Development and Validation of the Pediatric Medical Complexity Algorithm (PMCA) Version 2.0.

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.

Stratification of children by medical complexity.

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.

Pediatric medical complexity algorithm: a new method to stratify children by medical complexity.

OBJECTIVES: 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.

Care management for children with special needs: part I: the role of health plans.

This study documents screening methods and services provided by health plan case managers for high need children in a Washington State health plan. Enrollees were screened to identify 315 children who had or were at risk of developing a chronic condition and were high users of health services. From this group, 46 children/families could be contacted and needed case management. Services included assessment of physical/social needs, patient education, referral to community resources, and benefit utilization. These services were different from care coordination provided in primary care practices.

Care management for children with special needs: Part II: the role of primary care.

This study documents care management services in 2 pediatric clinics for children with or at risk for a chronic condition during 8 months in 2005. Patients were identified by the clinic staff from a list provided by the health plan of patients at risk for or with a chronic condition. Care management services were documented for 161 of 189 selected patients. Services included family support, condition management, medical equipment management, and referrals to specialty care. Pediatric clinical care management activities directly relate to patient care and are complementary to, not duplicative of, case management provided by health plan managers.

Using medical billing data to evaluate chronically ill children over time.

This study evaluates stability of chronic condition identification in children older than 4 years in a health plan billing data using Clinical Risk Groups. A total of 31,055 children were continuously enrolled for 4 years; 7.5% (2,334) identified with a chronic condition status in year 1, 2002, and another 15.4% (4,784) during subsequent years; 63.6% (19,759) were identified as "healthy" throughout. The most stable were those identified with a catastrophic health condition. The least stable were those with minor and moderate/dominant major chronic conditions. Overall, 73.1% (1,706) of the children with chronic conditions in year 1 improved in status, and 5.7% (133) progressed to more complex conditions.

Identifying and classifying children with chronic conditions using administrative data with the clinical risk group classification system.

OBJECTIVE: To identify and categorize children with chronic health conditions using administrative data. METHODS: The Clinical Risk Groups (CRGs) system is used to classify children, aged 0-18 years, in a mid-sized health plan into mutually exclusive categories and severity groups. Enrollees are categorized into 9 health status groups--healthy, significant acute, and 7 chronic conditions--and are then stratified by severity. Utilization is examined by category and severity level based on eligibility and claims files for calendar year 1999. Only children enrolled for at least 6 months (newborns at least 3 months) are included. RESULTS: This analysis of 34544 children classifies 85.2% as healthy, including 19.6% with no claims; 5.2% with a significant acute illness; 4.6% with a minor chronic condition; and 4.9% with a moderate to catastrophic chronic condition. The average number of unique medical care encounters per child increases by chronic condition category and by severity level. Compared to national prevalence norms for selected conditions, CRGs do well in identifying patients who have conditions that require interaction with the health care system. CONCLUSIONS: CRGs are a useful tool for identifying, classifying, and stratifying children with chronic health conditions. Enrollees can be grouped into categories for patient tracking, case management, and utilization.