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Summary

Monitoring machine-learning-based clinical decision support (ML-CDS) requires a broader approach beyond algorithm accuracy. This study introduces a dashboard to bridge the gap between ML-CDS outputs and patient outcomes, ensuring effective integration into care systems.

Area of Science:

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  • Current monitoring of ML-CDS focuses primarily on algorithm accuracy.
  • Effective patient care necessitates monitoring how ML-CDS outputs translate into actionable interventions by care teams.
  • There is a need to expand monitoring beyond model performance to encompass the entire care system.