Evidence map›Paper›PMID 36860328›Full record

ReviewJournal of healthcare engineering2023

Critical Device Reliability Assessment in Healthcare Services.

Noorul Husna Abd Rahman, Ayman Khallel Ibrahim, Khairunnisa Hasikin, Nasrul Anuar Abd Razak

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of healthcare engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
14.4field-weighted citation impact, top 1% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024
    Pooled it
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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 at 3 institutions in 1 country.

Noorul Husna Abd RahmanDepartment of Biomedical Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
Ayman Khallel IbrahimFaculty of Computing and Informatics, Universiti Malaysia Sabah, Sabah 88400, Malaysia.
Khairunnisa HasikinDepartment of Biomedical Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.ORCID 0000-0002-0471-3820
Nasrul Anuar Abd RazakDepartment of Biomedical Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
University of Malaya · MYMinistry of Health · MYUniversiti of Malaysia Sabah · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.

Indexed as

Artificial IntelligenceHealth ServicesDelivery of Health CareHealth FacilitiesHumansInfantReproducibility of Results

Identifiers

PMID36860328
PMCPMC9970731
OpenAlexW4321366672

What Socratic 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 Socratic graph.