Effectiveness of Mobile Medical Apps in Ensuring Medication Safety Among Patients With Chronic Diseases: Systematic Review and Meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Along with the rapid growth of the global aging society, the mobile and health digital market has expanded greatly. Countless mobile medical apps (mmApps) have sprung up in the internet market, aiming to help patients with chronic diseases achieve medication safety. OBJECTIVE: Based on the medication safety action plans proposed by the World Health Organization, we aimed to explore the effectiveness of mmApps in ensuring the medication safety of patients with chronic diseases, including whether mmApps can improve the willingness to report adverse drug events (ADEs), improve patients' medication adherence, and reduce medication errors. We hoped to verify our hypothesis through a systematic review and meta-analysis. METHODS: The meta-analysis was performed in strict accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included literature searched from 7 databases-PubMed, Web Of Science, Embase, CINAHL, China National Knowledge Infrastructure, Wanfang, and SinoMed. The publication time was limited to the time of database establishment to April 30, 2022. Studies were screened based on inclusion and exclusion criteria. The data extracted included authors, years of publication, countries or regions, participants' characteristics, intervention groups, and control groups, among others. Our quality assessment followed the guidelines of the Cochrane Handbook for Systematic Reviews of Interventions, Version 6.3. RevMan 5.2 software (Cochrane Collaboration) was used to analyze the statistical data, and a sensitivity analysis was performed to assess data stability. The degree of stability was calculated by using a different statistical method and excluding large-sample studies from the analysis. RESULTS: We included 8 studies from 5 countries (China, the United States, France, Canada, and Spain) that were published from January 1, 2014, to December 31, 2021. The total number of participants was 1355, and we analyzed the characteristics of included studies, each app's features, the risk of bias, and quality. The results showed that mmApps could increase ADE reporting willingness (relative risk [RR] 2.59, 95% CI 1.26-5.30; P=.009) and significantly improve medication adherence (RR 1.17, 95% CI 1.04-1.31; P=.007), but they had little effect on reducing medication errors (RR 1.54, 95% CI 0.33-7.29; P=.58). CONCLUSIONS: We analyzed the following three merits of mmApps, with regard to facilitating the willingness to report ADEs: mmApps facilitate more communication between patients and physicians, patients attach more importance to ADE reporting, and the processing of results is transparent. The use of mmApps improved medication adherence among patients with chronic diseases by conveying medical solutions, providing educational support, tracking medications, and allowing for remote consultations. Finally, we found 3 potential reasons for why our medication error results differed from those of other studies. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42022322072; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=322072.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,024 | 0,047 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».