A multinational survey of patient utilization of and value conveyed through virtual symptom triage and healthcare referral
Notice bibliographique
Résumé
Objective: To describe the use patterns, impact and derived patient-user value of a mobile web-based virtual triage/symptom checker. Methods: Online survey of 2,113 web-based patient-users of a virtual triage/symptom checker was completed over an 8-week period. Questions focused on triage and care objectives, pre- and post-triage care intent, frequency of use, value derived and satisfaction with virtual triage. Responses were analyzed and stratified to characterize patient-user pre-triage and post-triage intent relative to triage engine output. Results: Seventy-eight percent of virtual triage users were female, and 37% were 18-24 years old or younger, 28% were 25-44, 16% were 45-54, and 19% were 55 years or older; 41.2% completed the survey from the U.S., 12.5% from the U.K., 9.1% from Canada, 5.6% from India, 3.8% from South Africa. Motivations were to determine need to consult a physician (44.2%), to secure medical advice without visiting a physician (21.0%), and to confirm a diagnosis received (14.2%). Forty-three percent were first time users of virtual triage, 36.6% utilized a triage engine at least once every few months or more often. Pre-triage, 40.5% did not know what level of healthcare they were planning to utilize, 33.9% stated they intended to seek a physician consultation, 23.7% engage self-care and 1.8% seek emergency care. Virtual triage recommended 56.8% of patient-users consult a physician, 33.8% seek emergency care and 9.4% engage self-care. In three-fourths, virtual triage helped users decide level of care to pursue. Among 74.1%, triage recommended care different than pre-triage intentions. Post-triage, those who remained uncertain of their care path decreased by 25.4%. Patient-user experience and satisfaction with virtual triage was high, with 80.1% stating that they were highly likely or likely to use it again, and interest in and willingness to use telemedicine doubled. Conclusion: Virtual triage successfully redirected patient-users who initially planned to seek an inappropriate level of care acuity, reduced patient uncertainty of care path, and doubled the percentage of patients amenable to telemedicine and virtual health engagement. Patient-users were highly satisfied with virtual triage and the virtual triage patient experience, and a large majority will use virtual triage recurrently in the future.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».