Evidence map›Paper›PMID 38094223›Full record

ArticleSSM - population health2024

Examining the existing usage gap of electronic health records in the United States: A study of National Health Survey.

Qinglin Hu, Yuhui Yao, Jing Han, Xin Thomas Yang, Jason Parton

Abstract read
In one paragraph

Article in SSM - population health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Qinglin HuInstitute of Data and Analytics, The University of Alabama, Tuscaloosa, AL, 35487, USA.
Yuhui YaoDepartment of Community Medicine and Population Health, The University of Alabama, Tuscaloosa, AL, 35487, USA.
Jing HanDepartment of Management, Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL, 35487, USA.
Xin Thomas YangInstitute of Data and Analytics, The University of Alabama, Tuscaloosa, AL, 35487, USA.
Jason PartonInstitute of Data and Analytics, The University of Alabama, Tuscaloosa, AL, 35487, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Little is known about the extent of geographic variation in online health record usage and related demographic characteristics in the United States. Methods: In order to examine geographical variation in the usage of online e-health records (EHR) patient portals in the US, and the sociodemographic factors effects on the access and use of the EHR patient's portal. This study using data from the 2019 and 2020 Health Information National Trends Survey. Specifically, predictors associated with accessing patients' EHR portal were examined. Furthermore, geographic variation of EHR portal' availability and usage gap were examined and mapped. Results: Respondents had significantly higher likelihood to access EHR portals when they are higher educated, willing to seek health information online, insured and had regular providers (adjusted OR = 2.01, 95% CI: 1.44 - 2.80; adjusted OR = 3.51, 95% CI: 2.49 - 4.94; adjusted OR = 2.38, 95% CI: 1.05 - 5.43; adjusted OR = 2.1, 95% CI: 1.51 - 2.92, respectively). Individuals living in Central-West, South regions or other non-urban areas as well as deprived urban areas are less likely to access their EHR portals (adjusted OR = 0.6, 95% CI: 0.41 - 0.89). Furthermore, we found that people living in the Midwest, Southern regions, and Mountain rural areas are more likely to have greater difficulties to access EHR than other regions. Therefore, populations residing in these underserved (deprived urban, rural or remote) areas tend to face more considerable obstacles to e-healthcare. Conclusions: Improve the disparities, accessibility, and educational initiatives on the usage of eHealth resources and encouragement from both healthcare providers and policymakers should be implemented with a particular focus on targeting non-urban areas and underserved population.

Indexed as

Electronic health recordsGeographical variationsHealth disparitiesHealth managementUnderserved population

Identifiers

PMID38094223
PMCPMC10716783

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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.