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BACKGROUND AND OBJECTIVES: The Pediatric Medical Complexity Algorithm (PMCA) was developed to stratify children by level of medical complexity. We sought to refine PMCA and evaluate its performance based on the duration of eligibility and completeness of Medicaid data. METHODS: PMCA version 1.0 was applied to a cohort of 299 children insured by Washington State Medicaid with ≥1 Seattle Children's Hospital outpatient, emergency department, and/or inpatient encounter in 2012. Blinded assessment of the validation cohort's PMCA category was performed by using medical records. In-depth review of discrepant cases was performed and informed the development of PMCA version 2.0. The sensitivity and specificity of PMCA version 2.0 were assessed. RESULTS: Using Medicaid data, the sensitivity of PMCA version 2.0 was 74% for complex chronic disease (C-CD), 60% for noncomplex chronic disease (NC-CD), and 87% for those without chronic disease (CD). Specificity was 84% to 91% in Medicaid data for all 3 groups. Medicaid data were most complete for children that had primarily fee-for-service claims and were less complete for those with some managed care encounter data. PMCA version 2.0 performed optimally when children had a longer duration of coverage (25 to 36 months) with fee-for-service reimbursement, identifying children with C-CD with 85% sensitivity and 75% specificity, children with NC-CD with 55% sensitivity and 88% specificity, and children without CD with 100% sensitivity and 97% specificity. CONCLUSIONS: PMCA version 2.0 identifies children with C-CD with good sensitivity and very good specificity when applied to Medicaid data. Data quality is a critical consideration when using PMCA.
Simon et al. (Fri,) studied this question.