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Record W2339098117 · doi:10.5539/mas.v10n6p136

The Use of NFC Technology to Record Medical Information in Order to Improve the Quality of Medical and Treatment Services

2016· article· en· W2339098117 on OpenAlexvenueno aff
Nasreen Nabi Khah Razmi, Amin Babazadeh Sangar

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)Health careQuality (philosophy)Mobile deviceComputer scienceInternet privacyMedical emergencyBusinessComputer securityWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

Although doctors are increasingly interested in electronic systems of registering medical record, but in practice such systems are used less. Mobile devices provide a new way for accessing users to data of health cares and services in a secure environment and user-selection. Mobile health cares' systems (M-health) are considered as a solution to reduce health care costs without reducing the quality of patient care. In this paper we are going to develop a common architecture for mobile health cares' services using NFC in order to facilitate providing health cares to people anywhere and anytime using the mobile devices that are connected to wireless communication technology, to be able to provide required services by a secure and available structure for patient' information in hospitals and health centers and treatment, especially intensive care units, emergency or patients needed home care. Also it can be avoided from forgery and misuse of physicians' stamp in current versions with this system by preparing electronic version using NFC technology.

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.452
Teacher spread0.386 · 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 designOther design
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

Citations12
Published2016
Admission routes1
Has abstractyes

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