Evidence map›Paper›PMID 38451980›Full record

ArticlePloS one2024

Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model.

Salma Albreiki, Mecit Can Emre Simsekler, Abroon Qazi, Ali Bouabid

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

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

  1. Pooled it
  2. Article
  3. 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

4 authors.

Salma AlbreikiDepartment of Management Science and Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
Mecit Can Emre SimseklerDepartment of Management Science and Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.ORCID 0000-0002-1555-5012
Abroon QaziSchool of Business Administration, American University Sharjah, Sharjah, United Arab Emirates.ORCID 0000-0001-6648-3306
Ali BouabidInstitute of Educational Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the exponential transformation occurring in the healthcare industry, operational failures pose significant challenges in the delivery of safe and efficient care. Incident management plays a crucial role in mitigating these challenges; however, it encounters limitations due to organizational factors within complex and dynamic healthcare systems. Further, there are limited studies examining the interdependencies and relative importance of these factors in the context of incident management practices. To address this gap, this study utilized aggregate-level hospital data to explore the influence of organizational factors on incident management practices. Employing a Bayesian Belief Network (BBN) structural learning algorithm, Tree Augmented Naive (TAN), this study assessed the probabilistic relationships, represented graphically, between organizational factors and incident management. Significantly, the model highlighted the critical roles of morale and staff engagement in influencing incident management practices within organizations. This study enhances our understanding of the importance of organizational factors in incident management, providing valuable insights for healthcare managers to effectively prioritize and allocate resources for continuous quality improvement efforts.

Indexed as

Delivery of Health CareHospitalsAlgorithmsBayes TheoremHumans

Identifiers

PMID38451980
PMCPMC10919587

What OpenQuestion holds

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LicenceCC BY
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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.