Telehealth Initiative to Enhance Primary Care Access in Brazil (UBS+Digital Project): Multicenter Prospective Study
Notice bibliographique
Résumé
BACKGROUND: Brazil faces significant inequities in health care access, particularly in remote communities. The Brazilian Unified Health System is struggling to deliver adequate health care to its vast population. Telehealth, regulated in Brazil starting in 2022, emerged as a solution to improve access and quality of care. Thus, the Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, in partnership with the Agência Brasileira de Apoio à Gestão do Sistema Único de Saúde, created the Unidade Básica de Saúde (UBS)+Digital project, which aimed to mitigate the lack of medical care in remote areas of Brazil by providing teleconsultation in primary health units (PHUs) across the country. Through teletraining and digital health strategies, the initiative enabled health care professionals to provide remote assistance, improving access to medical care. OBJECTIVE: To describe the implementation and results of the UBS+Digital project, a telehealth initiative focused on training health care professionals, providing teleconsultations, and monitoring key performance indicators among PHUs in Brazil. METHODS: The study examined 15 Brazilian PHUs using a multicenter, prospective design. Data were collected through anonymous surveys of patients and physicians, which were recorded in the REDCap (Research Electronic Data Capture) database. PHUs were selected based on criteria such as the absence of an on-site physician and existing technological infrastructure. Synchronous and asynchronous training was provided, focusing on digital health and teleconsultation skills. In loco training included workshops and community events to share experiences and foster local engagement. A community of practice facilitated ongoing knowledge exchange. Teleconsultations followed the person-centered clinical method and Calgary-Cambridge methodology. Key performance indicators were monitored by a dashboard to guide continuous improvement. The transition of operations was managed based on physician availability and project duration. Microcosting analysis assessed the project's economic impact using Brazilian guidelines, with statistical analysis performed using Jamovi software. RESULTS: From March to November 2023, the project conducted 6312 telehealth sessions. A total of 342 professionals were trained, including participants from all three training modalities that were implemented. The Net Promoter Score for teleconsultations was 97, indicating excellent service quality. Of the teleconsultations, 65.3% (4009/6140) were prescheduled, and 34.7% (2130/6140) were on demand, depending on the family health team organization. Teleconsultations resolved 85% (5219/6140) of cases, with 15% (921/6140) requiring in-person referrals or emergency care. The average absenteeism rate was 15% (1083/7223), and consultation durations were between 15 and 20 minutes, suggesting potential adjustments in scheduling. CONCLUSIONS: The results highlight the effectiveness of telehealth programs in primary care settings with limited medical professionals. The UBS+Digital project demonstrated that telehealth can enhance health care access, presenting a pioneering model within the Brazilian Unified Health System for digital primary care.
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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 | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 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 ».