Data integration and systems interoperability: the prerequisite for artificial intelligence in anesthesiology

Abstract

PURPOSE OF REVIEW: Anesthesiology generates large volumes of heterogeneous perioperative data, including high-resolution physiological signals, clinical documentation, and device-generated information. Despite this richness, the clinical deployment of artificial intelligence systems remains limited. This review examines how limitations in data integration and systems interoperability constrain the translation of artificial intelligence into routine practice. RECENT FINDINGS: Recent literature indicates that the principal barrier to artificial intelligence adoption in anesthesiology is not algorithmic performance but inadequate data integration. Data are distributed across anesthesia information management systems, electronic health records, and multiple medical devices, with variable data models, inconsistent semantic representations, and limited temporal synchronization. These systems were primarily designed for documentation, billing, and medicolegal purposes, rather than real-time analytics or secondary data use. In contrast, intensive care medicine has benefited from early investments in shared databases, which have facilitated reproducible artificial intelligence research and validation. Although emerging standards such as HL7 Fast Healthcare Interoperability Resources and common data models provide technical frameworks for interoperability, their implementation in anesthesiology remains partial. SUMMARY: The effective deployment of artificial intelligence in anesthesiology depends on the development of interoperable, high-quality data infrastructures. Establishing standardized data models, semantic harmonization, temporal alignment, and robust data governance is a prerequisite for scalable, trustworthy artificial intelligence-enabled perioperative care.

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