Exploring Patient and Staff Experiences With Video Consultations During COVID-19 in an English Outpatient Care Setting: Secondary Data Analysis of Routinely Collected Feedback Data
Bibliographic record
Abstract
BACKGROUND: Video consultations (VCs) were rapidly implemented in response to COVID-19 despite modest progress before. OBJECTIVE: We aim to explore staff and patient experiences with VCs implemented during COVID-19 and use feedback insights to support quality improvement and service development. METHODS: Secondary data analysis was conducted on 955 patient and 521 staff responses (from 4234 consultations; 955/4234, 22.6% and 521/4234, 12.3%, respectively) routinely collected following a VC between June and July 2020 in a rural, older adult, and outpatient care setting at a National Health Service Trust. Responses were summarized using descriptive statistics and inductive thematic analysis and presented to Trust stakeholders. RESULTS: Most patients (890/955, 93.2%) reported having good (210/955, 22%) or very good (680/955, 71.2%) experience with VCs and felt listened to and understood (904/955, 94.7%). Most patients accessed their VC alone (806/955, 84.4%) except for those aged ≥71 years (23/58, 40%), with ease of joining VCs negatively associated with age (P<.001). Despite more difficulties joining, older adults were most likely to be satisfied with the technology (46/58, 79%). Patients and staff generally felt that patients' needs had been met (860/955, 90.1% and 453/521, 86.9%, respectively), although staff appeared to overestimate patient dissatisfaction with VC outcomes (P=.02). Patients (848/955, 88.8%) and staff (419/521, 80.5%) felt able to communicate everything they wanted, although patients were significantly more positive than staff (P<.001). Patient satisfaction with communication was positively associated with technical performance satisfaction (P<.001). Most staff members (466/521, 89.4%) reported positive (185/521, 35.5%) or very positive (281/521, 53.9%) experiences with joining and managing VCs. Staff reported reductions in carbon footprint (380/521, 72.9%) and time (373/521, 71.6%). Most patients (880/955, 92.1%) would choose VCs again. We identified three themes in responses: barriers, including technological difficulties, patient information, and suitability concerns; potential benefits, including reduced stress, enhanced accessibility, cost, and time savings; and suggested improvements, including trial calls, turning music off, photo uploads, expanding written character limit, supporting other internet browsers, and shared online screens. This routine feedback, including evidence to suggest that patients were more satisfied than clinicians had anticipated, was presented to relevant Trust stakeholders, allowing for improved processes and supporting the development of a business case to inform the Trust decision on continuing VCs beyond COVID-19 restrictions. CONCLUSIONS: The findings highlight the importance of regularly reviewing and responding to routine feedback following digital service implementation. The feedback helped the Trust improve the VC service, challenge clinician-held assumptions about patient experience, and inform future use of VCs. It has focused improvement efforts on patient information; technological improvements such as blurred backgrounds and interactive whiteboards; and responding to the needs of patients with dementia, communication or cognitive impairment, or lack of appropriate technology. These findings have implications for other health care 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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".