title: Stratification of children by medical complexity.
abstract: OBJECTIVE
  To stratify children using available software, Clinical Risk Groups (CRGs), in a tertiary children's hospital, Seattle Children's Hospital (SCH), and a state's Medicaid claims data, Washington State (WSM), into 3 condition groups: complex chronic disease (C-CD); noncomplex chronic disease (NC-CD), and nonchronic disease (NC).
  
  
  METHODS
  A panel of pediatricians developed consensus definitions for children with C-CD, NC-CD, and NC. Using electronic medical record review and expert consensus, a gold standard population of 700 children was identified and placed into 1 the 3 groups: 350 C-CD, 100 NC-CD, and 250 NC. CRGs v1.9 stratified the 700 children into the condition groups using 3 years of WSM and SCH encounter data (2008-2010). WSM data included encounters/claims for all sites of care. SCH data included only inpatient, emergency department, and day surgery claims.
  
  
  RESULTS
  A total of 678 of 700 children identified in SCH data were matched in WSM data. CRGs demonstrated good to excellent specificity in correctly classifying all 3 groups in SCH and WSM data; C-CD in SCH (94.3%) and in WSM (91.1%); NC-CD in SCH (88.2%) and in WSM (83.7%); and NC in SCH (84.9%) and in WSM (94.6%). There was good to excellent sensitivity for C-CD in SCH (75.4%) and in WSM (82.1%) and for NC in SCH (98.4%) and in WSM (81.1%). CRGs demonstrated poor sensitivity for NC-CD in SCH (31.0%) and WSM (58.0%). Reasons for poor sensitivity in NC-CD are explored.
  
  
  CONCLUSIONS
  CRGs can be used to stratify children receiving care at a tertiary care hospital according to complexity in both hospital and Medicaid administrative data. This method will enhance reporting of health-related outcome data.
authors.affiliation: Center for Child Health, Behavior, and Development, Seattle Children's Hospital, Seattle, Wash. Electronic address: john.neff@seattlechildrens.org.
authors.forename: John M
authors.initials: JM
authors.lastname: Neff
authors.affiliation: Department of Knowledge Management, Seattle Children's Hospital, Seattle, Wash.
authors.forename: Holly
authors.initials: H
authors.lastname: Clifton
authors.affiliation: Department of Clinical Effectiveness, Seattle Children's Hospital, Seattle, Wash.
authors.forename: Jean
authors.initials: J
authors.lastname: Popalisky
authors.affiliation: Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle Children's Hospital, Seattle, Wash.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
mesh_terms: D000208:Acute Disease / Q000145:classification*; D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease / Q000145:classification*; D057286:Electronic Health Records; D005260:Female; D006776:Hospitals, Pediatric; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016247:Information Storage and Retrieval; D008297:Male; D008484:Medicaid; D017063:Outcome Assessment, Health Care; D012720:Severity of Illness Index; D012984:Software; D062606:Tertiary Care Centers; D014481:United States; D014861:Washington
keywords: administrative billing data; children; chronic diseases; clinical risk group; stratification