The Benefits of Nurse Telemedicine Triage: A Case Study from North America
Bibliographic record
Abstract
Abstract This paper describes how an energy technology company has implemented a virtual nurse triage program to ensure a consistent level of professional medical support and care for onshore operations in Canada. Many of the company's remote operational locations are without professional onsite medical services. The nurse telemedicine triage program is used to ensure personnel are provided with timely medical care and professional case management while ensuring the confidentiality of medical information. The nurse telemedicine triage program provides access to registered nurses 24 hours a day, 7 days a week, to support onsite first aid response. Standardized nurse triage protocols combined with the nurse's experience and medical knowledge are used to advise on the necessary level of care. This includes determining whether the person requires emergency medical treatment or needs to be examined by a doctor, or if onsite first aid response is sufficient. A follow-up consultation is provided within 4 hours of the initial report to assess and monitor progress. If necessary, the nurse helps to organize doctor appointments and specialist treatment. The nurse telemedicine triage program was implemented in 2023 across the company's land operational locations in Canada. The nurse telemedicine triage program has reduced the number of nonessential trips from remote locations, which often require driving in high-risk environments as a result of extreme local weather conditions. The service has proved to be an effective health, safety, and environment (HSE) engagement tool, with personnel appreciating the care shown by the company for their wellbeing. Professional case management and maintaining the confidentiality and privacy of medical data are additional positive outcomes of the program, ensuring compliance with applicable regulations and company health management requirements. The nurse telemedicine triage program has helped employees avoid unnecessary visits to public health facilities for medical issues that were not serious or urgent. The nurse telemedicine triage program has also proven to be cost effective as a result of reduced medical claims and sickness-related absences. In addition, the company saw a reduction in its industry-recognized total recordable incident rate to date in 2024 compared with 2023 following implementation of the program.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".