Evidence map›Paper›PMID 37840509›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2024

The development and impact of an app for a smart drug interaction reminder system.

Hung-Fu Lee, Pei-Hung Liao

Open access · bronzeAbstract read
In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 78% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Hung-Fu LeeSchool of Nursing.
Pei-Hung LiaoSchool of Nursing.
National Taipei University of Nursing and Health Science · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImproved access to media and medical knowledge has elicited stronger public health awareness.

objectiveThis study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload.

methodsThis study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction.

resultsThe results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions.

conclusionUsing information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future.

Indexed as

Drug InteractionsMobile ApplicationsReminder SystemsAdultFemaleHealth Knowledge, Attitudes, PracticeHumansJob SatisfactionMaleMiddle AgedPatient Education as TopicTaiwanapplicationdata miningDrug interactionnursesreminder systems

Identifiers

PMID37840509
PMCPMC11091626
OpenAlexW4387613905

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.