About this work package
This work package focuses on clinical training and assessment. It aims to use quantitative and objective measures for evaluating instrument manipulation, managing intraoperative imaging modalities and interacting with the target anatomy to build an effective training programme. The majority of such motion metrics are based on motion analysis; by establishing assessment metrics we will monitor the learning curves of trainees and be able to guide the development and ergonomics of our platform.
We have established a relationship between novice and expert performance when performing obstetric ultrasound in a simulated setting.
High quality training in ultrasound is challenged by several, interrelated problems:
- The ultrasound is operator dependent and has high inter-operator variability.
- There is little global consensus on how to train, assess and evaluate skill in obstetric ultrasound.
- Simulation has been proposed as a strategy to shorten training time and to allow clinicians learn in a safe, blame-free environment.
- Like laparoscopic surgery, ultrasound requires the operator to interpret a dynamic 2D image representing 3D anatomy. Motion analysis has been studies extensively in laparoscopy.
Published literature supports the use of simulation, but its implementation varies.
We aim to investigate similar assessment strategies in obstetric ultrasound and examine how learning curves differ between novices and experts. We hypothesise that objective measurements of hand and eye movements during obstetric ultrasound and fetal diagnostic procedures, such as amniocentesis, can identify experts from trainees. We are undertaking a study collecting information on operator hand and eye movements as they perform an ultrasound scan. We aim to use this data to quantify performance difference between expert and novice operators.
Work package tasks
Establishing Objective Metrics for Assessment of Skills
There is little global consensus on how to train, assess and evaluate skill in obstetric ultrasound. Curricula, where present, are often based on clinical volume, rather than objective outcomes of competence. The acquisition of complex psychomotor tasks follows a learning curve, but they have not been objectively measured or classified.
Dimensionless Jerk has been proposed as an objective parameter in endoscopic surgery where similar issues are faced. Inspired by comparable training challenges in endoscopic surgery, we observed operator behaviour by tracking the ultrasound probe during an ultrasound scan.
We included experienced and novice operators and describe measurable and reproducible performance differences between the groups. When time and dimensionless jerk are considered together, the experienced group had less unwanted and more purposeful movements. Our results show that quantifiable performance changes can be defined using objective measures
Assessing Objective Measurements of Skills in Simulation and Clinical Environments
Ultrasound is a dynamic, real-time imaging modality that is widely used in clinical obstetrics.
Widely accepted, validated and objective measures of ultrasound competency have not been established for clinical practice. Simulation has been proposed as a training method, but how learners’ performance translates from the simulator to the clinic is poorly understood.
We have previously described an objective metric, Dimensionless Jerk (DJ), which can differentiate between novice and experienced operators when scanning a commercially available phantom of a 23 week fetus. We now test DJ in the clinical setting and hope to publish our results soon.
Sign up for our studies
The Computer Assisted Quantification of Learning Curves in Obstetric Ultrasound and Invasive Procedures (CAL-Obs) study aims to find out how healthcare practitioners move when they perform ultrasound scans and examine how the hand and eye movements of a less experienced operator differ from an experienced one.
Our abstract entitled “Quantifying Expert Performance in Obstetric Ultrasound Using Probe Tracking Systems: Data to Improve Training” was presented at BMFMS: British Maternal & Fetal Medicine Society in March 2019 and was awarded First Prize in the student researcher category.
A Systematic Review and Meta-analysis of the Use of High-Fidelity Simulation in Obstetric Ultrasound. Dromey, B. P., Peebles, D. M., & Stoyanov, D. V. (2020). Simulation Healthcare. doi:10.1097/SIH.0000000000000485
Quantifying expert performance in obstetric ultrasound using probe tracking systems: data to improve training. Dromey, B. P., Ahmed, S., Vasconcelos, F., Mazomenos, E., David, A. L., Ourselin, S., . . . Peebles, D. (2019). Conference presentation.