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

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Integrating anesthesiology data is key for artificial intelligence (AI) adoption. Current systems hinder AI translation due to poor data integration, not algorithm performance.

Keywords:
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Area of Science:

  • Perioperative Medicine
  • Health Informatics
  • Artificial Intelligence

Background:

  • Anesthesiology generates vast, heterogeneous perioperative data, including physiological signals and clinical documentation.
  • Despite data richness, clinical deployment of artificial intelligence (AI) systems is limited.
  • Existing systems often lack interoperability, hindering AI integration into practice.

Purpose of the Study:

  • To review how data integration and systems interoperability limitations constrain AI translation in anesthesiology.
  • To identify barriers to AI adoption in perioperative care.
  • To propose solutions for enabling AI in anesthesiology.

Main Methods:

  • Literature review of recent studies on AI in anesthesiology.
  • Analysis of data integration challenges in perioperative data systems.
  • Examination of interoperability standards and their implementation.

Main Results:

  • The primary barrier to AI adoption in anesthesiology is inadequate data integration, not algorithmic performance.
  • Data are fragmented across systems (AIMS, EHRs, devices) with variable models and synchronization.
  • Emerging standards (HL7 FHIR, common data models) offer frameworks but have partial implementation.

Conclusions:

  • Effective AI deployment in anesthesiology requires interoperable, high-quality data infrastructures.
  • Standardized data models, semantic harmonization, and temporal alignment are crucial.
  • Robust data governance is a prerequisite for scalable, trustworthy AI-enabled perioperative care.