JMIR Publications

ABSTRACT

Background:

Patient safety classification systems are widely used to organize, aggregate, and analyze data on adverse events and near misses. These systems play a central role in patient safety surveillance, research, and governance. However, health information technology (HIT)–related safety problems increasingly challenge classification-based approaches because they emerge from complex, evolving sociotechnical interactions rather than discrete, time-bounded events.

Objective:

This paper examines the conceptual and methodological limits of patient safety classification systems when applied to HIT-related incidents. Rather than evaluating individual taxonomies, the study analyzes classification as an analytic lens that shapes how safety problems are represented, interpreted, and acted upon.

Methods:

A conceptual and methodological analysis was conducted using a comparative examination of diverse patient safety classification approaches, including global frameworks, HIT-specific schemes, and locally adapted systems. Illustrative examples from the HIT patient safety literature were used to identify recurring representational tensions. The analysis focused on patterns of breakdown that arise across classificatory traditions rather than on system-specific performance.

Results:

Four recurrent breakdowns were identified across patient safety classification systems when applied to HIT-related incidents: (1) fragmentation of sociotechnical interactions, (2) loss of temporality and process, (3) poor handling of scale and propagation, and (4) normalization of “use error.” These breakdowns were found to arise from structural features of classification itself, abstraction, boundary-setting, and stabilization, rather than from deficiencies in particular systems or implementations. Consequences include partial or misleading learning, misaligned interventions, and challenges in comparing findings across studies and settings.

Conclusions:

Patient safety classification systems remain indispensable for aggregation, surveillance, and governance, but their application to HIT-related incidents entails inherent limitations in their representational capacity. These limits cannot be fully resolved through refinement or expansion of taxonomies alone. Recognizing classification as a bounded analytic lens supports more reflexive interpretation of classification-based data and more realistic expectations about what such systems can capture in sociotechnical domains. This perspective is essential for advancing patient safety research and practice in increasingly digital healthcare environments.