Data Analytics of Electronic Medical Record to Study Racial Diversities in Cardiovascular Diagnosis and Treatment.

Maryam Panahiazar, Yorick Chern, Ramon Riojas, Omar S Latif, Dexter Hadley, Ramin E Beygui

Abstract read

In one paragraph

Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Maryam PanahiazarDepartment of Surgery, Division of Cardiothoracic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, USA.

Yorick ChernDepartment of Bioengineering, University of California Berkeley, Berkeley, CA, USA.

Ramon RiojasDepartment of Surgery, Division of Cardiothoracic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, USA.

Omar S LatifDepartment of Internal Medicine, Mayo Clinic, Rochester, MN, USA.

Dexter HadleyDepartment of Clinical Sciences, College of Medicine, University of Central Florida, Orlando, FL, USA.

Ramin E BeyguiDepartment of Surgery, Division of Cardiothoracic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, USA.

Funding

Crowd-Assisted Deep Learning (CrADLe) Digital Curation to Translate Big Data into Precision MedicineU01LM012675 · NLM · UNIVERSITY OF CENTRAL FLORIDA · PI HADLEY, DEXTER D · 2017 to 2020

$2.1M

NLM NIH HHS U01 LM012675

6 · The paper itself

Abstract

The study of precision medicine that measures the effects of social, cultural, and environmental influences on health is essential to improve health outcomes. Race is a social concept used historically to divide, track, control populations, and reinforce social hierarchies. Beyond genetics, race is also a surrogate for other socioeconomic factors affecting patient outcomes. Our data analytics study aims to analyze the Electronic Medical Record (EMR) to study patients of different races in diagnosing and treating Coronary Artery Disease (CAD). We found no race discrepancies at the University of California San Francisco Medical Centers. This study opens several new hypotheses for further research in this crucial field.

Indexed as

Coronary Artery DiseaseElectronic Health RecordsData ScienceHumansPrecision MedicineSocioeconomic FactorsCardiovascular DiseaseData AnalyticsElectronic Medical RecordPrecision MedicineRacial Study

Identifiers

PMID35612140

DOI10.3233/SHTI220519

PMCPMC10342194

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

Read underepoch 390

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.