Revisiting public health informatics: patient privacy concerns
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.154 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.051 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".