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Record W4299340158

Design and preliminary validation of a mobile application-based expert system to facilitate repair of medical equipment in resource-limited health settings

2018· article· en· W4299340158 on OpenAlexaboutno aff
Wong AL, Lacob KM, Wilson MG, Zwolski SM

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsResource (disambiguation)Computer scienceSystems engineeringRisk analysis (engineering)EngineeringBusiness
DOInot available

Abstract

fetched live from OpenAlex

Alison L Wong,1,2 Kelly M Lacob,1 Madeline G Wilson,1 Stacie M Zwolski,1 Soumyadipta Acharya1 1Center for Bioengineering, Innovation and Design, Johns Hopkins University, Baltimore, MD, USA; 2Division of Plastic Surgery, Dalhousie University, Halifax, NS, Canada Background: One of the greatest barriers to safe surgery is the availability of functional biomedical equipment. Biomedical technicians play a major role in ensuring that equipment is functional. Following in-field observations and an online survey, a mobile application was developed to aid technicians in troubleshooting biomedical equipment. It was hypothesized that this application could be used to aid technicians in equipment repair, as modeled by repair of a pulse oximeter.Methods: To identify specific barriers to equipment repair and maintenance for biomedical technicians, an online survey was conducted to determine current practices and challenges. These findings were used to guide the development of a mobile application system that guides technicians through maintenance and repair tasks. A convenience sample of technicians in Ethiopia tested the application using a broken pulse oximeter task and following this completed usability and content validity surveys.Results: Fifty-three technicians from 13 countries responded to the initial survey. The results of the survey showed that technicians find equipment manuals most useful, but these are not easily accessible. Many do not know how to or are uncomfortable reaching out to human resources. Thirty-three technicians completed the broken pulse oximeter task using the application. All were able to appropriately identify and repair the equipment, and post-task surveys of usability and content validity demonstrated highly positive scores (Agree to Strongly Agree) on both scales.Discussion: This research demonstrates the need for improved access to resources for technicians and shows that a mobile application can be used to address a gap in the access to knowledge and resources in low- and middle-income countries. Further research will include prospective studies to determine the impact of an application on the availability of functional equipment in a hospital and the effect on the provision and safety of surgical care. Keywords: medical apps, global health, mHealth, repair system, pulse oximeter

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.434
GPT teacher head0.618
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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