Effects of Virtual Care on Patient and Provider Experience of the Clinical Encounter: Qualitative Hermeneutic Study
Bibliographic record
Abstract
BACKGROUND: Virtual health care has transformed health care delivery, with its use dramatically increasing since the COVID-19 pandemic. While it has been quickly adopted for its convenience and efficiency, there has been a relative lack of in-depth exploration of its human impact, specifically how both patients and providers experience clinical encounters. OBJECTIVE: This analysis aims to identify and explore themes of change in how patients and providers in a geographically dispersed renal service described their experiences with virtual care, including those changes that occurred during the COVID-19 pandemic. METHODS: Hermeneutics is an interpretive research methodology that treats human experience as inherently interpretive, generating meaning through interactions with others in specific, historically conditioned, social contexts. A total of 17 patients and 10 providers from various disciplines were interviewed by phone as part of a study on health care implementation in the context of a kidney care service in northern British Columbia, Canada. The interview data were analyzed using a hermeneutic approach, which emphasizes careful attention to reported experiences in relation to the relationships and contexts of care. RESULTS: During analysis, the interdisciplinary team identified themes related to changes in the clinical encounter and how virtual care influenced perceptions of care among both providers and patients. We organized these themes into 2 categories: the structure and content of the encounter. The structure category included the convenience for patients, who no longer had to travel long distances for appointments, as well as changes in care networks. For example, communication between specialist services and local primary care providers became more crucial for ensuring continuity of care. The content category included issues related to trust-building and assessment. Providers expressed concerns about the difficulty in assessing and understanding their patients' physical and social well-being beyond laboratory results. CONCLUSIONS: Patients in the study appreciated the convenience of not needing to travel for appointments, while still having the option for in-person contact with local providers or specialists if their condition changed. Providers were more concerned about the loss of visual cues and sensory data for assessments, as well as the reduced opportunity to build relationships through conversation with patients. Providers also described changes in the locus of control and boundaries, as patients could join phone encounters from anywhere, bypassing traditional privacy and confidentiality boundaries. The study offers a nuanced view of the effects of virtual care on clinical encounters in one setting, seen through the experiences of both patients and providers.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".