JMIR Publications

ABSTRACT

Background:

Contemporary cardiac surgical risk assessment is largely based on static regression-derived models that do not account for evolving perioperative data or integration within clinical workflows. Although machine learning approaches offer improved capacity to model complex relationships, most existing applications remain retrospective, outcome-specific, and disconnected from real-world clinical implementation.

Objective:

To describe the development of the Artificial Intelligence for dynamic Risk assessment and Outcome optimization (AIRO) platform, a clinician-led, governance-driven framework designed to support dynamic, explainable risk assessment and clinical decision support across the perioperative continuum.

Methods:

AIRO was developed using a formative research approach integrating retrospective model development with prospective implementation design within a learning health system framework. Baseline predictive modelling (AIRO 1.0) was conducted using a prospectively maintained cardiac surgery registry comprising approximately 30,000 patients and over 13 million data elements. A standardized modelling pipeline was established to support development of multiple outcome-specific models. The AIRO conceptual framework was structured around three layers: population-level risk stratification, individual-level risk explanation, and dynamic outcome optimization. For clinical deployment (AIRO 2.0), a system architecture was designed to enable real-time model inference within the electronic health record using interoperable data standards (FHIR), supported by a dedicated integration layer and governance framework. An implementation and evaluation strategy was defined to assess feasibility, usability, and clinician adoption.

Results:

The AIRO platform was developed as an integrated system combining standardized model development, a three-layer conceptual framework, and a scalable architecture for electronic health record integration. Baseline models have been developed for multiple clinically relevant outcomes, including surgical site infection, postoperative delirium, and discharge disposition. A workflow-integrated deployment strategy was defined, intended to enabling near–real-time risk estimation and delivery of explainable outputs within the clinical environment. Governance structures were established supporting data security, auditability, and model oversight. The platform is designed to support prospective deployment and evaluation within routine perioperative care.

Conclusions:

AIRO represents a conceptual and implementation-focused approach to integrating explainable machine learning into perioperative clinical care. By combining standardized modelling, EHR-integrated deployment, and a governance-driven framework, the platform is designed to support clinically meaningful risk assessment and decision support across the care continuum. Further work is required to evaluate its impact on clinical practice, clinician adoption, and patient outcomes.