Evidence map›Paper›PMID 38269818›Full record

ArticleStudies in health technology and informatics2024

Learning from Non-Routine Events and Teamwork in Intensive Care Units: Challenges and Opportunities.

Yang Gong, You Chen

Open access · hybridAbstract read
In one paragraph

Article in Studies in health technology and informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 97% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 2 institutions in 1 country.

Yang GongThe University of Texas Health Science Center at Houston, TX, USA.
You ChenVanderbilt University, Nashville, TN, USA.
The University of Texas Health Science Center at Houston · USVanderbilt University · US

Funding

Discovering Care Coordination Practice Patterns in the EMR: Interpretation and Impact on Patient OutcomesR01LM012854 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CHEN, YOU · 2019 to 2022
$1.5M
NLM NIH HHS R01 LM012854
6 · The paper itself

Abstract

Patients admitted to intensive care units (ICUs) have profound and complex illnesses, often fraught with uncertainties in diagnoses, treatments, and care decisions. Clinicians often deviate from best practices to handle ICUs' myriad complexities and uncertainties. Non-routine events (NREs), defined as any aspect of care perceived by clinicians as deviations from optimal care, are latent and frequent safety threats that, if left unchecked, can be precursors to adverse events. Proper identification and analysis of NREs that represent latent safety threats have been proposed as a feasible and more effective approach for performance improvement than traditional root cause analysis for patient safety events. However, NRE studies to date have yet to show the holistic picture of NREs in the contexts of teamwork and time-dependent tasks that are frequently associated with NREs. NREs, an upstream interventional area to understand root causes, team performance, and human-computer interaction, still needs to be expanded. This article presents concepts of NREs, and the use of real-world data (RWD) and informatics methodology to investigate NREs in contexts and discusses the opportunities and challenges to enhance NREs research in teamwork and time-dependent tasks.

Indexed as

HospitalizationIntensive Care UnitsHumansRoot Cause AnalysisUncertaintyEHR access logselectronic health recordsintensive care unitsnetwork analysisNon-routine eventspatient safetytasksteamwork

Identifiers

PMID38269818
PMCPMC11606403
OpenAlexW4391224377

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.