Literature DB >> 24800161

Measuring prevention more broadly: an empirical assessment of CHIPRA core measures.

Nir Menachemi1, Justin Blackburn1, David J Becker1, Michael A Morrisey1, Bisakha Sen1, Cathy Caldwell2.   

Abstract

OBJECTIVE: To assess limitations of using select Children's Health Insurance Program Reauthorization Act (CHIPRA) core claims-based measures in capturing the preventive services that may occur in the clinical setting.
METHODS: We use claims data from ALL Kids, the Alabama Children's Health Insurance Program (CHIP), to calculate 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. Additionally, we examine the extent to which these two claims-based approaches to measuring quality differ in assessments of disparities in quality of care across subgroups of children.
RESULTS: Statistically significant differences in rates were identified when comparing the two definitions for calculating each quality measure. Measure differences ranged from a 1.9 percentage point change for measure #13 (receiving preventive dental services) to a 25.5 percentage point change for measure #12 (adolescent well-care visit). We were able to identify subgroups based upon family income, rural location, and chronic disease status with differences in quality within the core measures. However, some identified disparities were sensitive to the approach used to calculate the quality measure.
CONCLUSIONS: 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. Additional guidance and research may be necessary before reporting of the measures becomes mandatory.

Entities:  

Keywords:  Administrative Data Uses; Child and Adolescent Health; Children’s Health Insurance Program (CHIP, SCHIP); Quality of Care / Patient Safety (Measurement)

Mesh:

Year:  2013        PMID: 24800161      PMCID: PMC4001808          DOI: 10.5600/mmrr.003.03.a04

Source DB:  PubMed          Journal:  Medicare Medicaid Res Rev        ISSN: 2159-0354


  17 in total

1.  Impact of managed care on physicians' decisions to manipulate reimbursement rules: an explanatory model.

Authors:  Jonathan VanGeest; Saul Weiner; Timothy Johnson; Deborah Cummins
Journal:  J Health Serv Res Policy       Date:  2007-07

2.  State Medicaid and Children's Health Insurance Program's perspective on CHIPRA core measures.

Authors:  Mary Greene-McIntyre; Cathy Caldwell
Journal:  Acad Pediatr       Date:  2011 May-Jun       Impact factor: 3.107

3.  The effects of Medicaid and CHIP policy changes on receipt of preventive care among children.

Authors:  Genevieve M Kenney; James Marton; Ariel E Klein; Jennifer E Pelletier; Jeffery Talbert
Journal:  Health Serv Res       Date:  2010-11-05       Impact factor: 3.402

4.  Identifying children's health care quality measures for Medicaid and CHIP: an evidence-informed, publicly transparent expert process.

Authors:  Rita Mangione-Smith; Jeffrey Schiff; Denise Dougherty
Journal:  Acad Pediatr       Date:  2011 May-Jun       Impact factor: 3.107

5.  The Children's Health Insurance Program Reauthorization Act quality measures initiatives: moving forward to improve measurement, care, and child and adolescent outcomes.

Authors:  Denise Dougherty; Jeffrey Schiff; Rita Mangione-Smith
Journal:  Acad Pediatr       Date:  2011 May-Jun       Impact factor: 3.107

6.  Measuring and reporting quality of health care for children: CHIPRA and beyond.

Authors:  Gerry Fairbrother; Lisa A Simpson
Journal:  Acad Pediatr       Date:  2011 May-Jun       Impact factor: 3.107

7.  Physician reimbursement levels and adherence to American Academy of Pediatrics well-visit and immunization recommendations.

Authors:  Thomas K McInerny; William L Cull; Beth K Yudkowsky
Journal:  Pediatrics       Date:  2005-04       Impact factor: 7.124

8.  Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality?

Authors:  L I Iezzoni; S M Foley; J Daley; J Hughes; E S Fisher; T Heeren
Journal:  JAMA       Date:  1992 Apr 22-29       Impact factor: 56.272

Review 9.  Assessing quality using administrative data.

Authors:  L I Iezzoni
Journal:  Ann Intern Med       Date:  1997-10-15       Impact factor: 25.391

10.  Discordance of databases designed for claims payment versus clinical information systems. Implications for outcomes research.

Authors:  J G Jollis; M Ancukiewicz; E R DeLong; D B Pryor; L H Muhlbaier; D B Mark
Journal:  Ann Intern Med       Date:  1993-10-15       Impact factor: 25.391

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.