Privacy Interests in Prescription data, Part 2 Patient Privacy
Bibliographic record
Abstract
the sale or transfer of prescrip-• tion data from pharmacies to commercial data brokers; processing of the data to analyze • physicians ’ prescribing patterns; and the subsequent sale of these pre-• scribing patterns to pharmaceu-tical companies, among others, that use this information to cus-tomize their marketing strategies aimed at physicians. In part one of this two-part se-ries, we discussed privacy concerns with respect to prescribers. In this second installment, we examine the privacy risks to patients from Canadian and US perspectives. Identifiability In privacy terms, we generally understand personal information to be identifiable information about an individual. Although prescrip-tion data in relation to a patient is clearly information about him or her, the privacy issue that arises is whether prescription records con-tain fields that—taken together or in combination with other publicly available data—could identify the individual patient concerned. If so, pharmacies would have a clear privacy obligation to obtain prior consent from patients before dis-closing any prescription data about them to commercial data brokers. Prescription data disclosed by pharmacies to commercial data brokers don’t typically contain any directly identifiable information about the patient involved, but do contain fields such as the patient’s age and gender. Sometimes, phar-macies also disclose, directly or inferentially, geographic informa-tion about the patient’s residence, such as the first three characters of the Canadian postal code (other-wise known as the forward sortation area, or FSA). The privacy ques-tion here is whether these three fields, limited as they are, can still be used, either alone or in combi-nation with other available infor-mation, to re-identify individual patients, thereby jeopardizing their right to confidentiality. Privacy laws generally apply only to identifiable information. These laws might either explic-itly or implicitly provide some kind of threshold for determining when information ceases to be de-identified and becomes identifi-
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".