User Experience Regarding Digital Primary Health Care in Santarém, Amazon: Evaluation of Patient Satisfaction and Doctor’s Feedback
Bibliographic record
Abstract
BACKGROUND: With the arrival of the pandemic, telemedicine has been widely used to provide medical care and can be used to assist patients in regions far from urban centers that are difficult to access, such as riverside communities in the Brazilian Amazon region. A telemedicine project connecting São Paulo, a mega-metropolis, to Paysandú, a riverside district in the Amazon, was built to serve the local population where access to the nearest medical care is 6 hours away by speedboat. OBJECTIVE: This study aims to assess the feedback from patients and doctors regarding the use of telemedicine in outpatient care at Paysandú, a riverside district in the Amazon. METHODS: This is a single-center study following the guidelines "Evaluating digital health products" from Public Health England, with local adaptations for the project and the Brazilian reality, that was conducted between São Paulo and Santarém in Brazil. A survey was carried out with patients who were treated by a doctor in the city of São Paulo, about 2500 km from the local basic health unit, between September 27 to December 15, 2021. At the end of each teleconsultation, the attending physician answered an administrative survey form, and the patient answered a satisfaction survey. RESULTS: A total of 111 patients completed the satisfaction survey from a total of 220 consultations carried out during the period (95% CI margin error 0.22%). According to the survey, more than 95% of patients were satisfied with the service, 87.4% (n=97) had previous experience with videoconferencing, and 76.6% (n=85) reported that their demand was fully solved. Additionally, according to the hired doctor's feedback, the average duration of the consultations was between 15 and 20 minutes. Of the 220 teleconsultations performed, 90.9% (n=200) of the demands were solved with support from the local health team, and 99.1% (n=218) of the appointments had a problem with audio or video. CONCLUSIONS: This teleconsultation project between São Paulo and Paysandú showed that it is possible to offer medical care from more developed locations to communities far from urban centers, as is the case with Paysandú District. Beyond the feasibility of the infrastructure, acceptance and satisfaction among patients were high. This health care supply model has proven to be functional and should be expanded nationally or perhaps internationally to regions lacking medical assistance. Escalation of the project does not seem too difficult once infrastructure issues are solved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".