Quality Informatics

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).

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.

A review of both structural and pragmatic considerations in e-measurement in the context of the Children’s Health Insurance Program Reauthorization Act of 2009 (CHIPRA)-mandated Pediatric Quality Measurement Program.

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.

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).

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.