To understand and report on the direct-to-consumer virtual care industry in Canada, focusing on how companies collect, use and value patient data.

Qualitative study using situational analysis methodology.

Canadian for-profit virtual care industry.

18 individuals employed by or affiliated with the Canadian virtual care industry.

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.

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.

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.

Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions.

A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants’ attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants’ opinions on how their experiences compare. Data will be analysed thematically using the Framework Method.

This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.