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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».