Evidence map›Paper›PMID 38756741›Full record

ArticleHealth affairs scholar2023

Evolving availability and standardization of patient attributes for matching.

Yu Deng, Lacey P Gleason, Adam Culbertson, Xiaotian Chen, Elmer V Bernstam, Theresa Cullen, Ramkiran Gouripeddi, Christopher Harle, David F Hesse, Jacob Kean and 13 more

Abstract read
In one paragraph

Article in Health affairs scholar, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

23 authors.

Yu DengCenter for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.
Lacey P GleasonCenter for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.ORCID https://orcid.org/0009-0009-3320-4599
Adam CulbertsonCenter for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.
Xiaotian ChenStatistical Innovation Group, Data and Statistical Sciences, AbbVie, Inc, North Chicago, IL 60064, United States.ORCID https://orcid.org/0000-0002-3094-8922
Elmer V BernstamSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.ORCID https://orcid.org/0000-0001-7643-791X
Theresa CullenPima County Health Department, Tucson, AZ 85714, United States.ORCID https://orcid.org/0000-0002-8730-5688
Ramkiran GouripeddiClinical and Translational Science Institute and Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States.ORCID https://orcid.org/0000-0002-4345-9669
Christopher HarleDepartment of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, IN 46202, United States.ORCID https://orcid.org/0000-0002-4803-3632
David F HesseHesse Foot and Ankle Clinic, SC, Eau Claire, WI 54751, United States.
Jacob KeanVA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System and University of Utah, Salt Lake City, UT 84148, United States.ORCID https://orcid.org/0000-0002-8577-0586
John LeeEdward Hospital, Naperville, IL 60540, United States.ORCID https://orcid.org/0009-0002-2390-0282
Tanja MagocIntegrated Data Repository Research Services, Clinical and Translational Science Institute, University of Florida, Gainesville, FL 32609, United States.ORCID https://orcid.org/0000-0001-8213-2266
Daniella MeekerSection of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT 06510, United States.ORCID https://orcid.org/0000-0002-1034-7628
Toan OngDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States.
Jyotishman PathakDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States.
Marc RosenmanAnn & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, United States.ORCID https://orcid.org/0000-0001-9403-345X
Laura K RusieHoward Brown Health, Chicago, IL 60640, United States.ORCID https://orcid.org/0009-0008-7193-6339
Akash J ShahNuvance Health, Danbury, CT 06810, United States.ORCID https://orcid.org/0009-0003-2626-4960
Lizheng ShiDepartment of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, United States.ORCID https://orcid.org/0000-0002-7827-6766
Aaron ThomasNorth Carolina Translational and Clinical Sciences Institute, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.ORCID https://orcid.org/0009-0001-4696-8856
William E TrickCenter for Health Equity & Innovation, Cook County Health, Chicago, IL 60612, United States.ORCID https://orcid.org/0000-0002-3243-5098
Shaun GrannisRegenstrief Institute Center for Biomedical Informatics, Indianapolis, IN 46202, United States.
Abel KhoCenter for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.ORCID https://orcid.org/0000-0003-1993-5634

Funding

CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
Center for Clinical and Translational Sciences (CCTS)UL1TR000371 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MCPHERSON, DAVID D · 2012 to 2016
$13.4M
NCATS NIH HHS UL1 TR000371NCATS NIH HHS UM1 TR004409
6 · The paper itself

Abstract

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.

Indexed as

data collectiondata completenessdata standardizationdemographic attributeselectronic health records (EHRs)interoperabilitypatient matchingrecord linkage

Identifiers

PMID38756741
PMCPMC10986191

What OpenQuestion holds

Textmetadata
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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.