Medicaid and CHIP Children’s Healthcare Quality Measures: What States Use and What They Want

deLone, Sarah E.; Hess, Catherine

doi:10.1016/j.acap.2010.12.003

Cited by 12 publications

(9 citation statements)

References 3 publications

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“…Allowing such “wiggle room” is especially important when measuring care provided to publicly-insured populations, as they sometimes have sporadic patterns of care utilization and often experience insurance coverage gaps. 9,19-24 These publicly-insured families seek care more often when they can afford it and/or when they are insured. 25-27 Further, without strong evidence supporting strict timeframes, it is better to allow for some flexibility in measure specifications to reflect clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Are Pediatric Quality Care Measures Too Stringent?

Casciato

Angier

Milano

et al. 2012

The Journal of the American Board of Family Medicine

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Introduction We aimed (1) to demonstrate the application of national pediatric quality measures derived from claims-based data, for use with Electronic Medical Record (EMR) data, and (2) to determine the extent to which rates differ if specifications were modified to allow for flexibility in measuring receipt of care. Methods We reviewed EMR data for all patients up to 15 years with≥1 office visit to a safety net family medicine clinic in 2010 (n=1,544). We assessed rates of appropriate well-child visits (WCVs), immunizations, and body mass index (BMI) documentation, defined strictly by national guidelines versus by guidelines with clinically relevant modifications. Results Among children <3 years, 52.4% attended ≥6 WCVs by 15 months; 60.8% had ≥6 visits by 2 years. Less than 10% completed 10 vaccination series before their 2nd birthday; with modifications, 36% were up-to-date. Among children aged 3-15 years, 63% had a BMI percentile recorded; 91% had BMI recorded within 36 months of the measurement year. Discussion Applying relevant modifications to national quality measure definitions captured a substantial number of additional services. Strict adherence to measure definitions might miss the true quality of care provided, especially in populations who may have sporadic patterns of care utilization.

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Section: Discussionmentioning

confidence: 99%

Are Pediatric Quality Care Measures Too Stringent?

Casciato

Angier

Milano

et al. 2012

The Journal of the American Board of Family Medicine

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show abstract

“…The original specifications of the childhood preventive care measures (Table 1, numbers 6 and 7, [9][10][11][12] are also based on an enrolled population. To calculate these measures for 3-to 6-and 12-to 21-year-olds in EHR data, we suggest defining the denominator as children and youth who have $1 clinic visit in a given measurement year and also had $1 visit in the year prior, to identify "established" clinic patients.…”

Section: Specifying a Population Denominatormentioning

confidence: 99%

“…[8][9][10][11] Health insurance claims data include only services provided during a period of coverage under any given plan. Thus, the CHIPRA measures could compare the quality of care received by people continuously enrolled in one plan, or possibly compare longitudinal data from enrollees in different plans.…”

mentioning

confidence: 99%

Feasibility of Evaluating the CHIPRA Care Quality Measures in Electronic Health Record Data

et al. 2012

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The Children's Health Insurance Program Reauthorization Act of 2009 (CHIPRA) includes provisions for identifying standardized pediatric care quality measures. These 24 "CHIPRA measures" were designed to be evaluated by using claims data from health insurance plan populations. Such data have limited ability to evaluate population health, especially among uninsured people. The rapid expansion of data from electronic health records (EHRs) may help address this limitation by augmenting claims data in care quality assessments. We outline how to operationalize many of the CHIPRA measures for application in EHR data through a case study of a network of >40 outpatient community health centers in 2009-2010 with a single EHR. We assess the differences seen when applying the original claims-based versus adapted EHR-based specifications, using 2 CHIPRA measures (Chlamydia screening among sexually active female patients; BMI percentile documentation) as examples. Sixteen of the original CHIPRA measures could feasibly be evaluated in this dataset. Three main adaptations were necessary (specifying a visit-based population denominator, calculating some pregnancy-related factors by using EHR data, substituting for medication dispense data). Although it is feasible to adapt many of the CHIPRA measures for use in outpatient EHR data, information is gained and lost depending on how numerators and denominators are specified. We suggest first steps toward application of the CHIPRA measures in uninsured populations, and in EHR data. The results highlight the importance of considering the limitations of the original CHIPRA measures in care quality evaluations.

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“…The implementation of such efforts in practice has succeeded only in part. Apart from 2 HEDIS quality indicators there is no feasibility tested comprehensive set of quality indicators available to assess the care of children with developmental disorders and specifically with ADHD [8]. Studies demonstrating false diagnoses of ADHD underline that quality assurance measures make sense [9].…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of Treatment of Children and Adolescents with Developmental Disorders – A Feasibility Study Using the Example of Attention Deficit Hyperactivity Disorder

Ulusoy¹,

Borusiak

Hameister³

et al. 2017

Gesundheitswesen

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The question of a possible presence of attention deficit/hyperactivity disorder (ADHD) is raised with increasing frequency in pediatric practice. There are guidelines and expert recommendations for diagnostic approaches. But there are no instruments available to evaluate the structural, process and outcome quality. In this pilot study, a set of quality indicators on the treatment quality of ADHD was analyzed in terms of their feasibility in tertiary referral centers.A set of 39 quality indicators (QI) on ADHD developed in advance in a multistage procedure was assessed at 9 tertiary referral centers, with a focus on process verifiability and feasibility. QI values were calculated as ratios for individual centers as well as across centers, followed by an explorative analysis to assess feasibility under due consideration of possible influencing factors.QI assessment is possible but highly complex and expensive in practice. Calculated QI values showed a high degree of heterogeneity between facilities as well as between institutions, which was mainly due to a lack of standardization in the documentation of required data.Basically, it is possible to assess the quality of ADHD treatment via QIs. The approach described here in assessing QIs may be also applied to other types of developmental disorders.

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Medicaid and CHIP Children’s Healthcare Quality Measures: What States Use and What They Want

Cited by 12 publications

References 3 publications

Are Pediatric Quality Care Measures Too Stringent?

Are Pediatric Quality Care Measures Too Stringent?

Feasibility of Evaluating the CHIPRA Care Quality Measures in Electronic Health Record Data

Quality Assessment of Treatment of Children and Adolescents with Developmental Disorders – A Feasibility Study Using the Example of Attention Deficit Hyperactivity Disorder

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