Enhancing clinical practice: the Endoscore app for automated surgical data capture and endometriosis scoring

There is a growing unmet need to digitalise the management of clinical data in medicine. Web-based scoring applications for endometriosis align with this trend.

This study aimed to evaluate a web-based application that automatically calculates endometriosis staging scores [revised American Society for Reproductive Medicine classification (r-ASRM), the revised Enzian classification (#Enzian), Endometriosis Fertility Index (EFI)] and compare it to manual scoring in a proof-of-concept study.

20 endometriosis cases operated on in 2022 were retrospectively selected. Six experienced gynaecologists were randomly allocated to either the conventional paper-based method or the digital application for staging of disease.

Completion time, score consistency among examiners and methods, and user satisfaction were recorded using a Likert scale and a subjective mental effort questionnaire (SMEQ).

In comparison to the paper-based method, the web-based tool reduced scoring time by 25.1 seconds (128.0 vs. 153.1, P<0.05), was perceived as easier to use (higher Likert scale scores), and was associated with low-to-moderate mental effort on the SMEQ. The agreement between electronic and paper forms was rated as very good to excellent for r-ASRM [intraclass correlation coefficient (ICC): 0.93] and #Enzian (ICC: 0.84), while it was moderate for EFI (ICC: 0.67). Interrater agreement utilising the electronic form demonstrated high levels, yielding very good to excellent results for r-ASRM (ICC: 0.93) and EFI (ICC: 0.82) while showing moderate agreement for #Enzian (ICC: 0.63).

The application facilitates sequential data entry for users and automatically calculates r-ASRM, #Enzian, and EFI scores. It decreases scoring duration, strongly aligns with the paper-based method, and enhances user satisfaction.

This tool can potentially improve clinical efficiency, accuracy, and consistency in the staging of endometriosis.