The Primary Care Medical Record Industry in Canada and Its Data Collection and Commercialization Practices
Bibliographic record
Abstract
Importance: Massive volumes of health data flow to commercial data brokers worldwide, yet little empirical research has examined how this industry functions and the implications for patients. Objective: To describe and analyze the primary care medical record industry in Canada and its data collection and commercialization practices. Design, Setting, and Participants: This qualitative study of the Canadian primary care health data industry used situational analysis, a grounded theory methodology. Data sources included semistructured interviews of individuals affiliated with the commercial health data industry from May 2022 to May 2023 and publicly available documents. Data were analyzed from May 2022 to May 2024. Main Outcomes and Measures: Individual semistructured interviews and relevant publicly available documents were analyzed to gain an understanding of data collection and commercialization practices in the primary care record industry. The analysis involved a continuous and iterative process of data collection and analysis, theoretical sampling, data-driven coding, and creation of theoretical concepts. Results: A total of 19 interviews were conducted and 22 documents were sampled. Study participants described the primary care medical record industry in Canada as consisting of complex reciprocal relationships between commercial health data brokers, physicians, for-profit chains of primary care clinics, and pharmaceutical companies. In an emerging vertically integrated business model, the data broker brought the primary care clinics and physicians in house as a clinical subsidiary, thus obtaining more control over clinical practices. Participants understood the primary care medical record industry as having potential to transform patient care, but-because of financial considerations-also tied to pharmaceutical industry interests. According to participants, patients were not involved in decisions related to how their records were collected and used. Conclusions and Relevance: This qualitative study found that each entity within the Canadian primary care medical record industry contributes to, and benefits from, the conversion of patient medical records into commercial assets. The industry's activities reflect the pharmaceutical companies' interests. Patients are notably absent from decision-making; thus, the industry's activities may not reflect their values or interests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".