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Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data

John M Neff1, Holly Clifton, Kathleen J Park

  • 1Center for Children with Special Needs, Seattle Children's Hospital, Seattle, Wash 98101, USA. john.neff@seattlechildrens.org

Academic Pediatrics
|November 16, 2010
PubMed

Insights

Identifying children with lifelong chronic conditions (LLCC) using clinical risk groups (CRGs) in hospital data is feasible. This method offers a unique approach for care coordination in pediatric populations.

Area of Science:

  • Pediatric Health Informatics
  • Chronic Disease Management
  • Healthcare Data Analytics

Background:

  • Children with lifelong chronic conditions (LLCC) represent a significant, yet hard-to-identify patient group.
  • Effective identification of these children is crucial for targeted care coordination and resource allocation in pediatric healthcare settings.

Purpose of the Study:

  • To identify children with LLCC using clinical risk groups (CRGs) within hospital discharge data.
  • To evaluate the accuracy of the CRG methodology against manual chart review.
  • To analyze the accuracy of CRG identification across different condition groups.

Main Methods:

  • Utilized CRG software to analyze Seattle Children's Hospital discharge data for patients of Odessa Brown Children's Clinic.
  • Conducted a blind chart review of a subset of patients identified by CRG software.
  • Compared CRG designations with chart review findings to determine specificity and sensitivity.

Main Results:

  • The CRG methodology demonstrated high specificity (95.0%) in identifying children with LLCC.
  • Sensitivity for CRG identification was 76.3%, with notable under-identification in mental health and early-onset conditions.
  • Sickle cell disease and neurological conditions were the most frequent LLCCs identified.

Conclusions:

  • Hospital administrative data, specifically CRGs, offers a viable method for identifying children with LLCC.
  • This approach facilitates proactive care coordination for vulnerable pediatric populations.
  • Further refinement of CRG algorithms may improve identification rates for specific condition categories.
Abstract

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