Evidence map›Paper›PMID 33786985›Full record

ArticleJournal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing2021

Nurse's Achilles Heel: Using Big Data to Determine Workload Factors That Impact Near Misses.

Amy A Campbell, Todd Harlan, Matt Campbell, Madhuri S Mulekar, Bin Wang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

5 authors at 1 institution in 1 country.

Amy A CampbellProfessor, College of Nursing, Department of Community Mental Health, University of South Alabama, Mobile, AL, USA.ORCID 0000-0001-6647-9075
Todd HarlanChair and Professor, College of Nursing, Department of Community Mental Health, University of South Alabama, Mobile, AL, USA.
Matt CampbellProfessor, School of Computing, Department of Information Systems Technology, University of South Alabama, Mobile, AL, USA.
Madhuri S MulekarChair and Professor, Department of Mathematics and Statistics, University of South Alabama, Mobile, AL, USA.
Bin WangProfessor, Department of Mathematics and Statistics, University of South Alabama, Mobile, AL, USA.
University of South Alabama · US

Funding

UAB Center for Clinical and Translational Science (CCTS)UL1TR001417 · NCATS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI KIMBERLY, ROBERT P. · 2015 to 2018
$31.4M
NCATS NIH HHS UL1 TR001417NCATS NIH HHS UL1TR001417
6 · The paper itself

Abstract

purposeTo explore how big data can be used to identify the contribution or influence of six specific workload variables: patient count, medication count, task count call lights, patient sepsis score, and hours worked on the occurrence of a near miss (NM) by individual nurses.

designA correlational and cross-section research design was used to collect over 82,000 useable data points of historical workload data from the three unique systems on a medical-surgical unit in a midsized hospital in the southeast United States over a 60-day period. Data were collected prior to the start of the Covid-19 pandemic in the United States.

methodsCombined data were analyzed using JMP Pro version 12. Mean responses from two groups were compared using a t-test and those from more than two groups using analysis of variance. Logistic regression was used to determine the significance of impact each workload variable had on individual nurses' ability to administer medications successfully as measured by occurrence of NMs.

findingsThe mean outcome of each of the six workload factors measured differed significantly (p < .0001) among nurses. The mean outcome for all workload factors except the hours worked was found to be significantly higher (p < .0001) for those who committed an NM compared to those who did not. At least one workload variable was observed to be significantly associated (p < .05) with the occurrence or nonoccurrence of NMs in 82.6% of the nurses in the study.

conclusionsFor the majority of the nurses in our study, the occurrence of an NM was significantly impacted by at least one workload variable. Because the specific variables that impact performance are different for each individual nurse, decreasing only one variable, such as patient load, will not adequately address the risk for NMs. Other variables not studied here, such as education and experience, might be associated with the occurrence of NMs. CLINICAL RELEVANCE: In the majority of nurses, different workload variables increase their risk for an NM, suggesting that interventions addressing medication errors should be implemented based on the individual's risk profile.

Indexed as

Big DataNursing Staff, HospitalHumansNear Miss, HealthcareRisk FactorsSoutheastern United StatesWorkloadBig datamedication errorsnear misssafetyworkload factors

Identifiers

PMID33786985
PMCPMC8221452
OpenAlexW3143967737

What OpenQuestion holds

Textmetadata
LicenceTDM
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.