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Stratification of Children by Medical Complexity

Published:November 21, 2014DOI:https://doi.org/10.1016/j.acap.2014.10.007

      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.

      Keywords

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      References

      1. US Department of Health and Human Resource UG. Key features of the Affordable Care Act by year, children’s health, 2012. Available at: http://www.hhs.gov/healthcare/facts/timeline/timeline-text.html. Accessed October 9, 2014.

        • Neff J.M.
        • Sharp V.L.
        • Popalisky J.
        • et al.
        Using medical billing data to evaluate chronically ill children over time.
        J Ambul Care Manage. 2006; 29: 283-290
      2. Health Resources and Services Administration. The National Survey of Children With Special Health Care Needs chartbook, 2005–2006: prevalence of CSHCN. Available at: http://mchb.hrsa.gov/cshcn05/NF/1prevalence/individuals.htm. Accessed October 9, 2014.

        • Neff J.M.
        • Sharp V.L.
        • Muldoon J.
        • et al.
        Profile of medical charges for children by health status group and severity level in a Washington state health plan.
        Health Serv Res. 2004; 39: 73-89
        • Berry J.G.
        • Hall M.
        • Hall D.E.
        • et al.
        Inpatient growth and resource use in 28 children’s hospitals: a longitudinal, multi-institutional study.
        JAMA Pediatr. 2013; 167: 170-177
        • Simon T.D.
        • Berry J.
        • Feudtner C.
        • et al.
        Children with complex chronic conditions in inpatient hospital settings in the United States.
        Pediatrics. 2010; 126: 647-655
        • Burns K.H.
        • Casey P.H.
        • Lyle R.E.
        • et al.
        Increasing prevalence of medically complex children in US hospitals.
        Pediatrics. 2010; 126: 638-646
        • Cohen E.
        • Berry J.G.
        • Camacho X.
        • et al.
        Patterns and costs of health care use of children with medical complexity.
        Pediatrics. 2012; 130: e1463-e1470
        • Berry J.G.
        • Agrawal R.K.
        • Cohen E.
        • et al.
        The landscape of medical care for children with medical complexity.
        2013 (Available at:) (Accessed October 9, 2014)
        • Berry J.G.
        • Hall D.E.
        • Kuo D.Z.
        • et al.
        Hospital utilization and characteristics of patients experiencing recurrent readmissions within children’s hospitals.
        JAMA. 2011; 305: 682-690
        • Chang C.F.
        • Herrod H.G.
        • Steinberg S.S.
        Prevalence and costs of acute and chronic potentially avoidable pediatric hospitalizations in Tennessee.
        Tenn Med. 2009; 102: 35-39
        • Gay J.C.
        • Hain P.D.
        • Grantham J.A.
        • et al.
        Epidemiology of 15-day readmissions to a children’s hospital.
        Pediatrics. 2011; 127: e1505-e1512
        • Berry J.G.
        • Agrawal R.
        • Kuo D.Z.
        • et al.
        Characteristics of hospitalizations for patients who use a structured clinical care program for children with medical complexity.
        J Pediatr. 2011; 159: 284-290
        • Feudtner C.
        • Levin J.E.
        • Srivastava R.
        • et al.
        How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective, multicenter study.
        Pediatrics. 2009; 123: 286-293
        • O’Mahony L.
        • O’Mahony D.S.
        • Simon T.D.
        • et al.
        Medical complexity and pediatric emergency department and inpatient utilization.
        Pediatrics. 2013; 131: e559-e565
        • Neff J.M.
        • Sharp V.L.
        • Muldoon J.
        • et al.
        Identifying and classifying children with chronic conditions using administrative data with the clinical risk group classification system.
        Ambul Pediatr. 2002; 2: 71-79
        • Hughes J.S.
        • Averill R.F.
        • Eisenhandler J.
        • et al.
        Clinical risk groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management.
        Med Care. 2004; 42: 81-90
        • Chi D.L.
        • Momany E.T.
        • Neff J.
        • et al.
        Impact of chronic condition status and severity on dental utilization for Iowa Medicaid-enrolled children.
        Med Care. 2011; 49: 180-192
        • Neff J.
        • Clifton H.
        • Park K.J.
        • et al.
        Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data.
        Acad Pediatr. 2010; 10: 417-423
        • Simon T.
        • Cawthon M.
        • Sanford S.
        • et al.
        Pediatric medical complexity algorithm: a new method to stratify children by medical complexity.
        Pediatrics. 2014; 133: e1647-e1654
        • Antonelli R.C.
        • McAllister J.W.
        • Popp J.
        Making Care Coordination a Critical Component of the Pediatric Health Ssytem: A Multidisciplinary Framework.
        The Commonwealth Fund. 2009;
        • McDonald K.
        • Schultz E.
        • Albin L.
        • et al.
        Care Coordination Atlas, Version 3.
        Agency for Healthcare Research Quality, Rockville, Md2010 (AHRQ publication 11-0023-EF)
      3. R-Project: a language and environment for statistical computing [computer program]. R-Project, Vienna, Austria2009 (Available at:) (Accessed October 9, 2014)
        • Hollander M.
        • Wolfe D.A.
        Nonparametric Statistical Methods.
        John Wiley & Sons, New York1973