Medical education is a high-stakes climate of continuous monitoring, improvement, and innovation with an obligation to develop competent physicians. As the complexities of clinical knowledge and technologies rapidly progress, education stakeholders are transitioning to a competency-based medical education (CBME) model. The assessment of initial and ongoing technical skills proficiency represents a significant challenge in CBME, namely because of the nuances, variation, and unpredictable learning curves associated with medical procedures. This is especially true for Point-of-Care Ultrasound (POCUS), an ultrasound performed at the bedside to answer an immediate clinical question, which has not made any meaningful transition away from frequency-based competency due in part to the significant time and personnel investments required for POCUS training and testing. Yet, as the modality continues to integrate into regular emergency medicine practice, clinicians with no prior ultrasound experience, like resident physicians, are expected to establish and maintain proficiency in POCUS-based technical skills.
Hand Motion Analysis (HMA), a groundbreaking technology employing electromagnetic sensors affixed to a subject’s hands or instruments that gauge their position and movement through space, offers an objective and replicable method of assessing the performance of technical skills. As one gains proficiency in a given technical skill with training or time, HMA measurements can capture this growth, revealing an increasing economy of motion and thus serving as a reflection of improving procedural competency. Having been previously deployed by some surgical specialties, HMA has demonstrated consistent evidence showing its capability to differentiate between novices and experts.
The potential for integrating HMA technology into ultrasound training suggests that the culmination and synthesis of motor patterns required in POCUS image acquisition, integral to a learner’s path to competency, could be efficiently quantified. HMA could enable more frequent and rapid assessment of POCUS image acquisition, inject objectively measurable data into a traditionally qualitative training process, significantly improve program efficiency, and provide invaluable feedback for both educators and learners. Such an advancement could finally steer skills proficiency away from frequency-based requirements and facilitate widespread acceptance of CBME in POCUS training.Regardless of the complexity or infrequency of technical skill execution, objective performance tracking provides the ability to guide training, monitor proficiency, and provide insights related to competence decisions. HMA may even facilitate the detection of skill atrophy over time. To fully harness the potential of HMA in POCUS training, studies are needed to establish motion thresholds that define competence in specific POCUS-based technical skills.