Evidence map›Paper›PMID 38314738›Full record

ArticleJournal of telemedicine and telecare2025

The association between rurality, dual Medicare/Medicaid eligibility and chronic conditions with telehealth utilization: An analysis of 2019-2020 national Medicare claims.

Cari A Bogulski, George Pro, Mahip Acharya, Mir M Ali, Clare C Brown, Corey J Hayes, Hari Eswaran

Abstract read
In one paragraph

Article in Journal of telemedicine and telecare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 18% of its field
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 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
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

7 authors at 2 institutions in 1 country.

Cari A BogulskiDepartment of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID 0000-0001-6002-5945
George ProDepartment of Health Behavior and Health Education, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Mahip AcharyaInstitute for Digital Health and Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Mir M AliInstitute for Digital Health and Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Clare C BrownDepartment of Health Policy and Management, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Corey J HayesDepartment of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Hari EswaranDepartment of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
University of Arkansas for Medical Sciences · USCentral Arkansas Veterans Healthcare System · US

Funding

Algorithmic fairness in predictive models to eliminate disparities in adverse infant outcomes: A case for raceK01MD018072 · NIMHD · UNIV OF ARKANSAS FOR MED SCIS · PI BROWN, CLARE C · 2022 to 2025
$496k
Data to Clinical Action: Using Predictive Analytics to Improve Care of Veterans with Opioid Use DisorderIK2HX003358 · VA · CENTRAL ARKANSAS VETERANS HLTHCARE SYS · PI Corey J Hayes · 2022 to 2026
–
HSRD VA IK2 HX003358NIMHD NIH HHS K01 MD018072
6 · The paper itself

Abstract

IntroductionTelehealth services have the potential to increase healthcare access among underserved populations, such as rural residents and racial/ethnic minority groups. The COVID-19 public health emergency led to unprecedented growth in telehealth utilization, but evidence suggests the growth has not been equitable across all patient populations. This study aimed to explore whether telehealth utilization and expansion changed equitably from 2019 to 2020 among sub-groups of Medicare beneficiaries.MethodsWe conducted an analysis of telehealth utilization among a 20% random sample of 2019-2020 Medicare beneficiaries on a national level. We fit multivariable logistic regression models and calculated average marginal effects (AME) to assess the association between demographic and clinical characteristics on telehealth utilization.ResultsWe found telehealth utilization was less likely among non-Hispanic Black/African-American (2019: adjusted odds ratio [aOR] = 0.77, AME = -0.15; 2020: aOR = 0.85, AME = -3.50) and Hispanic (2019: aOR = 0.79, AME = -0.13; 2020: aOR = 0.87, AME = -2.89) beneficiaries, relative to non-Hispanic White beneficiaries in both 2019 and 2020, with larger disparities in 2020. Rural beneficiaries were more likely to utilize telehealth than urban beneficiaries in 2019 (aOR = 2.62, AME = 0.84), but less likely in 2020 (aOR = 0.57, AME = -14.47). In both years, dually eligible Medicare/Medicaid beneficiaries were more likely than non-dually eligible beneficiaries to utilize telehealth (2019: aOR = 4.75, AME = 0.84; 2020: aOR = 1.34, AME = 2.25). However, the effects of dual eligibility and rurality changed in both models as the number of chronic conditions increased.DiscussionWe found evidence of increasing disparities in telehealth utilization among several Medicare beneficiary sub-groups in 2020 relative to 2019, including individuals of minority race/ethnicity, rural residents, and dually eligible beneficiaries, with disparities increasing among individuals with more chronic conditions. Although telehealth has the potential to address health inequities, our findings suggest that many of the patients in greatest need of healthcare are least likely to utilize telehealth services.

Indexed as

MedicaidMedicarePatient Acceptance of Health CareRural PopulationTelemedicineAgedAged, 80 and overChronic DiseaseCOVID-19Eligibility DeterminationFemaleHealthcare DisparitiesHealth Services AccessibilityHumansMaleUnited Stateschronic conditionsCOVID-19health equityMedicareTelehealth

Identifiers

PMID38314738
PMCPMC11298575
OpenAlexW4391540698

What OpenQuestion holds

Textmetadata
LicenceTDM
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.