Implementing AI-Enabled Sepsis Alerts

Authors

  • Todd Hamilton Author

Keywords:

sepsis, clinical alerts, machine learning, implementation science, patient outcomes

Abstract

This implementation review examines implementation of artificial-intelligence-enabled sepsis alerts. The organizing question is how model validity, alert thresholds, workflow integration, clinician response, and patient outcomes should be evaluated together. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to equating alert generation with timely and beneficial clinical action. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in acute-care deterioration detection.

References

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Implementation Review

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Copyright (c) 2026 Precision Health and Clinical Innovation

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