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Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled

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Automated postoperative triage systems can identify patients needing intensive care, reducing costs and improving value of care. This system is reproducible and actionable for clinical decision support.

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Area of Science:

  • Healthcare Management
  • Medical Informatics
  • Surgical Outcomes

Background:

  • Overtriaging low-risk patients to ICUs leads to low value of care.
  • Undertriaging high-risk patients to general wards increases mortality and morbidity.
  • Automated systems can improve postoperative patient classification.

Purpose of the Study:

  • To test the reproducibility of an automated postoperative triage classification system.
  • To generate an actionable and explainable decision support system for patient triage.
  • To evaluate the impact of triage accuracy on patient outcomes and healthcare costs.

Main Methods:

  • A longitudinal cohort study at two university hospitals.
  • An explainable deep learning model used EHR data for triage.
  • Nearest neighbor algorithms identified risk-matched controls for outcome comparison.

Main Results:

  • Overtriage (5.1% of ICU admissions) resulted in higher costs and lower value of care.
  • Undertriage (12.0% of ward admissions) was linked to increased mortality, morbidity, and lower value of care.
  • The automated triage system demonstrated reproducibility across institutions.

Conclusions:

  • Automated postoperative triage systems are reproducible and can enhance clinical decision-making.
  • Accurate patient triage is crucial for optimizing value of care and patient outcomes.
  • Addressing both overtriaging and undertriaging is essential for improving surgical patient management.