Evidence map›Paper›PMID 39611020›Full record

ArticleFrontiers in medical technology2024

An approach to reduce inhaler errors using Donabedian's triad.

Dorothy May Isip Cruz, Manjush Karthika, Ashraf Alzaabi

Abstract read
In one paragraph

Article in Frontiers in medical technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

3 authors.

Dorothy May Isip CruzFaculty of Medical and Health Sciences, Liwa College, Abu Dhabi, United Arab Emirates.
Manjush KarthikaFaculty of Medical and Health Sciences, Liwa College, Abu Dhabi, United Arab Emirates.
Ashraf AlzaabiDepartment of Internal Medicine, College of Medical and Health Sciences, UAE University, Al Ain, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inhaler errors inversely affect the outcome of respiratory diseases. Inhaler devices, such as the metered-dose inhalers (MDI) and dry powder inhalers (DPI), are commonly used in treating respiratory diseases like asthma and chronic obstructive pulmonary disease (COPD), and incorrect use of these devices can result in suboptimal treatment outcomes, increased probabilities of hospitalizations or admissions, and poorer quality of life. Patient related factors to inhaler errors include age, cognitive and physical abilities, education, language barriers, and preferences. Device-related factors such as inhaler design and operational complexity can also lead to errors. Finally, factors related to healthcare professionals (HCP) such as competency, level of knowledge in disease and inhaler device and availability to educate patients, can play a role in inhaler error. Quality management is a potential solution to this problem. Quality improvement strategies towards addressing inhaler misuse can increase patient satisfaction and improve patient outcomes. Donabedian's triad, which includes structure, process, and outcome can be utilized in developing a framework for reducing inhaler errors. Institutional solutions are more towards the structural and process changes in the triad, such as HCP training, checklists on training efficacy, provision of action plans, and availability of staff to educate and train patients. Patient-centered solutions focus more on process and outcome domains, such as improvement in lung functions, patient education, re-assessment and re-education of inhaler techniques, and adherence to treatment regimen. By focusing on structural and process domains, the quality of care can be enhanced, resulting in improved outcomes.

Indexed as

Donabedian’s triadinhaler errorsoutcomeprocessstructure

Identifiers

PMID39611020
PMCPMC11603695

What OpenQuestion holds

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Registered trials

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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.