Assessment of Qatar’s Health Care Community Call Center Efficacy in Addressing COVID-19 Pandemic Health Care Challenges: Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: The global COVID-19 pandemic caused by SARS-CoV-2 created many unprecedented challenges for health care organizations worldwide, placing a great deal of strain on the health care systems, especially access to health care services. To address these challenges, Qatar established a centralized digital platform as a community call center, initially offering digital consultations via its hotline (number: 16000) and later expanding to include a COVID-19 vaccination hotline (number: 7077) for mass immunization. OBJECTIVE: This study aims to comprehensively examine the community call center's operations and their significant role during the COVID-19 pandemic. METHODS: Retrospective data were collected from the Health Information and Technology Department of the Primary Health Care Corporation, Qatar, from March 29, 2020, to January 27, 2022. Data analysis for the hotline (number: 16000) focused on telephone and video call volumes, call response rates, abandonment rates, and call classification. In addition, data from the COVID-19 vaccination hotline (number: 7077) were analyzed for call volumes, call response rates, abandonment rates, appointment booking rates, confirmations, rescheduling, and cancellations. RESULTS: The hotline (number: 16000) received a substantial total of 429,212 calls, with 284,849 (66.37%) calls effectively answered. The average number of calls received per day during the study period was 640.61 (SD 470.53), and the average number of calls answered per day was 425.14 (SD 206.64). Notably, of the total 128,468 consultations, video consultations were conducted for 3810 (2.96%). Among the diverse call categories, diabetes mellitus (6284/84,299, 7.45%), prescriptions and medications (4709/84,299, 5.59%), hypertension (3874/84,299, 4.6%), vitamin D-related issues (3770/84,299, 4.47%), upper respiratory tract infections (2690/84,299, 3.19%), and COVID-19-related inquiries (2590/84,299, 3.07%) were most frequently addressed. For the COVID-19 vaccination hotline (number: 7077), an impressive total of 1,512,354 calls were received, with a 58.27% (n=881,305) call response rate. The average number of calls per day during the study period was 3828.74 (SD 2931.94), and the average number of calls answered per day was 2231.15 (SD 1496.02). Appointment booking accounted for 26.37% (265,721/1,007,596), appointment confirmation accounted for 10.24% (103,136/1,007,596), rescheduling accounted for 7.95% (80,124/1,007,596), and cancellations accounted for 1.6% (16,128/1,007,596) of the calls. CONCLUSIONS: The findings of this research highlight the crucial significance of the community call center hotline (number: 16000) and the COVID-19 vaccination hotline (number: 7077) in effectively addressing the multifaceted challenges posed by the global COVID-19 pandemic. In Qatar, the community call center emerged as an indispensable and accessible centralized resource, facilitating streamlined digital consultations and vaccination appointments. The impressive call response rate highlights its operational efficiency, adeptly managing a diverse range of health-related issues. This study emphasizes the critical role of community call centers in health care emergency response, signaling their potential as invaluable assets for future preparedness and effective mitigation strategies during similar public health crises.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».