Telehealth Initiative to Enhance Primary Care Access in Brazil (UBS+Digital Project): Multicenter Prospective Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".