The Use of Remote Presence Robotic Tele-Presentation in Rural and Remote Canada: A Systematic Review
Bibliographic record
Abstract
Objective: One potential solution to limited health care in rural and remote regions is remote presence robotic tele-presentation to allow health care providers to care for patients in their home community via a robotic interface. We synthesized evidence regarding the use of remote presence robotic tele-presentation in rural and/or remote Canadian health settings. Methods: Medline, PubMed, and Embase were searched up to August 2023. Remote presence robotic tele-presentation refers to any robotic device used for the purpose of presenting and/or collecting patient information. Primary research was included if the patient was located in remote and/or rural Canada, featured remote presence robotic tele-presentation, and assessed patient, family, or clinician satisfaction, patient transport to nearby regional or urban center, health care costs, clinical outcomes, infrastructure outcomes, adverse events, or telementoring. Results : Six studies were included. Patients, nurses, and physicians all reported high levels of satisfaction when using the remote presence robotic tele-presentation. Fifty to sixty-three percent of patients were managed in their home community and did not require transfer to another center. Remote presence robotic sonography resulted in adequate imaging in 81% of first trimester ultrasound limited exams but was less useful for second trimester complete obstetric ultrasounds (20% adequate imaging). Two of eight laparoscopic colorectal surgeries had to be converted to open surgeries. Telerobotic ultrasound clinics resulted in a diagnosis in 70% of cases. Conclusions: Evidence suggests remote presence robotic tele-presentation is a safe and cost-effective approach to providing care in distant communities and can prevent some transfers and evacuations to tertiary hospitals.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".