Evidence map›Paper›PMID 38288794›Full record

ReviewCurrent pharmaceutical biotechnology2024

Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness using a Personalized Second-generation Digital System.

Areej Bayatra, Rima Nasserat, Yaron Ilan

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current pharmaceutical biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 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

3 authors.

Areej BayatraDepartment of Medicine, the Hebrew University-Hadassah Medical Center, Jerusalem, Israel.
Rima NasseratDepartment of Medicine, the Hebrew University-Hadassah Medical Center, Jerusalem, Israel.
Yaron IlanDepartment of Medicine, the Hebrew University-Hadassah Medical Center, Jerusalem, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLow adherence to chronic treatment regimens is a significant barrier to improving clinical outcomes in patients with chronic diseases. Low adherence is a result of multiple factors.

methodsWe review the relevant studies on the prevalence of low adherence and present some potential solutions.

resultsThis review presents studies on the current measures taken to overcome low adherence, indicating a need for better methods to deal with this problem. The use of first-generation digital systems to improve adherence is mainly based on reminding patients to take their medications, which is one of the reasons they fail to provide a solution for many patients. The establishment of a second-generation artificial intelligence system, which aims to improve the effectiveness of chronic drugs, is described.

conclusionImproving clinically meaningful outcome measures and disease parameters may increase adherence and improve patients' response to therapy.

Indexed as

Artificial IntelligenceMedication AdherenceChronic DiseaseHumansPrecision MedicineReminder SystemsadherenceArtificial intelligencechronic drugsdigital healthpersonalized medicinevariability.

Identifiers

What OpenQuestion holds

Textmetadata
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.