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.

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.

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.

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.

The effectiveness of family-centered transition processes from hospital settings to home: a review of the literature.

BACKGROUND AND OBJECTIVES: The quality of care transitions is of growing concern because of a high incidence of postdischarge adverse events, poor communication with patients, and inadequate information transfer between providers. The objective of this study was to conduct a targeted literature review of studies examining the effectiveness of family-centered transition processes from hospital- and emergency department (ED)-to-home for improving patient health outcomes and health care utilization. METHODS: We conducted an electronic search (2001-2012) of PubMed, CINAHL, Cochrane, PsycInfo, Embase, and Web of Science databases. Included were experimental studies of hospital and ED-to-home transition interventions in pediatric and adult populations meeting the following inclusion criteria: studies evaluating hospital or ED-to-home transition interventions, study interventions involving patients/families, studies measuring outcomes≤30 days after discharge, and US studies. Transition processes, principal outcome measures (patient health outcomes and health care utilization), and assessment time-frames were extracted for each study. RESULTS: The search yielded 3458 articles, and 16 clinical trials met final inclusion criteria. Four studies evaluated pediatric ED-to-home transitions and indicated family-tailored discharge education was associated with better patient health outcomes. Remaining trials evaluating adult hospital-to-home transitions indicated a transition needs assessment or provision of an individualized transition record was associated with better patient health outcomes and reductions in health care utilization. The effectiveness of postdischarge telephone follow-up and/or home visits on health care utilization showed mixed results. CONCLUSIONS: Patient-tailored discharge education is associated with improved patient health outcomes in pediatric ED patients. Effective transition processes identified in the adult literature may inform future quality improvement research regarding pediatric hospital-to-home transitions.

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.

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.

Nutrition Interventions to Optimize Pediatric Wound Healing: An Evidence-Based Clinical Pathway.

Background and Objective: There is no accepted nutrition approach for wound healing in children. Our aims were to determine optimal nutrition support for pediatric wound healing. Methods: We applied local methods to create evidence- and consensus-based recommendations, supported by implementation tools, including algorithms, clinical decision supports, and measures. We applied these recommendations to the care of 49 patients from December 5, 2011, to December 5, 2012. Results: Six articles were found that addressed our clinical questions, and we formulated 5 clinical recommendations. Evidence supported evaluating patients for vitamin C, zinc, and protein deficiency. Of the patients where laboratory values were checked, 9 patients were zinc deficient (33%) and 12 patients were vitamin C deficient (48%). Discussion and Practical Application: The implementation of our recommendations has led to increased identification of micronutrient deficiencies and closer monitoring of nutrition status and intake. Online clinical decision supports can accelerate the adoption of clinical recommendations and reduce provider practice variation.

Improving care for pediatric diabetic ketoacidosis.

OBJECTIVE: We sought to create and implement recommendations from an evidence-based pathway for hospital management of pediatric diabetic ketoacidosis (DKA) and to sustain improvement. We hypothesized that development and utilization of standard work for inpatient care of DKA would lead to reduction in hypokalemia and improvement in outcome measures. METHODS: Development involved systematic review of published literature by a multidisciplinary team. Implementation included multidisciplinary feedback, hospital-wide education, daily team huddles, and development of computer decision support and electronic order sets. RESULTS: Pathway-based order sets forced clinical pathway adherence; yet, variations in care persisted, requiring ongoing iterative review and pathway tool adjustment. Quality improvement measures have identified barriers and informed subsequent adjustments to interventions. We compared 281 patients treated postimplementation with 172 treated preimplementation. Our most notable findings included the following: (1) monitoring of serum potassium concentrations identified unanticipated hypokalemia episodes, not recognized before standard work implementation, and earlier addition of potassium to fluids resulted in a notable reduction in hypokalemia; (2) improvements in insulin infusion management were associated with reduced duration of ICU stay; and (3) with overall improved DKA management and education, cerebral edema occurrence and bicarbonate use were reduced. We continue to convene quarterly meetings, review cases, and process ongoing issues with system-based elements of implementing the recommendations. CONCLUSIONS: Our multidisciplinary development and implementation of an evidence-based pathway for DKA have led to overall improvements in care. We continue to monitor quality improvement metric measures to sustain clinical gains while continuing to identify iterative improvement opportunities.

Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data.

BACKGROUND: Children with lifelong chronic conditions (LLCC) are costly, of low prevalence, and a high proportion of patients at children's hospitals. Few methods identify these patients. OBJECTIVES: We sought to identify children with LLCC in hospital discharge data for care coordination by using clinical risk groups (CRGs), to evaluate the accuracy of this methodology compared with a chart review and to investigate accuracy according to condition groups. METHODS: CRG software identified LLCC children who receive care at a primary care clinic, Odessa Brown Children's Clinic, by using Seattle Children's Hospital discharge data. RESULTS: There were 5356 active Odessa Brown Children's Clinic patients with at least 1 clinic encounter in 2006-2007. Six hundred two (11.2%) patients were admitted to Seattle Children's Hospital, and 1703 (31.8%) were seen only in the emergency department over 7 years (2001-2007). One hundred sixty-four (7%) were identified to have a LLCC. In a blind review of 200 (33.2%) children with inpatient encounters, the specificity of the CRG designation to LLCC was 95.0% (95% confidence interval [CI], 90.0%-98.0%), sensitivity 76.3% (95% CI, 63.4%-86.4%). Mental health conditions formed the largest group that was chart-review positive and CRG negative (7 of 14). Children hospitalized before 13 months of age were the second largest group (3 of 14). Clinical review placed the 164 patients in these condition groups: sickle cell disease, 43 (26.2%), neurological, 37 (22.6%), mental health, 22 (13.4%), malignancies, 4 (2.4%), other 52 (31.7%), and no chronic condition 6 (3.7%). CONCLUSION: This study demonstrates a unique way to identify children with LLCC for care coordination by using hospital administrative data.