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Enregistrement W2153948706 · doi:10.5812/aapm.16468

Robotic Anesthesia: How is it Going to Change Our Practice?

2014· article· en· W2153948706 sur OpenAlex
Arthur Atchabahian, Thomas M. Hemmerling

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Notice bibliographique

RevueAnesthesiology and Pain Medicine · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueAnesthesia and Sedative Agents
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésMedicineAnesthesiaDexmedetomidineMedical education

Résumé

récupéré en direct d'OpenAlex

In the relatively brief course of its history since the days of open ether inhalation, anesthesiology has undergone multiple radical or incremental changes. Endotracheal intubation and the introduction of muscle relaxants, continuous EKG monitoring, pulse oximetry and capnography, less toxic, shorter acting agents, processed EEG monitoring, and ultrasound guided regional anesthesia, among others, have completely transformed our practice. We would not conceive today of administering an anesthetic without access to these technological advances. Computerized recordkeeping is in the process of freeing practitioners from the rote task of copying to paper data that computers can easily store. Yet progress has been rather slow compared for example to computer science or aviation: only 65 years elapsed between the Wright brothers’ first flight and both a supersonic commercial airplane and man walking on the moon. While most practitioners are aware of the progress of robotic surgery, especially for prostatic surgery, robotic anesthesia has gained rather little exposure until now. Impressive progress has been made, however, such as closed loop systems (1), intubating robots (2) or regional anesthesia robots (3). Despite uncertainty on how to measure all components of anesthesia, and especially analgesia (some researchers are using derivatives from the bispectral index, such as the variance of the BIS value or the EMG component, although it is unclear how these reflect clinically acceptable surrogates of pain (4)), closed loop systems will actually enter clinical practice very soon. The Sedasys system, that administers propofol sedation titrated to the processed EEG and vital signs to patients undergoing endoscopy without direct supervision by an anesthesia provider, was recently approved by the Food and Drugs Administration in the United States. As industrial robots, once relegated to working behind fences lest they injure humans standing in the wrong place, are fitted with sensors and safety systems that allow them to work alongside humans, we cannot help thinking that these “collaborative robots” will soon be assisting us in our daily tasks in the operating room. The first question most colleagues ask when robotic anesthesia is discussed is “are we going to lose our jobs?” Most artificial intelligence specialists speculate on the occurrence of the Singularity, the time at which computers will match then surpass human intelligence, and predict it to occur sometime between 2030 and 2045. While the broad consequences of such an event are unpredictable and beyond the topic of this editorial, this would make human anesthesia providers redundant; however, that would be true of most other sectors of human activity. Ultimately, we might lose our jobs, but so will everyone else. The current priority is to address the question of how those changes will impact our daily practice. Technological progress has constantly upset societal order. For example, Luddites in the 19th century destroyed the first mechanical looms that they thought threatened their livelihood. The Industrial Revolution transformed first England, then most of the Western world, beyond recognition. Closer to us, the rise of computers, the internet, mobile telephony and data connections has changed our daily life to an extent that was in the realm of science fiction only a few decades ago. The technological improvements in the field of anesthesiology, noted above, have made anesthesia significantly safer. However, we must also recognize that they have led to a loss of clinical skills among younger practitioners, who tend to rely on tests and monitors rather than examining the patient. While robotic assistance for anesthesia is being rolled out, we can focus on those tasks that humans perform better than computers. Robots can help human practitioners improve care by increasing their precision and reliability, aiding their vigilance, and freeing them up to focus on higher level tasks and procedures. Humans are flexible and are better at problemsolving than machines, but they take poorly to repetitive tasks that quickly lead to boredom, fatigue and a drop in vigilance as well as low morale. The assumption of researchers is that robotic assistance during anesthesia will make our profession more enjoyable and even safer by decreasing the menial aspects that machines do well, simplifying the documentation, and allowing us to focus on the patient rather than the equipment and the paperwork. The risk, obviously, is overreliance on the technology and a paradoxical drop in vigilance. Ergonomics, i.e. adjusting the environment to the needs of the humanrobot team, might help reduce that risk by providing feedback in a form that is informative yet not overwhelming, and highlights the essential. Economic considerations might include a reduction in the cost of care, provided that the cost of the equipment decreases enough due to economies of scale, and a need for fewer “higher level” practitioners (physicians rather than nurse anesthetists or anesthesia assistants) per patient. However, as the population ages and more surgical procedures are performed, that should not involve a decrease in the number of positions available. Experience with industrial robots shows that while workers initially fear losing their jobs, companies often end up hiring more personnel because production costs drop. Workers warm up quickly to the robots and, as they do the programming themselves, tend to see the robots as subordinates rather than a threat. Research is also ongoing on the ways to improve robot acceptability and likability. For anthropomorphic robots, gestures accompanying speech increase their likability. Interestingly, mildly incongruent gestures, suggesting that the robot could make mistakes, made the robot even more likable (5). Whether that is desirable in a medical setting is debatable. The question is “How can robotic anesthesia enter the daily practice, in which useful and structured way, allowing a smooth transformation from the present state of development towards the future of anesthesia?” One could envision a 3 step introduction:

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,444
Score d'incertitude au seuil0,965

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,060
Tête enseignante GPT0,326
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle