Technical challenges and solutions for high-fidelity patient simulation in support of telemedicine research in remote and extreme environments
Bibliographic record
Abstract
Telemedicine is emerging as a valuable tool to improve access to health care in pre-hospital and acute care settings. Unfortunately, clinical research and the development of telemedicine technologies are severely limited by the extreme variation in experimental variables alongside legal and ethical concerns. High-fidelity medical simulation, the use of a robotic manikin that mimics a human patient, may overcome some of these challenges by allowing rare or complex medical procedures to be reproduced in a controlled manner. Improvements in simulation technology have increased the portability of these systems and enabled their use in remote and extreme environments. These locations have limited access to medical resources and expertise, and may benefit greatly from telemedicine support. This research describes initial attempts at defining the requirements for running high-fidelity simulation in remote and extreme environments in support of telemedicine research. Three additional variations were evaluated to explore possible applications: 1) remote operation and instruction of the simulation, 2) progressive simulations (movement from one location to another), and 3) telemedical support under time delay. iv Fourteen 30-minute simulations were conducted on Devon Island, Nunavut, Canada, and on Mauna Kea, Hawaii, USA. Two medically naïve participants rendered care to a simulated patient experiencing an acute medical emergency. Participants were connected through a videoconferencing link over satellite to an experienced physician who provided medical support. All but one simulation was completed successfully, however, all encountered unanticipated technical barriers related to the videoconferencing system, network connection, or simulation technology. The results show that running high-fidelity patient simulation in extreme environments is technically feasible. They also highlight the importance of rigorous pre-deployment system testing and an appreciation of the effect of network infrastructure and environmental conditions on equipment. Nevertheless, it is clear that high-fidelity simulation holds the potential to unlock new and exciting findings in acute care telemedicine research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".