What are the primary care physicians and cardiologists talking about? a cross-sectional analysis of two telemedicine services in Rio de Janeiro, Brazil
Bibliographic record
Abstract
BACKGROUND: Primary care physicians (PCPs) face challenging clinical situations. Telemedicine between PCPs and specialists involving case discussions in cardiology are frequent. Assessing these interactions is essential for identifying knowledge gaps and tailoring support. In Rio de Janeiro, Brazil, two new telemedicine services provide cardiology support for PCPs: one from the Municipal Health Department using WhatsApp (Meta) and one from the Brazilian Heart Insufficiency with Telemedicine (BRAHIT) research project, which uses a web-based platform. This study analysed and compared the use of these two services in terms of their frequency, distribution among city areas, and content of the PCPs’ questions and cardiologists’ answers. METHODS: Cross-sectional study. We described the demographic characteristics of the patients whose cases were discussed and the primary care physicians’ use frequency. We classified the reasons for encounter and discussed diagnoses using the International Classification of Primary Care (ICPC-3), the question types using the Taxonomy of General Clinical Questions domains, and the specialist’s answers using an adapted version of the Champlain eConsult BASE™ research group’s classification. RESULTS: We analysed the usage data of all interactions (N = 1065) and the detailed content of a random sample (n = 346). The PCPs used the Health Department service more frequently (332/1093, 31%) than the BRAHIT project service (43/1331, 5%). The median answer time was shorter for the Health Department service (19 min) than for the BRAHIT service (two days). Most questions to the health department service were classified within the diagnosis domain, mainly regarding electrocardiography interpretation. The questions asked to the BRAHIT service were more frequently classified into treatment or management domains. The advantages and drawbacks of both models and the contributions of the findings to future implementation projects and continuing medical education opportunities are discussed. CONCLUSIONS: The two types of telemedicine services were adopted differently by the PCPs, with more frequent use and focus on diagnosis in the Health Department WhatsApp (Meta)-based service, compared with less frequent use, more centred on treatment and management topics, in the BRAHIT. Further research using standardised taxonomies for content analysis is needed to inform optimal practices in telemedicine services between providers and guide future initiatives.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".