title: Pediatric medical complexity algorithm: a new method to stratify children by medical complexity.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Pediatrics, University of Washington/Seattle Children's Hospital, Seattle, Washington;Seattle Children's Research Institute, Seattle, Washington; tamara.simon@seattlechildrens.org.
authors.forename: Tamara D
authors.initials: TD
authors.lastname: Simon
authors.affiliation: Research and Data Analysis Division, Washington Department of Social and Health Services, Olympia, Washington; and.
authors.forename: Mary Lawrence
authors.initials: ML
authors.lastname: Cawthon
authors.affiliation: Seattle Children's Research Institute, Seattle, Washington;
authors.forename: Susan
authors.initials: S
authors.lastname: Stanford
authors.affiliation: Seattle Children's Research Institute, Seattle, Washington;
authors.forename: Jean
authors.initials: J
authors.lastname: Popalisky
authors.affiliation: Research and Data Analysis Division, Washington Department of Social and Health Services, Olympia, Washington; and.
authors.forename: Dorothy
authors.initials: D
authors.lastname: Lyons
authors.affiliation: Research and Data Analysis Division, Washington Department of Social and Health Services, Olympia, Washington; and.
authors.forename: Peter
authors.initials: P
authors.lastname: Woodcox
authors.affiliation: Department of Pediatrics, University of Washington/Seattle Children's Hospital, Seattle, Washington;
authors.forename: Margaret
authors.initials: M
authors.lastname: Hood
authors.affiliation: Department of Pediatrics, Children's Hospital Los Angeles, Keck School of Medicine at the University of Southern California, Los Angeles, California.
authors.forename: Alex Y
authors.initials: AY
authors.lastname: Chen
authors.affiliation: Department of Pediatrics, University of Washington/Seattle Children's Hospital, Seattle, Washington;Seattle Children's Research Institute, Seattle, Washington;
authors.forename: Rita
authors.initials: R
authors.lastname: Mangione-Smith
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D002908:Chronic Disease / Q000145:classification*; D005260:Female; D054625:Healthcare Disparities / Q000145:classification / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007345:Insurance Claim Review; D038801:International Classification of Diseases; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D012189:Retrospective Studies; D062606:Tertiary Care Centers / Q000706:statistics & numerical data; D014481:United States; D014861:Washington
keywords: administrative data; children; claims data; medical complexity; sensitivity; specificity; stratification; validation