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Record W2800196470 · doi:10.1108/ijhg-11-2017-0058

Revisiting public health informatics: patient privacy concerns

2018· article· en· W2800196470 on OpenAlexaffabout
David Birnbaum, Kathryn Gretsinger, Marcy Antonio, Elizabeth Loewen, Paulette Lacroix

Bibliographic record

VenueInternational Journal of Health Governance · 2018
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsHealth informaticsPublic relationsStakeholderSession (web analytics)Information privacyPublic healthInformaticsOriginalityInternet privacyPolitical scienceKnowledge managementBusinessComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

Purpose Expanding networks of data portals and repositories linked to electronic patient record systems, along with advances in information technology, have created both new opportunities in improving public health and new challenges in protecting patient privacy. The purpose of this paper is to review stakeholder perspectives and provide a framework for promoting implementation of current privacy protection improvement recommendations. Design/methodology/approach This paper summarizes a workshop session discussion stemming from the 2017 Information Technology and Communication in Health (ITCH) biennial international conference in Victoria, British Columbia, Canada. The perspectives within health service research, journalism, informatics and privacy protection were represented. Findings Problems underlying gaps in privacy protection in the USA and Canada, along with then-current changes recommended by public health leaders as well as Information and Privacy Commissioners, were identified in a session of the 2015 ITCH conference. During the 2017 conference, a workshop outlined the current situation, identifying ongoing challenges and a lack of significant progress. This paper summarizes that 2017 discussion identifying political climate as the major impediment to progress on this issue. It concludes with a framework to guide the path forward. Originality/value This paper provides an international perspective to problems, resources and solution pathways with links useful to readers in all countries.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.469
Teacher spread0.362 · 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 designNot applicable
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

Citations10
Published2018
Admission routes2
Has abstractyes

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