CLINICAL DECISION SUPPORT SYSTEMS  IN REAL-WORLD HEALTHCARE: CHALLENGES  OF STRUCTURED MODELS AND THE CASE  FOR HYBRID APPROACHES

Nadiia Babkova; Anna Pospekhova

doi:10.36074/logos-31.10.2025.020

Information technologies and systems

October 31, 2025; Paris, France: IX International Scientific and Practical Conference «DÉBATS SCIENTIFIQUES ET ORIENTATIONS PROSPECTIVES DU DÉVELOPPEMENT SCIENTIFIQUE»

CLINICAL DECISION SUPPORT SYSTEMS IN REAL-WORLD HEALTHCARE: CHALLENGES OF STRUCTURED MODELS AND THE CASE FOR HYBRID APPROACHES

Nadiia Babkova⁺⁻
Anna Pospekhova⁺⁻

DOI: https://doi.org/10.36074/logos-31.10.2025.020
Published: 31.10.2025

Abstract

Clinical decision support systems (CDSS) play an important role in the digital transformation of healthcare. They provide personalized prompts, recommendations, and alerts using patient data and specific clinical knowledge. Reviews show that CDSS can improve adherence to protocols, speed up interventions, and enhance patient safety. However, their success relies on how well they integrate into workflows, the validity of the knowledge they use, and the design of interactions between humans and AI

References

Sutton, R. T., Pincock, D., Baumgart, D. C., et al. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17.
Sadeghi, Z., et al. (2024). A review of explainable AI in healthcare. Computers in Industry, 157, 104026.
Andersen, E. S., Møller, T., & Thomsen, T. W. (2024). Monitoring performance of clinical artificial intelligence in healthcare: A review. JBI Evidence Synthesis, 22(12), 2877–2895.
WHO. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.
Kore, A., et al. (2024). Empirical data drift detection experiments on real-world medical datasets. Nature Communications, 15, 398.
Vasey, B., Nagendran, M., Campbell, B., et al. (2022). DECIDE-AI: Reporting guideline for early-stage clinical evaluation of AI decision support. Nature Medicine, 28(5), 924–933.
Wong, A., Otles, E., Donnelly, J. P., et al. (2021). External validation of the Epic sepsis model. JAMA Internal Medicine, 181(8), 1065–1070.
Lyons, P. G., Hofford, M. R., Yu, S. C., et al. (2023). Variability in performance of a proprietary sepsis prediction model across hospitals. JAMA Internal Medicine, 183(6), 611–612.
Collins, G. S., Dhiman, P., Ma, J., et al. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models. BMJ, 385, e078378.

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How to Cite

Babkova, N., & Pospekhova, A. (2025). CLINICAL DECISION SUPPORT SYSTEMS IN REAL-WORLD HEALTHCARE: CHALLENGES OF STRUCTURED MODELS AND THE CASE FOR HYBRID APPROACHES. Collection of Scientific Papers «ΛΌГOΣ», (October 31, 2025; Paris, France), 114–118. https://doi.org/10.36074/logos-31.10.2025.020

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[1] Sutton, R. T., Pincock, D., Baumgart, D. C., et al. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17.

[2] Sadeghi, Z., et al. (2024). A review of explainable AI in healthcare. Computers in Industry, 157, 104026.

[3] Andersen, E. S., Møller, T., & Thomsen, T. W. (2024). Monitoring performance of clinical artificial intelligence in healthcare: A review. JBI Evidence Synthesis, 22(12), 2877–2895.

[4] WHO. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.

[5] Kore, A., et al. (2024). Empirical data drift detection experiments on real-world medical datasets. Nature Communications, 15, 398.

[6] Vasey, B., Nagendran, M., Campbell, B., et al. (2022). DECIDE-AI: Reporting guideline for early-stage clinical evaluation of AI decision support. Nature Medicine, 28(5), 924–933.

[7] Wong, A., Otles, E., Donnelly, J. P., et al. (2021). External validation of the Epic sepsis model. JAMA Internal Medicine, 181(8), 1065–1070.

[8] Lyons, P. G., Hofford, M. R., Yu, S. C., et al. (2023). Variability in performance of a proprietary sepsis prediction model across hospitals. JAMA Internal Medicine, 183(6), 611–612.

[9] Collins, G. S., Dhiman, P., Ma, J., et al. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models. BMJ, 385, e078378.