Evidence map›Paper›PMID 39536310›Full record

Observational studyJournal of medical Internet research2024

Differences in Use of a Patient Portal Across Sociodemographic Groups: Observational Study of the NHS App in England.

Sukriti Kc, Chrysanthi Papoutsi, Claire Reidy, Bernard Gudgin, John Powell, Azeem Majeed, Felix Greaves, Anthony A Laverty

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Article
  3. Yonder: Antidepressant follow-up, NHS app use, sore throats in community pharmacy, and dementia and anticholinergics.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
    Article
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

8 authors.

Sukriti KcImperial College London, School of Public Health, London, United Kingdom.ORCID 0000-0003-3525-3908
Chrysanthi PapoutsiUniversity of Oxford, Nuffield Department of Primary Care Health Sciences, Oxford, United Kingdom.ORCID 0000-0003-1189-7100
Claire ReidyUniversity of Oxford, Nuffield Department of Primary Care Health Sciences, Oxford, United Kingdom.ORCID 0000-0002-0013-6843
Bernard GudginPublic Member, Cambridge, United Kingdom.ORCID 0000-0001-9606-5121
John PowellUniversity of Oxford, Nuffield Department of Primary Care Health Sciences, Oxford, United Kingdom.ORCID 0000-0002-1456-4857
Azeem MajeedImperial College London, School of Public Health, London, United Kingdom.ORCID 0000-0002-2357-9858
Felix Greaves *Imperial College London, School of Public Health, London, United Kingdom.ORCID 0000-0001-9393-3122
Anthony A Laverty *Imperial College London, School of Public Health, London, United Kingdom.ORCID 0000-0003-1318-8439

Funding

National Health Service (NHS) /Department of Health and Social CareNational Institute of Health Research (NIHR) Health Services Delivery and Research Programme NIHR128285
6 · The paper itself

Abstract

backgroundThe adoption of patient portals, such as the National Health Service (NHS) App in England, may improve patient engagement in health care. However, concerns remain regarding differences across sociodemographic groups in the uptake and use of various patient portal features, which have not been fully explored. Understanding the use of various functions across diverse populations is essential to ensure any benefits are equally distributed across the population.

objectiveThis study aims to explore differences in the use of NHS App features across age, sex, deprivation, ethnicity, long-term health care needs, and general practice (GP) size categories.

methodsWe used weekly NHS App use data from the NHS App dashboard for 6386 GPs in England from March 2020 to June 2022. Negative binomial regression models explored variations in weekly rates of NHS App features used (registrations, log-ins, prescriptions ordered, medical record views, and appointments booked). Outcomes were measured as weekly rates per 1000 GP-registered patients, and we conducted separate models for each outcome. Regression models included all covariates mentioned above and produced incident rate ratios, which we present here as relative percentages for ease of interpretation. GP-level covariate data on sociodemographic variables were used as categorical variables in 5 groups for deprivation (Q1=least deprived practices and Q5=most deprived practices) and 4 groups for all other variables (Q1=least deprived practices and Q4=most deprived practices).

resultsWe found variations in the use of different features overall and across sociodemographic categories. Fully adjusted regression models found lower use of features overall in more deprived practices (eg, Q5 vs Q1: registrations=-34%, log-ins=-34.9%, appointments booked=-39.7%, medical record views=-32.3%, and prescriptions ordered=-9.9%; P<.001). Practices with greater proportions of male patients also had lower levels of NHS App use (eg, Q4 vs Q1: registration=-7.1%, log-in=-10.4%, and appointments booked=-36.4%; P<.001). Larger practices had an overall higher use of some NHS App features (eg, Q4 vs Q1: registration=3.2%, log-ins=11.7%, appointments booked=73.4%, medical record views=23.9%, and prescriptions ordered=20.7%; P<.001), as well as those with greater proportions of White patients (eg, Q4 vs Q1: registration=1.9%, log-ins=9.1%, appointments booked=14.1%, medical record views=28.7%, and prescriptions ordered=130.4%; P<.001). Use patterns varied for practices with greater proportions of patients with long-term health care needs (eg, Q4 vs Q1: registrations=-3.6%, appointments booked=-20%, and medical record views=6%; P≤.001).

conclusionsThis study highlights that the use of the NHS App features varied across sociodemographic groups. In particular, it is used less by people living in more deprived areas. Tailored interventions and patient support are required to ensure that any benefits from the NHS App are spread equally throughout the population.

Indexed as

Patient PortalsState MedicineAdolescentAdultAgedEnglandFemaleHumansMaleMiddle AgedMobile ApplicationsSociodemographic FactorsYoung Adultappdemographicsdeprivationdigital healthdigital technologydisparitiesecologicaleHealthEnglandgeneral practiceinequalityinterventionlong-term health carenegative binomial regression modelobservationalpatientpatient portalpatient portalspatient supporttechnological health divideyouth

Identifiers

PMID39536310
PMCPMC11602757

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.