ArticleEGEMS (Washington, DC)2015
Applied Use of Composite Quality Measures for EHR-enabled Practices.
Article in EGEMS (Washington, DC), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Bridging the gap between clinical practice and public health: Using EHR data to assess trends in the seasonality of blood-pressure control.Preventive medicine reports · 2017Article
- Assessing Impacts on Unplanned Hospitalisations of Care Quality and Access Using a Structural Equation Method: With a Case Study of Diabetes.International journal of environmental research and public health · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe Primary Care Information Project (PCIP) of the New York City Department of Health and Mental Hygiene has been assisting providers to implement health information technology such as electronic health records (EHRs) since its founding in 2005. Currently, all practices affiliated with PCIP are offered technical support services in order to improve the use of the EHR. We studied the performance of clinical practices on EHR-derived Composite Quality Measures (CQMs) over time. Because specific EHR functionalities are important to calculating the quality measures, we hypothesize that performance on each of the CQMs will differ according to the EHR functionalities, and that this can inform the process of developing targeted technical assistance for the practices.
methodsWe created four CQMs: (1) Screening, (2) Assessment, (3) Control-BP, and (4) Control-Other. Using data from 93 practices, we identified three tertiles of CQM performance (premier, average, and low tiers) for each measure. A scatterplot of CQMs in 2010 versus 2011 was used to examine the individual movement of practices by tier. A dependent t-test compared the change in mean CQMs, and a chi-square test examined the association between the score and performance tier changes.
resultsOver a one-year period, low tier practices demonstrated the highest gains, average tier practices had modest gains, and premier tier practices had gains in some measures, but losses in others. On the Screening CQM 70 percent of practices remained within the same tier, with 60 percent on Assessment, 52 percent on Control-BP, and 38 percent on Control-Other; the Control-Other group showed the greatest improvement. DISCUSSION: By considering EHR functionalities associated with each of the four CQMs, we suggest that technical assistance can be better targeted to low-tier performing practices. In addition, there is still the potential for improvement over time at practices more familiar with key functionalities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.