Develop universal objective assessment and feedback methods that go beyond time and motion to capture tissue handling and interaction forces¶

Strategy · All strategies
Evidence · 4 claims (3 for, 1 mixed) · 3 studies (1 quant-synthesis, 1 review, 1 associational), q2–q3 · 0 of 3 report an effect size · 4 claims rest on one study

Description¶

The review concludes that focused training on tissue handling, manipulation, and force interaction is lacking despite the absence of haptic feedback, and recommends that "Future research should focus on developing universal objective assessment and feedback methods to address these limitations". It further suggests that objective feedback should cover interaction forces on tissue and suture quality, and that prediction models and machine learning could predict the necessary training load for each trainee.

Design Implications¶

Context¶

Requirements¶

• Objective parameters for assessing skill acquisition and reaching preset proficiency levels
• Continuous validation of assessment forms for new training tasks or procedures

Constraints¶

• Current subjective forms such as GEARS and OSATS have limited generalizability and depend on expert presence or post hoc evaluation, with interrater variability
• Current VR technology cannot simulate realistic virtual tissue and instrument interaction forces

Target Learners¶

• surgical trainees using robotic-assisted surgery systems

Target Learning Goals¶

• tissue handling and manipulation
• force interaction skills
• technical surgical skill proficiency

Claims¶

• Sensor-based force measurement discriminates skill levels, with experts suturing faster and with lower applied force than novices [+M]
• Excessive tissue handling forces can cause severe complications, with reported safety thresholds between 1.3 and 11.4 Newton depending on tissue type and task [+M]
• Computer vision and machine learning enable objective, automated surgical skill assessment [+M]
• Most data-driven assessment tools in laparoscopic training focus on instrument handling, with only three studies evaluating tissue handling skills [~M]

Related Strategies¶

• Structure robotic surgery curricula as a stepwise progression in which training modalities complement rather than replace each other

Examples¶

-

Key Sources¶

• Rahimi A. Masie, Uluç Ezgi, Hardon Sem F., Bonjer H. Jaap, van der Peet Donald L., Daams Freek. (2024). Training in robotic-assisted surgery: a systematic review of training modalities and objective and subjective assessment methods. Surgical Endoscopy. https://doi.org/10.1007/s00464-024-10915-7