How the commercial virtual care industry gathers, uses and values patient data: a Canadian qualitative study
Bibliographic record
Abstract
OBJECTIVES: To understand and report on the direct-to-consumer virtual care industry in Canada, focusing on how companies collect, use and value patient data. DESIGN: Qualitative study using situational analysis methodology. SETTING: Canadian for-profit virtual care industry. PARTICIPANTS: 18 individuals employed by or affiliated with the Canadian virtual care industry. METHODS: Semistructured interviews were conducted between October 2021 and January 2022 and publicly available documents on websites of commercial virtual care platforms were retrieved. Analysis was informed by situational analysis, a constructivist grounded theory methodology, with a continuous and iterative process of data collection and analysis; theoretical sampling and creation of theoretical concepts to explain findings. RESULTS: Participants described how companies in the virtual care industry highly valued patient data. Companies used data collected as patients accessed virtual care platforms and registered for services to generate revenue, often by marketing other products and services. In some cases, virtual care companies were funded by pharmaceutical companies to analyse data collected when patients interacted with a healthcare provider and adjust care pathways with the goal of increasing uptake of a drug or vaccine. Participants described these business practices as expected and appropriate, but some were concerned about patient privacy, industry influence over care and risks to marginalised communities. They described how patients may have agreed to these uses of their data because of high levels of trust in the Canadian health system, problematic consent processes and a lack of other options for care. CONCLUSIONS: Patients, healthcare providers and policy-makers should be aware that the direct-to-consumer virtual care industry in Canada highly values patient data and appears to view data as a revenue stream. The industry's data handling practices of this sensitive information, in the context of providing a health service, have implications for patient privacy, autonomy and quality of care.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".