Evidence map›Paper›PMID 39098993›Full record

ArticleBritish journal of clinical pharmacology2025

Mobile applications on app stores for deprescribing: A scoping review.

Lina Okati, Sarita Lo, Danijela Gnjidic, Susan Jiayu Li, Janani Thillainadesan

Abstract readScoping Review
In one paragraph

Article in British journal of clinical pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Mobile applications on app stores for deprescribing: A scoping review.British journal of clinical pharmacology · 2025
    Article
  9. Review
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.

Lina OkatiSchool of Pharmacy, Faculty of Medicine and Health, University of Sydney, Camperdown, Sydney, Australia.
Sarita LoCentre for Education and Research on Ageing, Concord Hospital, Concord, Sydney, Australia.
Danijela GnjidicSchool of Pharmacy, Faculty of Medicine and Health, University of Sydney, Camperdown, Sydney, Australia.ORCID https://orcid.org/0000-0002-9404-3401
Susan Jiayu LiDepartment of Geriatric Medicine, Concord Hospital, Concord, Sydney, Australia.
Janani ThillainadesanCentre for Education and Research on Ageing, Concord Hospital, Concord, Sydney, Australia.ORCID https://orcid.org/0000-0002-5287-7483

Funding

Royal Australasian College of Physicians Vincent Fairfax Research Fellowship
6 · The paper itself

Abstract

Deprescribing is an evidence-based intervention to reduce potentially inappropriate medication use. Yet its implementation faces barriers including inadequate resources, training and time. Mobile applications (apps) on app stores could address some barriers by offering educational content and interactive features for medication assessment and deprescribing guidance. A scoping review was undertaken to examine existing deprescribing apps, identifying features including interactive and artificial intelligence (AI) elements. A comprehensive search was conducted in August 2023 to identify mobile apps with deprescribing content within the Apple and Google Play Stores. The apps found were screened for inclusion, and data on their features were extracted. Quality assessment was undertaken using the Mobile App Rating Scale. Six deprescribing-related apps were identified: the American Geriatrics Society Beers Criteria 2023, Dementia Training Australia Medications, Evidence-Based Medicine Guide, Information Assessment Method Medical Guidelines, MedGPT-Medical AI App, and Polypharmacy: Manage Medicines. These apps focused primarily on educating both patients/carers and healthcare professionals about deprescribing. Amongst them, two apps included interactive features, with one incorporating AI technology. While these features allowed for search queries and input of patient-level details, the apps provided limited personalised deprescribing advice. In terms of quality, the apps scored highly on functionality and information, and poorly on engagement and aesthetics. This review found deprescribing apps, despite being educational, have limitations in personalization and user engagement. Future research should prioritize evaluating their feasibility and user experience in clinical settings, and further explore how AI and interactivity could enhance the usefulness of these apps for deprescribing practices.

Indexed as

Artificial IntelligenceDeprescriptionsMobile ApplicationsHumansInappropriate PrescribingPolypharmacydeprescribinginteractivitymobile applicationspolypharmacy

Identifiers

PMID39098993
PMCPMC11671330

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.