Evidence map›Paper›PMID 38007224›Full record

ArticleBMJ health & care informatics2023

Quantifying digital health inequality across a national healthcare system.

Joe Zhang, Jack Gallifant, Robin L Pierce, Aoife Fordham, James Teo, Leo Celi, Hutan Ashrafian

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 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

27 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. A Scoping Review of the Mechanisms Influencing Socioeconomic Disparities in Outcomes of Digital Interventions for Weight-Related Behaviors.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
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  18. Rural health research in the 21st century: A commentary on challenges and the role of digital technology.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025
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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

7 authors.

Joe ZhangInstitute of Global Health Innovation, Imperial College London, London, UK joe.zhang@imperial.ac.uk.ORCID http://orcid.org/0000-0001-6040-2122
Jack GallifantDepartment of Critical Care, Guy's and St. Thomas' Hospital, London, UK.ORCID http://orcid.org/0000-0003-1306-2334
Robin L PierceUniversity of Exeter Law School, University of Exeter, Exeter, UK.
Aoife FordhamTransformation Directorate, NHS England, London, UK.
James TeoDepartment of Neurology, Kings College Hospital NHS Foundation Trust, London, UK.ORCID http://orcid.org/0000-0002-6899-8319
Leo CeliInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Hutan AshrafianInstitute of Global Health Innovation, Imperial College London, London, UK.

Funding

Wellcome Trust
6 · The paper itself

Abstract

objectivesDigital health inequality, observed as differential utilisation of digital tools between population groups, has not previously been quantified in the National Health Service (NHS). Deployment of universal digital health interventions, including a national smartphone app and online primary care services, allows measurement of digital inequality across a nation. We aimed to measure population factors associated with digital utilisation across 6356 primary care providers serving the population of England.

methodsWe used multivariable regression to test association of population and provider characteristics (including patient demographics, socioeconomic deprivation, disease burden, prescribing burden, geography and healthcare provider resource) with activation of two independent digital services during 2021/2022.

resultsWe find a significant adjusted association between increased population deprivation and reduced digital utilisation across both interventions. Multivariable regression coefficients for most deprived quintiles correspond to 4.27 million patients across England where deprivation is associated with non-activation of the NHS App.

conclusionResults are concerning for technologically driven widening of healthcare inequalities. Targeted incentive to digital is necessary to prevent digital disparity from becoming health outcomes disparity.

Indexed as

Health Status DisparitiesState MedicineEnglandHealthcare DisparitiesHumansElectronic Health RecordsHealth EquityInformatics

Identifiers

PMID38007224
PMCPMC10680008

What OpenQuestion holds

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LicenceCC BY-NC
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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.