Transatlantic Medical Education: Preliminary Data on Distance-based High-fidelity Human Patient Simulation Training
Bibliographic record
Abstract
Advanced training using Human Patient Simulators (HPS) is, for the large part, unavailable for the majority of healthcare providers in rural, remote, and less developed regions of the world--either due to their separation from the major medical education centers or significant fiscal austerity. Remote access to HPS based on the Applications Software Provider principles may provide the solution to this problem. The medical ASP (MED-ASP) concept proposed and developed by MedSMART has been subjected to an extensive qualitative and quantitative international test conducted among France, Italy, and USA. Two SimMan HPSs (Laerdal, Norway) were used, with one unit based in Ann Arbor, MI, USA, and one in Laval, France. While the French site had both remote and hands-on access to the simulator, the Italian site could access the HPS only remotely. Simulator visualization was provided by 4 remotely operated cameras (Sony, zoom, pan, tilt) at each HPS site. HPS-generated vital signs were transmitted to each site together with the interactive simulator control panel using a communications hub at the MedSMART facility in Ann Arbor. All remote interactions were performed via the Internet (TCP/IP) using ISDN and/or ADSL connections at minimum 128 Kbps. During the course of training, the trainees were exposed to 3 emergency scenarios with the remote expert providing instruction. Interventions were performed either remotely (Italy) or remotely and hands-on (France). Quantitative measurement of the efficiency of training was performed at the Italian site based on the evaluation of video recordings of each session and the assessment of several performance measures. At the end of the training program, a Likert scale-based assessment test was also given. The trainees showed statistically significant (p<0.03 - 0.05) improvement in all testing measures. The Likert scale questionnaire revealed overwhelming satisfaction with the simulation-based distance training even when the access to the simulator was only remote (Italy). Confidence was also significantly improved. The trainees indicated the optimal frequency of distance training as one 2 hour-long session twice a month. In conclusion, simulation-based distance medical training proved to be a highly effective tool in improving emergency medical skills of junior physician trainees and, despite initial reservations, neither distance nor language and cultural differences posed significant obstacles. The present and historical data from our previous work confirm the concept of MED-ASP as a highly efficient tool in both national and international medical education and training. Moreover, we now validate for the first time the concept of simulation-based, fully interactive transatlantic medical ADL that we have proposed in our previous theoretical papers. The present experiments prove that training based on advanced technologies transcends barriers of distance, time, and national medical guidelines. Hence, international simulation-based distance training may ultimately provide the most realistic platform for a large-scale training of emergency medical personnel in less developed countries and in rural/remote regions of the globe.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".