Addressing Public Health informatics patient privacy concerns
Bibliographic record
Abstract
Purpose – The purpose of this paper is to review stakeholder perspectives and provide a framework for improving governance in health data stewardship. Patients may wish to view their own lab results or clinical records, but others (notably academics, journalists and lawyers) tend to want scores of patient records in their search for patterns or trends. Public Health informatics capabilities are growing in scope and speed as clinical information systems, health information exchange networks and other potential database linkages enable more access to healthcare data. This change facilitates novel service improvements, but also raises new personal privacy protection issues. Design/methodology/approach – This paper summarizes a panel session discussion from the 2015 Information Technology and Communication in Health biennial international conference. The perspectives of health service research, journalism, Public Health informatics and privacy protection were represented. Findings – In North America, an expectation of personal privacy exists as a quasi-constitutional right. Individuals should be allowed to control the amount of information shared about them, and in particular the public expects that details of their personal healthcare data are protected. This is supported by laws, regulations and administrative structures; however, there are fundamental differences between the approaches taken in Canada and in the USA. In both countries, population and Public Health has wide powers to collect data and share it appropriately in order to accomplish a social good. A recent report issued by the British Columbia Information and Privacy Commissioner, and a recent story issued by the Bloomberg News service, highlight ways in which laws and regulations have not kept pace with advances in technology. Changes are needed to enable population and Public Health agencies to protect confidential personal information while still being able to comply with legitimate requests for data by researchers, policy makers and the public at large. Originality/value – Similarities and differences in approach, gaps, current issues and recommendations of several countries were revealed in a conference session. Those concepts and the likelihood of ensuing legislative changes directly impact healthcare organizations’ patients and leadership.
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 | 0.130 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".