Synergy of wearable technologies and proficiency-based progression for effecting improvement in procedural skill training

Artificial intelligence (AI) refers to the capacity of a computer to perform operations analogous to learning and decision making in humans. Machine learning is one advanced application of AI concerned with developing computer programs that automatically improve with experience. Although it has existed for more than 50 yr, there has been recent interest in the potential for applying machine learning and big data (in combination) to clinical medicine, including anaesthesia and critical care. 1 This would have important implications for the practice of anaesthesia and, even sooner, for the training of anaesthetists. Machine learning and medical procedures Of the many aspects of medicine that might benefit from machine learning, its application to improving the performance and outcome of medical procedures represents a special case. First, medical procedures are the source of a significant proportion (13e45%) of medical errors. 2e4 Given that medical errors occur commonly and account for much death and disability 5,6 , improving procedural healthcare is a high priority. Second, advances in robotics, navigation systems, sensorised instruments, and image-guided interventions provide ready-made sources of data that could be used to 'train' a machine or decision support software. Third, the paradigm shift to competence-based training in medicine has resulted in the codification of observable behaviours that make up the procedure into specific steps and errors, which define proficient performance. The An example: ultrasound-guided peripheral nerve block Consider ultrasound-guided peripheral nerve block as a possible subject for application of machine learning. Video of

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Machine learning and evidence-based training in technical skills

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Srinivasan

Reinertsen

2018

British Journal of Anaesthesia

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Datafication of medical device use for training and device design

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2018

The Lancet

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Contemporary training methods in regional anaesthesia: fundamentals and innovations

2021

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Summary Over the past two decades, regional anaesthesia and medical education as a whole have undergone a renaissance. Significant changes in our teaching methods and clinical practice have been influenced by improvements in our theoretical understanding as well as by technological innovations. More recently, there has been a focus on using foundational education principles to teach regional anaesthesia, and the evidence on how to best teach and assess trainees is growing. This narrative review will discuss fundamentals and innovations in regional anaesthesia training. We present the fundamentals in regional anaesthesia training, specifically the current state of simulation‐based education, deliberate practice and curriculum design based on competency‐based progression. Moving into the future, we present the latest innovations in web‐based learning, emerging technologies for teaching and assessment and new developments in alternate reality learning systems.

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Synergy of wearable technologies and proficiency-based progression for effecting improvement in procedural skill training

Cited by 3 publications

References 6 publications

Machine learning and evidence-based training in technical skills

Machine learning and evidence-based training in technical skills

Datafication of medical device use for training and device design

Contemporary training methods in regional anaesthesia: fundamentals and innovations

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