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Area of Science:

  • Pediatric Health Informatics
  • Clinical Predictive Modeling
  • Health Services Research

Background:

  • Identifying children with complex health needs (CCHN) and intersecting medical/social requirements poses a significant challenge.
  • Existing clinical decision support (CDS) tools for CCHN identification have limitations.

Purpose of the Study:

  • To develop and evaluate an electronic health record (EHR)-based clinical predictive model for identifying high-risk CCHN.
  • To compare the performance of this novel EHR-based model against other CDS tools for CCHN identification.

Main Methods:

  • A retrospective cohort study utilized EHR data from children aged 0-20 years.
  • Machine learning methods generated a predictive model for 6-month hospitalization probability.
  • Model performance was assessed using sensitivity, positive predictive value, AUC, and AU-PRC, and compared to the Pediatric Medical Complexity Algorithm (PMCA) and prior hospital utilization.

Main Results:

  • The EHR-based model demonstrated an AUC of 0.79 and AU-PRC of 0.13.
  • While PMCA identified more children as high-risk (17.3%) with higher sensitivity (52.4%), the model-based CDS rule achieved a higher positive predictive value (19%) than PMCA (1.9%) and previous utilization (15%).

Conclusions:

  • A novel EHR-based predictive model was successfully developed and validated.
  • This model serves as a valuable population-level CDS tool for identifying CCHN at high risk for future hospitalizations.