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Stratification of children by medical complexity
John M Neff1, Holly Clifton2, Jean Popalisky3
1Center for Child Health, Behavior, and Development, Seattle Children's Hospital, Seattle, Wash.
Insights
Clinical Risk Groups (CRGs) effectively stratify children into complex chronic disease (C-CD), noncomplex chronic disease (NC-CD), and nonchronic disease (NC) categories. This stratification method shows good to excellent accuracy in both hospital and Medicaid claims data.
Area of Science:
- Pediatric Health Services Research
- Health Informatics
- Disease Classification Systems
Background:
- Stratifying pediatric populations by disease complexity is crucial for resource allocation and outcome analysis.
- Existing methods may not adequately capture the nuances of chronic conditions in children.
- Tertiary children's hospitals and state Medicaid data offer unique insights into pediatric healthcare utilization.
Framework:
- Clinical Risk Groups (CRGs) v1.9 was employed as the software tool for patient stratification.
- CRGs aim to categorize patients into distinct condition groups: complex chronic disease (C-CD), noncomplex chronic disease (NC-CD), and nonchronic disease (NC).
- Consensus definitions developed by pediatricians guided the gold standard classification.
Implementation:
- A gold standard population of 700 children was established through expert consensus and electronic health record review.
- CRGs were applied to 3 years of Washington State Medicaid (WSM) and Seattle Children's Hospital (SCH) encounter data (2008-2010).
- Data linkage between SCH and WSM identified 678 of 700 children for analysis.
Implications:
- CRGs demonstrated strong specificity and good to excellent sensitivity for classifying C-CD and NC groups in both SCH and WSM data.
- The system showed poor sensitivity for the NC-CD group, indicating areas for refinement.
- CRGs provide a viable method for stratifying pediatric patients by complexity, enhancing health outcome reporting in administrative and hospital data.
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.
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