Evidence map›Paper›PMID 36001872›Full record

ArticleJournal of medical Internet research2022

The Disparities in Patient Portal Use Among Patients With Rheumatic and Musculoskeletal Diseases: Retrospective Cross-sectional Study.

Enid Y Sun, Carolina Alvarez, Leigh F Callahan, Saira Z Sheikh

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. A novel approach to patient portal activation data to power equity improvements.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Disparities in Pediatric Rheumatic Diseases.Rheumatic diseases clinics of North America · 2022
    Review
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  14. Article
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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

4 authors.

Enid Y SunSection of Rheumatology, Department of Medicine, Temple Lewis Katz School of Medicine, Philadelphia, PA, United States.ORCID 0000-0003-1369-5799
Carolina AlvarezThurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC, United States.ORCID 0000-0003-1409-0898
Leigh F CallahanThurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC, United States.ORCID 0000-0001-6362-7220
Saira Z SheikhThurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC, United States.ORCID 0000-0003-4128-0443

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring the COVID-19 pandemic, the shift to virtual care became essential for the continued care of patients. Individuals with rheumatic and musculoskeletal diseases (RMDs) especially require frequent provider visits and close monitoring. To date, there have been limited studies examining inequities in health technology use among patients with RMDs.

objectiveOur goal was to identify characteristics associated with patient portal use before and after the COVID-19 pandemic in a convenience sample of patients with RMDs from a large academic medical center.

methodsIn this cross-sectional study, Epic electronic medical record data were queried to identify established patients of the University of North Carolina Hospitals adult rheumatology clinic between November 1, 2017, through November 30, 2019. Demographic and clinical data were collected to compare MyChart (Epic's patient portal) users with nonusers before and after the COVID-19 pandemic. MyChart activation and use were modeled using logistic regression and adjusted odds ratios, and confidence intervals were estimated.

resultsWe identified 5075 established patients with RMDs who met the inclusion criteria. Prior to the pandemic, we found that younger age (P<.001), suburban residence (P=.05), commercial/state insurance (P<.001), military insurance (P=.05), and median income >US $50,000 (P<.001) were associated with significantly higher odds of MyChart activation. Male sex (P<.001), being of Black or African American (P<.001) or "other" race (P<.001), Spanish as a primary language (P<.001), rural residence (P=.007), Medicaid insurance (P<.001), and median income of <US $25,000 (P=.01) were associated with lower odds of MyChart activation. Following COVID-19, younger age (P<.001), commercial insurance (P=.03), state insurance (P=.02), and median income of US $50,000-75,000 (P=.01) were associated with significantly higher odds of MyChart use. However, being of Black or African American (P<.001) or "other" race (P=.01), Spanish as a primary language (P=.002), male sex (P=.004), rural residence (P=.005), and having no insurance (P<.001) or Medicaid (P=.008) were associated with lower odds of MyChart use.

conclusionsResidence in a rural area, being of minority race/ethnicity, older age, male sex, lower median income, Medicaid, being uninsured, and non-English primary language are associated with lower odds of patient portal activation and use. Future health policy and clinical practice measures should focus on reducing barriers to health technology adoption among these groups.

Indexed as

COVID-19Musculoskeletal DiseasesPatient PortalsAdultCross-Sectional StudiesHumansMalePandemicsRetrospective StudiesUnited Stateschronic diseaseCOVID-19digital healthhealth care disparitieshealth technologyminority populationmusculoskeletal diseasesMyChartpatient portalrheumatologyrural areatelehealthtelemedicinevirtual care

Identifiers

PMID36001872
PMCPMC9439379

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.