Managing hospital databases: can large hospitals really protect patient data?
Bibliographic record
Abstract
Between 1998 and 2003 a number of European countries, the UK, Canada, Australia and the US all introduced data privacy legislation that sought to comply with the European Data Privacy Directive of 1995 in protecting the privacy of individuals undergoing treatment in large hospitals. In 2004 we find that hospital administrators within these jurisdictions are still struggling to find ways to implement and maintain hospital databases while complying with the given legislation - where compliance seems to require a whole new approach to database management. This research examines the UK Data Protection Act 1998 and considers whether current database management systems allow the EU Directives contained in the Act to be followed in practice. It finds a number of recurrent problems with hospital systems that would make compliance with the Act difficult. These findings have significant implications for hospital information systems development and design.
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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 | 0.057 | 0.241 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.020 | 0.044 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".