Evidence map›Paper›PMID 32721028›Full record

ArticleJAMA network open2020

Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.

Juell Homco, Hélène Carabin, Zsolt Nagykaldi, Tabitha Garwe, F Daniel Duffy, David Kendrick, Sydney Martinez, Yan Daniel Zhao, Julie Stoner

Abstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025
    Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Juell HomcoDepartment of Medical Informatics, School of Community Medicine, University of Oklahoma Health Sciences Center, Tulsa.
Hélène CarabinDepartment of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.
Zsolt NagykaldiDepartment of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City.
Tabitha GarweDepartment of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.
F Daniel DuffyDepartment of Medical Informatics, School of Community Medicine, University of Oklahoma Health Sciences Center, Tulsa.
David KendrickDepartment of Medical Informatics, School of Community Medicine, University of Oklahoma Health Sciences Center, Tulsa.
Sydney MartinezDepartment of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.
Yan Daniel ZhaoDepartment of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.
Julie StonerDepartment of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City.

Funding

Tracking and Evaluation CoreU54GM104938 · NIGMS · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · PI PAUL G SPICER · 2013 to 2026
$68.2M
NIGMS NIH HHS U54 GM104938
6 · The paper itself

Abstract

Importance: Cardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure). Objective: To compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis. Design, Setting, and Participants: This cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019. Main Outcomes and Measures: The main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models. Results: A total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score. Conclusions and Relevance: Ensuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.

Indexed as

Cardiovascular DiseasesRisk AssessmentAspirinBlood Pressure DeterminationCross-Sectional StudiesElectronic Health RecordsFemaleHumansMaleMiddle AgedPlatelet Aggregation InhibitorsPrimary Health CareQuality ImprovementQuality Indicators, Health CareReference StandardsReproducibility of ResultsAspirinPlatelet Aggregation Inhibitors

Identifiers

PMID32721028
PMCPMC7388024

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.