Boosting the hospital by integrating mobile robotic assistance systems: a comprehensive classification of the risks to be addressed
Bibliographic record
Abstract
Abstract Mobile service robots are a promising technology for supporting workflows throughout the hospital. Combined with an understanding of the environment and the current situation, such systems have the potential to become invaluable tools for overcoming personal shortages and streamlining healthcare workflows. However, few robotic systems have actually been translated to practical application so far, which is due to many challenges centered around the strict and unique requirements imposed by the different hospital environments, which have not yet been collected and analyzed in a structured manner. To address this need, we now present a comprehensive classification of different dimensions of risk to be considered when designing mobile service robots for the hospital. Our classification consists of six risk categories – environmental complexity, hygienic requirements, interaction with persons and objects, workflow flexibility and autonomy – for each of which a scale with distinct risk levels is provided. This concept, for the first time allows for a precise classification of mobile service robots for the hospital, which can prove useful for certification and admission procedures as well as for defining architectural and safety requirements throughout the design process of such robots.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".