Evidence map›Paper›PMID 38180791›Full record

ArticleJMIR human factors2024

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

Shiming Yang, Samuel Galvagno, Neeraj Badjatia, Deborah Stein, William Teeter, Thomas Scalea, Stacy Shackelford, Raymond Fang, Catriona Miller, Peter Hu and 1 more

Open access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Shiming YangDepartment of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0003-0338-4268
Samuel GalvagnoDepartment of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0001-5563-4092
Neeraj BadjatiaDepartment of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0003-1509-9034
Deborah SteinDepartment of Surgery, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-0726-9390
William TeeterEmergency Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0001-9584-0579
Thomas ScaleaDepartment of Surgery, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0001-8794-4108
Stacy ShackelfordUnited States Air Force Academy, Colorado Springs, CO, United States.ORCID 0000-0003-3585-2150
Raymond FangDepartment of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States.ORCID 0000-0003-3887-0143
Catriona MillerDepartment of Surgery, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-7243-8058
Peter HuDepartment of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, United States.ORCID 0000-0001-7332-758X
VS viewer study group *See Acknowledgments, .
University of Maryland, Baltimore · USJohns Hopkins University · USKnowledge Centre for Agriculture · DKUnited States Air Force Academy · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinicians 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.

objectiveThis 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.

methodsICU 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.

resultsA 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.

conclusionsMorning 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.

Indexed as

Intensive Care UnitsMonitoring, PhysiologicVital SignsBlood PressureHeart RateHumansRespiratory Rateclinical decision-makinghealth information technologyintensive care unitspatient care prioritizationphysiological monitoringvisualizationvital signs

Identifiers

PMID38180791
PMCPMC10799282
OpenAlexW4388822622

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.