Leveraging real-world data for continuous evaluation of computational clinical practice guidelines

There is a bidirectional interaction between clinical practice guidelines and clinical care, with each informing the other. Structural signalling of trends in guideline adherence in clinical practice is essential for advanced updates. Recent advances in computable care guidelines allow automated evaluation using real-world registry data. Here, we assess the feasibility by evaluating adherence to Dutch endometrial cancer (EC) guidelines.

This retrospective cohort study uses real-world data of EC patients from the Netherlands Cancer Registry (NCR) between January 2010 and May 2022. The Dutch guideline for EC was parsed into clinical decision trees (CDTs). Primary outcome was guideline adherence for multiple (sub)populations, with secondary outcomes encompassing adherence trends, recommendation implementation pace, non-adherent treatment strategies and impact of additional non-guideline-based patient and tumour characteristics on adherence.

The Dutch EC guideline was parsed into 10 CDTs, revealing 22 patient and disease characteristics and 46 interventions. NCR data were mapped to CDT data items. Four CDTs were successfully populated with NCR data, and 21 602 cases were assessed. Adherence levels were computed, which showed a mean adherence of 82.7% (range 44-100%). Three statistically significant trends in adherence were identified: two increasing trends in the 'non-adherent' compared with the 'adherent' group, and one decreasing trend.

This study introduces a novel framework for continuously evaluating (non-)adherence to cancer guidelines. Future efforts should focus on the inclusion of health outcome measurements.

Through the integration of real-world data with a computer-interpretable guideline, we effectively calculated various facets of adherence to guidelines for EC.