Implementing AI-Enabled Sepsis Alerts

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.

1. Bloch, E., Rotem, T., Cohen, J., Singer, P., & Aperstein, Y. (2019). Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction. Journal of Healthcare Engineering, 2019, 1-11. https://doi.org/10.1155/2019/5930379 DOI
2. Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021). Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature Communications, 12(1), 711. https://doi.org/10.1038/s41467-021-20910-4 DOI
3. Horng, S., Sontag, D. A., Halpern, Y., Jernite, Y., Shapiro, N. I., & Nathanson, L. A. (2017). Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE, 12(4), e0174708. https://doi.org/10.1371/journal.pone.0174708 DOI
4. Ibrahim, Z. M., Wu, H., Hamoud, A., Stappen, L., Dobson, R. J. B., & Agarossi, A. (2019). On classifying sepsis heterogeneity in the ICU: insight using machine learning. Journal of the American Medical Informatics Association, 27(3), 437-443. https://doi.org/10.1093/jamia/ocz211 DOI
5. Islam, K. R., Prithula, J., Kumar, J., Tan, T. L., Reaz, M. B. I., Sumon, M. S. I., & Chowdhury, M. E. H. (2023). Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review. Journal of Clinical Medicine, 12(17), 5658. https://doi.org/10.3390/jcm12175658 DOI
6. Joshi, M., Mecklai, K., Rozenblum, R., & Samal, L. (2022). Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study. JAMIA Open, 5(2), ooac022. https://doi.org/10.1093/jamiaopen/ooac022 DOI
7. Masino, A. J., Harris, M. C., Forsyth, D., Ostapenko, S., Srinivasan, L., Bonafide, C. P., Balamuth, F., Schmatz, M., & Grundmeier, R. W. (2019). Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS ONE, 14(2), e0212665. https://doi.org/10.1371/journal.pone.0212665 DOI
8. Shimabukuro, D. W., Barton, C. W., Feldman, M. D., Mataraso, S. J., & Das, R. (2017). Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respiratory Research, 4(1), e000234. https://doi.org/10.1136/bmjresp-2017-000234 DOI
9. Vegt, A. H. V. D., Scott, I. A., Dermawan, K., Schnetler, R. J., Kalke, V. R., & Lane, P. J. (2023). Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. Journal of the American Medical Informatics Association, 30(7), 1349-1361. https://doi.org/10.1093/jamia/ocad075 DOI
10. Yan, M. Y., Gustad, L. T., & Nytrø, Ø. (2021). Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review. Journal of the American Medical Informatics Association, 29(3), 559-575. https://doi.org/10.1093/jamia/ocab236 DOI