Technology-Enhanced Consultations in Diabetes, Cancer, and Heart Failure: Protocol for the Qualitative Analysis of Remote Consultations (QuARC) Project
Bibliographic record
Abstract
BACKGROUND: Remote videoconsulting is promoted by policy makers as a way of delivering health care efficiently to an aging population with rising rates of chronic illness. As a radically new service model, it brings operational and interactional challenges in using digital technologies. In-depth research on this dynamic is needed before remote consultations are introduced more widely. OBJECTIVE: The objective of this study will be to identify and analyze the communication strategies through which remote consultations are accomplished and to guide patients and clinicians to improve the communicative quality of remote consultations. METHODS: In previous research, we collected and analyzed two separate datasets of remote consultations in a National Institute for Health Research-funded study of clinics in East London using Skype and a Wellcome Trust-funded study of specialist community heart failure teams in Oxford using Skype or FaceTime. The Qualitative Analysis of Remote Consultations (QuARC) study will combine datasets and undertake detailed interactional microanalysis of up to 40 remote consultations undertaken by senior and junior doctors and nurse specialists, including consultations with adults with diabetes, women who have diabetes during pregnancy, people consulting for postoperative cancer surgery and community-based patients having routine heart failure reviews along with up to 25 comparable face-to-face consultations. Drawing on established techniques (eg, conversation analysis), analysis will examine the contextual features in remote consultations (eg, restricted visual field) combined with close analysis of different modes of communication (eg, speech, gesture, and gaze). RESULTS: Our findings will address the current gap in knowledge about how technology shapes the fine detail of communication in remote consultations. Alongside academic outputs, findings will inform the coproduction of information and guidance about communication strategies to support successful remote consultations. CONCLUSIONS: Identifying the communication strategies through which remote consultations are accomplished and producing guidance for patients and clinicians about how to use this kind of technology successfully in consultations is an important and timely goal because roll out of remote consultations is planned across the National Health Service. REGISTERED REPORT IDENTIFIER: RR1-10.2196/10913.
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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 | 0.100 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.081 | 0.012 |
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".