Comprehensive Evaluation of Quality Indicators: Analyzing the Dutch Breast Cancer Audit.

Abstract

Background: Quality indicators (QIs) are widely used to benchmark hospital performance and improve quality of care but are often based on expert opinion rather than data-driven assessment. This study aims to evaluate QIs, using a framework that assesses four criteria: Feasibility, discriminative ability, validity, and reliability.

Methods: We used data from the Dutch breast cancer registry (NABON Breast Cancer Audit, NBCA) and included all surgically treated breast cancer patients in the Netherlands between 2021-2023. Eighteen QIs were evaluated. Feasibility was determined by QI numerator completeness, with >90% data availability considered feasible. Discriminative ability was assessed by between-hospital variation in QI scores, where an interquartile range (IQR) >10% indicated good discrimination. Validity was evaluated by the impact of case-mix adjustment and considered low when (pseudo-) R² <0.10. The (pseudo-)R² reflects the proportion of variance in QI scores explained by all case-mix variables in a regression model. Reliability was assessed by rankability, the proportion of between-hospital variation not due to chance and, therefore, explainable by quality of care. Rankability >75% was considered high.

Results: After exclusion of one QI with feasibility <1%, feasibility ranged from 80.2%-100%, and 15 QIs were feasible. Overall, the IQR ranged from 1 to 36, with 8 QIs having an IQR higher than 10, indicating good discriminative ability. The (pseudo-)R² ranged from 0.01-0.53, with 11 QIs showing low case-mix impact. Rankability ranged from 0-69%, with none of the QIs having a high rankability. None of the QIs met all preset criteria, but six QIs met at least three out of four criteria.

Conclusion: The QIs employed by the Dutch breast cancer registry fulfilled most criteria, but rankability is a concern and requires specific attention, especially for public reporting. Our results show the importance of considering feasibility, discriminative ability, validity, and reliability when evaluating QIs, and these should also be taken into account when developing new QIs.