Despite extensive efforts in data collection, quality and safety measurement remains a significant global challenge, with limited understanding of how and under what conditions quality and patient safety surveillance systems function effectively. With the aim of informing the development and effective functioning of quality and patient safety surveillance systems, a rapid realist review was conducted to develop a set of theories that address how, why, for whom and in what context quality and patient safety surveillance systems work.

Rapid realist review to inform recommendations and intervention design for the monitoring and evaluation phase of the QS Signals Project, reported according to Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) guidelines.

Initial programme theories were constructed based on data collected from key articles on quality and patient safety surveillance systems, consultation with an expert panel, informal meetings with a project team charged with developing a quality and patient safety surveillance system for maternal and infant health and a review of the project’s planning documents. A three-phase iterative search of PubMed, PsycInfo, CENTRAL, CINAHL and grey literature was conducted, including studies in healthcare settings across all patient groups.

Documents were assessed for relevance (alignment with the theory under test), richness (depth of insight) and rigour (trustworthiness and coherence of data).

Context–mechanism–outcome configurations were generated, iteratively refined and grouped under relevant programme theories to contribute to theory refinement.

The review process resulted in the development of 11 final programme theories, identifying mechanisms operating at organisational and national levels. Effective systems were enabled by leadership commitment, organisational readiness for change and a supportive safety culture. Clear governance structures, including defined local and national roles, strengthened accountability and coordination. The establishment of multidisciplinary clinical advisory groups facilitated the selection of meaningful safety indicators. Sustainable financial investment and adequate human and technical resources were critical for implementation. Robust data governance frameworks enhanced trust, transparency and appropriate data use. User-centred system design improved data accessibility and usability, while feedback loops supported learning and continuous improvement.

Quality and patient safety surveillance systems function most effectively when supported by strong leadership, clear governance structures, adequate resources and a learning-oriented culture that enables the meaningful use of safety data. The findings emerging from this review provide comprehensive, practical and testable systems-level programme theories to inform future research on the development of quality and patient safety surveillance systems across diverse healthcare settings and international contexts.