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Pediatric medical complexity algorithm: a new method to stratify children by medical complexity
Tamara D Simon1, Mary Lawrence Cawthon2, Susan Stanford3
1Department of Pediatrics, University of Washington/Seattle Children's Hospital, Seattle, Washington;Seattle Children's Research Institute, Seattle, Washington; tamara.simon@seattlechildrens.org.
Insights
A new Pediatric Medical Complexity Algorithm (PMCA) effectively identifies children with complex chronic diseases (C-CD) using ICD-9-CM codes. This algorithm shows good sensitivity and specificity for targeting care coordination resources to these children.
Area of Science:
- Pediatric healthcare research
- Health informatics
- Medical coding systems
Background:
- Accurate classification of pediatric chronic disease (CD) is essential for resource allocation.
- Existing methods for identifying children with varying medical complexity have limitations.
- International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes offer a potential basis for such classification.
Purpose of the Study:
- To develop and validate the Pediatric Medical Complexity Algorithm (PMCA) using ICD-9-CM codes.
- To classify children with chronic disease by their level of medical complexity.
- To assess the sensitivity and specificity of the developed PMCA.
Main Methods:
- A retrospective observational study involving 700 children insured by Washington State Medicaid.
- Modification of the existing Chronic Disability Payment System algorithm to create PMCA.
- Validation of PMCA against a gold standard population categorized into complex chronic disease (C-CD), noncomplex chronic disease (NC-CD), and no CD groups.
Main Results:
- PMCA demonstrated high sensitivity for C-CD (84-89%) and children without CD (80-96%) across different data sources.
- Sensitivity for NC-CD was lower (41-45%).
- Specificity ranged from 85% to 92% for all groups, indicating good overall classification accuracy.
Conclusions:
- The Pediatric Medical Complexity Algorithm (PMCA) accurately identifies children with complex chronic diseases (C-CD) who have accessed tertiary care.
- PMCA exhibits good to excellent sensitivity and specificity when applied to hospital discharge or Medicaid claims data.
- PMCA may serve as a valuable tool for targeting care coordination and other resources to children with C-CD.
Objectives:
The goal of this study was to develop an algorithm based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes for classifying children with chronic disease (CD) according to level of medical complexity and to assess the algorithm's sensitivity and specificity.
Methods:
A retrospective observational study was conducted among 700 children insured by Washington State Medicaid with ≥1 Seattle Children's Hospital emergency department and/or inpatient encounter in 2010. The gold standard population included 350 children with complex chronic disease (C-CD), 100 with noncomplex chronic disease (NC-CD), and 250 without CD. An existing ICD-9-CM-based algorithm called the Chronic Disability Payment System was modified to develop a new algorithm called the Pediatric Medical Complexity Algorithm (PMCA). The sensitivity and specificity of PMCA were assessed.
Results:
Using hospital discharge data, PMCA's sensitivity for correctly classifying children was 84% for C-CD, 41% for NC-CD, and 96% for those without CD. Using Medicaid claims data, PMCA's sensitivity was 89% for C-CD, 45% for NC-CD, and 80% for those without CD. Specificity was 90% to 92% in hospital discharge data and 85% to 91% in Medicaid claims data for all 3 groups.
Conclusions:
PMCA identified children with C-CD (who have accessed tertiary hospital care) with good sensitivity and good to excellent specificity when applied to hospital discharge or Medicaid claims data. PMCA may be useful for targeting resources such as care coordination to children with C-CD.
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