Comparing Patient Simulation With a Humanoid Robot or a Human Actor in Terms of Training Success and Acceptance: Pilot Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The addition of simulated patients to medical and nursing training makes it possible to create a link between theory and practice. This makes what has been learned more realistic and allows the complexity and multilayered nature of many illnesses to be reflected in a real-life setting. However, the selection, training, and supervision of actors as simulated patients is time consuming and expensive. In this study, we investigated how differently students and nurses perceive 2 different methods of patient simulation. OBJECTIVE: The aim of this pilot study was to investigate whether patient behavior simulated by a humanoid robot is comparable to patient simulation by actors in videos in terms of training success and user acceptance. Participants were asked to recognize the symptoms presented by the humanoid robot and make a diagnosis. For comparison purposes, we asked a second group of participants to make a diagnosis based on a video featuring a human patient actor. METHODS: We asked the participants (medical students and nursing staff; N=21) to conduct a psychopathological assessment. Group 1 (n=11) used the humanoid robot as a patient simulator, and group 2 (n=10) watched the identical symptoms in a video with a human actor as patient. RESULTS: The participants had a mean age of 28.7 (SD 3.5) years. The students were in their sixth semester and had, on average, 7.6 (SD 3.3) years of professional experience in the medical field. The correct diagnosis was made 90% (9/10) of the time based on the video with the human patient actor and 91% (10/11) of the time based on the robot. One participant in each group made the wrong diagnosis, constituting a total error rate of 10% (2/21). In general, participants with the humanoid robot as patient simulator felt more confident that their diagnosis was correct compared to those with the human actor as patient (humanoid robot: 9/11, 82% were neutral to very confident and 2/11, 18% were uncertain to very uncertain; human actor: 9/10, 90% were neutral to very confident and 1/10, 10% were uncertain or unsure). CONCLUSIONS: The simulations of the human actor in the video were judged to be more realistic overall than those of the humanoid robot as patient simulator. However, the differences between the simulation methods in relation to the result (diagnosis) were very small. The results of our pilot study show a good performance of the robot in the simulation of selected psychiatric patient cases. We conclude that a humanoid robot could be a useful addition to patient simulators in medical education and discuss future directions.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".