Assessing the Usability and Effectiveness of an AI-Powered Telehealth Platform: Mixed Methods Study on the Perspectives of Patients and Providers
Notice bibliographique
Résumé
BACKGROUND: Telemedicine has revolutionized health care by significantly enhancing accessibility and convenience, yet barriers remain, such as providers' challenges with technology use. With advancements in telemedicine technologies, understanding the viewpoints of patients and providers is crucial for an effective and acceptable telemedicine service. This study reports the findings on the usability and effectiveness of the HelixVM artificial intelligence powered platform, analyzing key aspetcs like asynchronous health care, access, time efficiency, productivity, data exchange, security, privacy, and quality of care from patient and provider perspectives. OBJECTIVE: This study aims to assess the usability and effectiveness of the HelixVM marketplace platform. METHODS: We recruited 102 patients and 12 providers in a mixed methods study design involving surveys and in-depth structured interviews with a subset of the providers. The survey questionnaires are a modified version of the Telehealth Usability Questionnaire. We analyzed patient data using descriptive statistics and exploratory factor analysis to identify latent demographic patterns. For provider data, we used a deductive thematic analysis approach to identify key themes from the interviews and interpreted overall sentiments of the providers as negative, neutral, or positive. We also calculated percentages of different provider responses from the survey and interviews, where applicable. RESULTS: Overall, 86.3% (88/102) of the patients reported satisfaction with HelixVM, and 89.2% (91/102) indicated that they would use the services again. A total of 91.1% (93/102) of the patients agreed that HelixVM improves access to health care and is an acceptable way to receive health care, and 98% (100/102) agreed it saves time. Chi-square tests demonstrated statistical significance for all survey questions (P<.001). The results from factor analysis show a higher propensity of middle-aged women, who had a fast-track encounter type, who self-reported a medium level of technology savviness, and who are residing in the South region of the United States rating the platform more positively. With regard to the providers, the thematic analysis identified themes of asynchronous medicine in terms of the accessibility and quality of care, time and productivity, integration within the workflow, data exchange, and artificial intelligence triage. Certain challenges regarding incomplete data in patient charts and its impact on provider time were cited. Suggestions for improvements included options to ensure the completeness of patient charts and better screening to ensure that only asynchronous, qualified patients are able to reach the provider. CONCLUSIONS: Overall, our study findings indicate a positive experience for patients and providers. The use of fast-track prescription was considered favorable compared to traditional telemedicine. Some concerns on data completeness, gaps, and accuracy exist. Suggestions are provided for improvement. This study adds to the knowledge base of existing literature and provides a detailed analysis of the real-world implementation of a telemedicine market-place platform.
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 | 0,030 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| 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 ».