ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
A Mixed Methods Task Analysis of the Implementation and Validation of EHR-Based Clinical Quality Measures.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
4 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
- Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality.JCO clinical cancer informatics · 2022Article
- Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers.JMIR medical informatics · 2021Article
- Moving from Quality Measurement to Quality Improvement: Applying Meaningful Use Lessons to the Quality Payment Program.Perspectives in health information managemen · 2019Article
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
No grant is acknowledged in the PubMed record.
Abstract
Clinical quality measures (CQMs) are important tools for the assessment and improvement of health care quality. Federal requirements initially set forth in the American Recovery and Reinvestment Act, and advanced in subsequent stages of the requirements, codified electronic health record (EHR)-based CQM reporting, and have made automated CQM implementation a priority amongst the clinical and informatics communities. Nevertheless, the processes surrounding CQM implementation and validation remain complex, time-consuming, and largely undefined. We collected issue-tracking data during the course of an agile and rigorous collaborative project to build an analytics platform for the Knight Cardiovascular Institute at OHSU, with nine heart failure CQMs defined by the American College of Cardiology (ACC) as an exemplar. Using a mixed methods approach we provide an overview of our CQM implementation and validation process, identify major roadblocks and bottlenecks, and make recommendations for other professionals working in the area of health care quality assessment and improvement.
Indexed as
Identifiers
28269920PMC5333295What OpenQuestion holds
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.