ArticleEGEMS (Washington, DC)2018
Understanding the Impact of Variations in Measurement Period Reporting for Electronic Clinical Quality Measures.
Article in EGEMS (Washington, DC), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness.Journal of the American Medical Informatics Association : JAMIA · 2023Trial
- Improving screening, brief intervention and referral to treatment for unhealthy alcohol use in diverse, low-resourced primary care clinics.BMC health services research · 2024Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives.Applied clinical informatics · 2022Article
- Establishing Trust in Pharmaceutical Data with an Independent Verification and Validation Methodology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo understand the impact of varying measurement period on the calculation of electronic Clinical Quality Measures (eCQMs).
backgroundeCQMs have increased in importance in value-based programs, but accurate and timely measurement has been slow. This has required flexibility in key measure characteristics, including measurement period, the timeframe the measurement covers. The effects of variable measurement periods on accuracy and variability are not clear.
methods209 practices were asked to extract and submit four eCQMs from their Electronic Health Records on a quarterly basis using a 12-month measurement period. Quarterly submissions were collected via REDCap. The measurement periods of the survey data were categorized into non-standard (3, 6, 9 months and other) and standard periods (12 months). For comparison, patient-level data from three clinics were collected and calculated in an eCQM registry to measure the impact of varying measurement periods. We assessed the central tendency, shape of the distributions, and variability across the four measures. Analysis of variance (ANOVA) was conducted to analyze the differences among standard and non-standard measurement period means, and variation among these groups.
resultsOf 209 practices, 191 (91 percent) submitted data over three quarters. Of the 546 total submissions, 173 had non-standard measurement periods. Differences between measures with standard versus non-standard periods ranged from -3.3 percent to 14.2 percent between clinics (p < .05 for 3 of 4), using the patient-level data yielded deltas of -1.6 percent to 0.6 percent when comparing non-standard and standard periods.
conclusionVariations in measurement periods were associated with variation in performance between clinics for 3 of the 4 eCQMs, but did not have significant differences when calculated within clinics. Variations from standard measurement periods may reflect poor data quality and accuracy.
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Registered trials
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