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      A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study

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          Abstract

          Background

          Clinicians working in intensive care units (ICUs) are immersed in a cacophony of alarms and a relentless onslaught of data. Within this frenetic environment, clinicians make high-stakes decisions using many data sources and are often oversaturated with information of varying quality. Traditional bedside monitors only depict static vital signs data, and these data are not easily viewable remotely. Clinicians must rely on separate nursing charts—handwritten or electric—to review physiological patterns, including signs of potential clinical deterioration. An automated physiological data viewer has been developed to provide at-a-glance summaries and to assist with prioritizing care for multiple patients who are critically ill.

          Objective

          This study aims to evaluate a novel vital signs viewer system in a level 1 trauma center by subjectively assessing the viewer’s utility in a high-volume ICU setting.

          Methods

          ICU attendings were surveyed during morning rounds. Physicians were asked to conduct rounds normally, using data reported from nurse charts and briefs from fellows to inform their clinical decisions. After the physician finished their assessment and plan for the patient, they were asked to complete a questionnaire. Following completion of the questionnaire, the viewer was presented to ICU physicians on a tablet personal computer that displayed the patient’s physiologic data (ie, shock index, blood pressure, heart rate, temperature, respiratory rate, and pulse oximetry), summarized for up to 72 hours. After examining the viewer, ICU physicians completed a postview questionnaire. In both questionnaires, the physicians were asked questions regarding the patient’s stability, status, and need for a higher or lower level of care. A hierarchical clustering analysis was used to group participating ICU physicians and assess their general reception of the viewer.

          Results

          A total of 908 anonymous surveys were collected from 28 ICU physicians from February 2015 to June 2017. Regarding physicians’ perception of whether the viewer enhanced the ability to assess multiple patients in the ICU, 5% (45/908) strongly agreed, 56.6% (514/908) agreed, 35.3% (321/908) were neutral, 2.9% (26/908) disagreed, and 0.2% (2/908) strongly disagreed.

          Conclusions

          Morning rounds in a trauma center ICU are conducted in a busy environment with many data sources. This study demonstrates that organized physiologic data and visual assessment can improve situation awareness, assist clinicians with recognizing changes in patient status, and prioritize care.

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          Most cited references40

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          Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?

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            Monitor alarm fatigue: an integrative review.

            Alarm fatigue is a national problem and the number one medical device technology hazard in 2012. The problem of alarm desensitization is multifaceted and related to a high false alarm rate, poor positive predictive value, lack of alarm standardization, and the number of alarming medical devices in hospitals today. This integrative review synthesizes research and non-research findings published between 1/1/2000 and 10/1/2011 using The Johns Hopkins Nursing Evidence-Based Practice model. Seventy-two articles were included. Research evidence was organized into five main themes: excessive alarms and effects on staff; nurse's response to alarms; alarm sounds and audibility; technology to reduce false alarms; and alarm notification systems. Non-research evidence was divided into two main themes: strategies to reduce alarm desensitization, and alarm priority and notification systems. Evidence-based practice recommendations and gaps in research are summarized.
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              Insights into the Problem of Alarm Fatigue with Physiologic Monitor Devices: A Comprehensive Observational Study of Consecutive Intensive Care Unit Patients

              Purpose Physiologic monitors are plagued with alarms that create a cacophony of sounds and visual alerts causing “alarm fatigue” which creates an unsafe patient environment because a life-threatening event may be missed in this milieu of sensory overload. Using a state-of-the-art technology acquisition infrastructure, all monitor data including 7 ECG leads, all pressure, SpO2, and respiration waveforms as well as user settings and alarms were stored on 461 adults treated in intensive care units. Using a well-defined alarm annotation protocol, nurse scientists with 95% inter-rater reliability annotated 12,671 arrhythmia alarms. Results A total of 2,558,760 unique alarms occurred in the 31-day study period: arrhythmia, 1,154,201; parameter, 612,927; technical, 791,632. There were 381,560 audible alarms for an audible alarm burden of 187/bed/day. 88.8% of the 12,671 annotated arrhythmia alarms were false positives. Conditions causing excessive alarms included inappropriate alarm settings, persistent atrial fibrillation, and non-actionable events such as PVC's and brief spikes in ST segments. Low amplitude QRS complexes in some, but not all available ECG leads caused undercounting and false arrhythmia alarms. Wide QRS complexes due to bundle branch block or ventricular pacemaker rhythm caused false alarms. 93% of the 168 true ventricular tachycardia alarms were not sustained long enough to warrant treatment. Discussion The excessive number of physiologic monitor alarms is a complex interplay of inappropriate user settings, patient conditions, and algorithm deficiencies. Device solutions should focus on use of all available ECG leads to identify non-artifact leads and leads with adequate QRS amplitude. Devices should provide prompts to aide in more appropriate tailoring of alarm settings to individual patients. Atrial fibrillation alarms should be limited to new onset and termination of the arrhythmia and delays for ST-segment and other parameter alarms should be configurable. Because computer devices are more reliable than humans, an opportunity exists to improve physiologic monitoring and reduce alarm fatigue.
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                Author and article information

                Contributors
                Journal
                JMIR Hum Factors
                JMIR Hum Factors
                JMIR Human Factors
                JMIR Human Factors
                JMIR Publications (Toronto, Canada )
                2292-9495
                2024
                5 January 2024
                : 11
                : e46030
                Affiliations
                [1 ] Department of Anesthesiology University of Maryland School of Medicine Baltimore, MD United States
                [2 ] Department of Neurology University of Maryland School of Medicine Baltimore, MD United States
                [3 ] Department of Surgery University of Maryland School of Medicine Baltimore, MD United States
                [4 ] Emergency Medicine University of Maryland School of Medicine Baltimore, MD United States
                [5 ] United States Air Force Academy Colorado Springs, CO United States
                [6 ] Department of Surgery Johns Hopkins University School of Medicine Baltimore, MD United States
                [7 ] See Acknowledgments
                Author notes
                Corresponding Author: Shiming Yang syang@ 123456som.umaryland.edu
                Author information
                https://orcid.org/0000-0003-0338-4268
                https://orcid.org/0000-0001-5563-4092
                https://orcid.org/0000-0003-1509-9034
                https://orcid.org/0000-0002-0726-9390
                https://orcid.org/0000-0001-9584-0579
                https://orcid.org/0000-0001-8794-4108
                https://orcid.org/0000-0003-3585-2150
                https://orcid.org/0000-0003-3887-0143
                https://orcid.org/0000-0002-7243-8058
                https://orcid.org/0000-0001-7332-758X
                Article
                v11i1e46030
                10.2196/46030
                10799282
                38180791
                301c4f60-0a6d-40fd-86f1-d48fb102bd34
                ©Shiming Yang, Samuel Galvagno, Neeraj Badjatia, Deborah Stein, William Teeter, Thomas Scalea, Stacy Shackelford, Raymond Fang, Catriona Miller, Peter Hu, VS viewer study group. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 05.01.2024.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.

                History
                : 27 February 2023
                : 14 June 2023
                : 3 November 2023
                : 20 November 2023
                Categories
                Original Paper
                Original Paper

                clinical decision-making,health information technology,intensive care units,patient care prioritization,physiological monitoring,visualization,vital signs

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