training, testing, and validation cohorts, respectively. The bland clinical data model yielded ROC AUC's 0.78 (0.76-0.81) and 0.77 (0.73-0.81), for training and testing, respectively, and ranged from 0.74-0.79 in four hospital validation. The second model which included auxiliary queries yielded 0.84 (0.82-0.87) and 0.83 (0.79-0.86), and ranged from 0.78-0.83 in four hospital validation. The first algorithm did not require clinician input but yielded middling performance. The second showed a trend towards superior performance, though required additional user effort. These methods are alternatives to predictive algorithms downstream of clinical evaluation and diagnostic testing. For hospital early warning algorithms, consideration should be given to bias and usability of various methods.
    Language English
    Publishing date 2023-11-01
    Publishing country United States
    Document type Journal Article
    ISSN 2767-3170
    ISSN (online) 2767-3170
    DOI 10.1371/journal.pdig.0000365
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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