Training and assessment of competencies in hysteroscopy - A systematic review

OBJECTIVE: Hysteroscopy is a core gynecological competence, acquired by training. Simulation training offers safe competence acquisition, but requires resource-efficient, clinically transferable approaches. Current hysteroscopic training curricula lack standardized assessment and validated pass-fail criteria. This review synthesizes evidence to inform optimized training and robust competency assessment.

METHODS: Five databases were systematically searched on June 2nd 2025. Studies assessing hysteroscopy training and skills assessment were included without restrictions on population, study design, or publication year. Data extraction was performed using Covidence. Outcomes were evaluated with Kirkpatrick's levels and quality assessment was evaluated with the Medical Education Research Study Quality Instrument (MERSQI).

RESULTS: A total of 39 studies, 36 prospective cohort studies (92 %) and 3 RCTs (8 %), met the inclusion criteria. Studies used animal (13 %), inanimate (33 %), virtual reality (39 %), and combinations of models (15 %). Training approaches varied, with numbers-based training being most common (74 %). Outcomes were primarily evaluated with pre-post testing (41 %) and self-assessment (28 %). All studies reported improved performance, especially in novices, with either pre- and posttests or questionnaires. Study quality varied, with a mean MERSQI of 11.3 (range 5-16, of possible 5-18). Only one RCT showed clinical transferability.

CONCLUSION: Simulation-based training enhances hysteroscopic competencies. Despite methodological variability, evidence supports structured curricula combining theory, hands-on training, and pass-fail-based progression. A combination of virtual reality with tactile feedback seems optimal. Overall evidence failed to establish optimized training and robust competency assessment. Future research should include high-quality studies, patient-level outcomes, and updated validity frameworks to decide how to optimize training effectively.