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Area of Science:

  • Pediatric Emergency Medicine
  • Clinical Decision Support Systems
  • Laboratory Medicine

Background:

  • Laboratory tests are sometimes repeated in pediatric emergency departments (PEDs) shortly after community-based testing.
  • Unnecessary repeat testing causes child discomfort and increases healthcare costs.
  • Minimizing redundant tests is crucial without compromising diagnostic quality.

Purpose of the Study:

  • To develop a decision tree (DT) model for minimizing unnecessary repeat blood tests in PEDs.
  • To provide physicians with an interpretable tool for guiding repeat testing decisions.
  • To optimize healthcare resource utilization in pediatric emergency care.

Main Methods:

  • Utilized the minimal decision tree (MDT) algorithm for model development.
  • Included children aged 3 months to 18 years with prior community CBC, ELE, and CRP tests.
  • Evaluated repeat tests within 12 hours, defining justification by normal-to-abnormal transitions or ≥20% value changes.

Main Results:

  • The DT model demonstrated high accuracy in predicting the necessity of repeat CBC and ELE tests.
  • The model's performance surpassed that of logistic regression models.
  • The DT model was less accurate for predicting repeat C-reactive protein (CRP) tests.

Conclusions:

  • A data-driven DT model offers a practical and interpretable guide for clinicians.
  • The model aids in reducing unnecessary repeat laboratory testing in PEDs.
  • Implementation can enhance patient care and optimize resource allocation.