Family Resilience From the Perspective of Caregivers of Youth With Sickle Cell Disease.

Families coping with sickle cell disease (SCD) often face heightened psychosocial risk factors, and research in pediatric SCD has often focused more on this area than resiliency factors. The aim of this study was to gain a better understanding of family resiliency in SCD based on caregiver perspectives. A secondary qualitative analysis was conducted with data from a mixed-methods study of caregivers of youth with SCD (n=22). Qualitative analyses involved coding based on 2 resiliency frameworks, organizing coding categories into themes, and systematically reintegrating these themes into a conceptualization that reflected family resiliency. Themes aligned well with the resiliency frameworks and related to family belief systems and meaning-making around SCD (acceptance of SCD, positive attitude, religious faith), family organization and adaptation (flexibility, stability, social supports), and the importance of communication and problem-solving. Study findings emphasize the importance of assessing resilience in families of youth with SCD and suggest the potential clinical benefits of developing psychosocial interventions based on family strengths.

Psychosocial Screening in Sickle Cell Disease: Validation of the Psychosocial Assessment Tool.

OBJECTIVE: Families of youth with Sickle Cell Disease (SCD) can face psychosocial adversity including emotional distress, functional impairments, and sociodemographic risk factors. Systematic screening of psychosocial risk can identify families who may benefit from further assessment and evidence-based care. The Psychosocial Assessment Tool (PAT) is a brief caregiver-report screener based on the tri-level Pediatric Psychosocial Preventative Health Model (PPPHM). METHODS: Findings are presented from the baseline assessment of a longitudinal study validating a Sickle Cell version of the PAT 2.0. Primary caregivers of 136 youth with SCD receiving care through a multidisciplinary SCD clinic in a children's hospital completed the PAT and validation measures. A subset of 25 caregivers completed the PAT a second time within 3-5 weeks. RESULTS: Internal consistency for the total score was strong (α = .87), and for the subscales was moderate to strong (α = .74-.94), with the exception of the Family Structure (α = .38), Caregiver Beliefs (α = .48), and Stress Reactions (α = .56) subscales. Test-retest reliability was also strong (r = .86, p < .001). Moderate to strong correlations with all except two criteria measures provided validation for the total and subscale scores. Validation measures varied significantly across the three levels of the PPPHM. CONCLUSIONS: Results provide support for the reliability and validity of the PAT in SCD. Systematic screening with the PAT can help identify families of youth with SCD at risk for psychosocial problems and potentially help connect them to appropriate services.

Implementation of a learning healthcare system for sickle cell disease.

OBJECTIVE: Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. MATERIALS AND METHODS: Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. RESULTS: The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. CONCLUSIONS: Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.