Pediatric Medical Complexity Algorithm: A New Method to Stratify Children by Medical Complexity

Abstract

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.

Using State Administrative Data to Identify Social Complexity Risk Factors for Children

“…Child demographics were based on enrollment data obtained from each of the social and health information systems in which the child was served. We determined medical complexity using the more conservative version of the Pediatric Medical Complexity Algorithm (PMCA), 38 which uses diagnoses in administrative billing data to classify children into 3 categories: (1) no chronic illness (eg, febrile seizure), (2) noncomplex chronic illness (eg, epilepsy), or (3) complex chronic illness (eg, epilepsy with chronic respiratory insufficiency). For each child, PMCA was applied to a 3-year period We also adjusted for clustering 39 by family because 16% of the younger cohort and 28% of the older cohort had at least 1 sibling with the same biologic mother and father.…”

ASD Validity

“…Many forms of electronic health records (EHRs) have been outlined (Castillo et al 2015;McCoy et al 2015;Simon et al 2014). Gillberg (2010) proposed BEarly Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations^or ESSENCE, to include early appearing difficulties in general development, communication and language, social interrelatedness, motor coordination, attention, activity, behavior, mood, and/or sleep.…”

Development of the Children With Disabilities Algorithm

“…CWDA (1) includes diagnoses related to intellectual and emotional conditions with a high likelihood of functional impairment (eg, schizophrenia); (2) excludes chronic conditions with a low likelihood of long-term functional impairment (eg, allergic rhinitis); (3) includes life-threatening conditions with a long-term clinical trajectory (eg, end-stage renal disease) but excludes those with a short-term trajectory (eg, sepsis); and (4) identifies children irrespective of the number of organ systems that might be involved. [20][21][22] Of note, sensitivity of CWDA was 0.75 (95% CI 0.63-0.84) compared with parent report and 0.98 (95% CI 0.95-0.99) compared with physician assessment. We hypothesize that CWDA sensitivity relative to parent report may be lower than that for physician assessment because parents respond according to their own definitions of disability, rather than the WHO/UN concepts and definitions used for CWDA development.…”

“…19 Studies using other algorithms have provided important insight into aspects of pediatric care quality, such as access to palliative care, so we anticipate that an algorithm aimed at identifying CWD would similarly highlight the particular health care needs of CWD. [19][20][21][22][23][24] Thus, the main objectives of this study were to (1) create CWDA using pediatric disability experts to classify ICD-9-CM codes according to their likelihood of indicating CWD, and (2) triangulate CWDA with the parent perspective of disability and physician assessment of patient charts. The goal of CWDA is to identify a population of children with a high likelihood of having a disability based on their diagnoses found in claims data.…”