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Record W3011494897 · doi:10.4018/ijhisi.2020070105

Blockchain in Healthcare

2020· article· en· W3011494897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Healthcare Information Systems and Informatics · 2020
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBlockchainInteroperabilityHealth careBusinessScalabilityConceptualizationComputer securityComputer scienceHealth recordsAnalyticsInternet privacyKnowledge managementData scienceWorld Wide WebDatabasePolitical science

Abstract

fetched live from OpenAlex

Blockchain, an immutable ledger or database shared by peers in a network, is comprised of records of events or transactions that are appended chronologically. Introduced via Bitcoin to the world, blockchain is increasingly being accepted and adopted in different industries and for diverse use cases. Among key industries, health care offers several significant opportunities for applying blockchain conceptualization. Chief areas for health care blockchain applications include electronic medical records management, pharmaceutical supply chain management, biomedical research and education, remote patient monitoring, health insurance claim processing, and health data analytics. Even so, applying blockchain concepts in health care is not without challenges, including interoperability, security-privacy, scalability-speed, and stakeholders' engagement issues. While these challenges may militate against blockchain applications in health care, there are possible countermeasures and implementation techniques, which if adhered to, can reasonably contain many aspects of such challenges.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.017
GPT teacher head0.272
Teacher spread0.256 · 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