Measuring Prevention More Broadly: An Empirical Assessment of CHIPRA Core Measures

Using claims data from the Alabama Children’s Health Insurance Program, calculated each of four quality measures under two alternative definitions: (1) the formal claims-based guidelines outlined in the CMS Technical Specifications, and (2) a broader definition of appropriate claims for identifying preventive service use. Concludes: Differences in CHIP design and structure, across states and over time, may limit the usefulness of select claims-based core measures for detecting disparities accurately (Medicare and Medicaid Research Review).

Menachemi 2013 (Link) | PubMed 24800161 | Author Search

Variation in Outcomes of Quality Measurement by Data Source

Using quality measures (BMI calculation rate and immunization rates) defined by the Children’s Health Insurance Program Reauthorization Act, researchers using data from the OCHIN network of community health centers determined whether successful calculation of these measures could be enhanced by the use of EHR data in addition to the traditional claims data. BMI calculation rates went up significantly when EHR data was used, and immunization rates went up when EHR data was combined with claims data.

Angier 2014 (Pediatrics) | PubMed 24864178 | Author Search

Self-Reported Quality of Life of Young Children With Conditions From Early Infancy: A Systematic Review

A systematic review of 37 studies of self-reported quality-of-life (QoL) scales in children aged < 12 years with congenital health conditions. Regardless of the condition or the instrument used, children often reported QoL similar to the reference population, except for lower scores in the physical functioning/health domain. Disparity between parental and child assessments of QoL suggested that both child and parent perspectives are essential to understanding the child’s QoL. [su_cite_pediatrics url_fragment = '134/4/e1129' author = 'Jardine' year = '2014'] [su_pubmed pmid = '25246620' author = 'Jardine' inits = 'J']

Relationship Between the Functional Status Scale and the Pediatric Overall Performance Category and Pediatric Cerebral Performance Category Scales

Evaluated the relationship between the Functional Status Scale (FSS) and the Pediatric Overall Performance Category and Pediatric Cerebral Performance Category (POPC/PCPC) in a PICU setting. Authors investigated the association between the baseline and PICU discharge POPC/PCPC scores and the baseline and PICU discharge FSS scores, the dispersion of FSS scores within each of the POPC/PCPC ratings, and the relationship between the FSS neurologic components (FSS-CNS) and the PCPC.

Pollack 2014 (JAMA Pediatrics) | PubMed 24862461 | Author Search

Recognizing Differences in Hospital Quality Performance for Pediatric Inpatient Care

An examination of hospital quality measures as executed by children’s hospitals. Few hospitals had sufficient data to detect when care is 20% worse than average over a 3-year period for condition-specific measures (sickle cell disease, appendectomy, cerebrospinal fluid shunt surgery, gastroenteritis, heart surgery, and seizure).

Berry 2015 (Pediatrics) | PubMed 26169435 | Author Search

Development of an Electronic Pediatric All-Cause Harm Measurement Tool Using a Modified Delphi Method

Stockwell DC, Bisarya H, Classen DC, Kirkendall ES, Lachman PI, Matlow AG, Tham E, Hyman D, Lehman SM, Searles E, Muething SE, Sharek PJ

J Patient Saf 2014 Aug;

PMID: 25162206

OBJECTIVES: To have impact on reducing harm in pediatric inpatients, an efficient and reliable process for harm detection is needed. This work describes the first step toward the development of a pediatric all-cause harm measurement tool by recognized experts in the field.

METHODS: An international group of leaders in pediatric patient safety and informatics were charged with developing a comprehensive pediatric inpatient all-cause harm measurement tool using a modified Delphi technique. The process was conducted in 5 distinct steps: (1) literature review of triggers (elements from a medical record that assist in identifying patient harm) for inclusion; (2) translation of triggers to likely associated harm, improving the ability for expert prioritization; (3) 2 applications of a modified Delphi selection approach with consensus criteria using severity and frequency of harm as well as detectability of the associated trigger as criteria to rate each trigger and associated harm; (4) developing specific trigger logic and relevant values when applicable; and (5) final vetting of the entire trigger list for pilot testing.

RESULTS: Literature and expert panel review identified 108 triggers and associated harms suitable for consideration (steps 1 and 2). This list was pared to 64 triggers and their associated harms after the first of the 2 independent expert reviews. The second independent expert review led to further refinement of the trigger package, resulting in 46 items for inclusion (step 3). Adding in specific trigger logic expanded the list. Final review and voting resulted in a list of 51 triggers (steps 4 and 5).

CONCLUSIONS: Application of a modified Delphi method on an expert-constructed list of 108 triggers, focusing on severity and frequency of harms as well as detectability of triggers in an electronic medical record, resulted in a final list of 51 pediatric triggers. Pilot testing this list of pediatric triggers to identify all-cause harm for pediatric inpatients is the next step to establish the appropriateness of each trigger for inclusion in a global pediatric safety measurement tool.