Integrating Mobile Health App Data Into Electronic Medical or Health Record Systems and Its Impact on Health Care Delivery and Patient Health Outcomes: Scoping Review
Notice bibliographique
Résumé
Background: Mobile health (mHealth) apps are increasingly being used to capture patient health data, provide information, and guide self-management, with reported improvements in health care service delivery and outcomes. However, the impact of integrating mHealth app data into electronic medical record or electronic health record (EMR/EHR) systems remains underexplored. Objective: This study aims to identify what is known about the impact of integrating mHealth app data into EMR/EHR systems on health care delivery and patient outcomes. Methods: A scoping review was conducted to identify original studies that investigated the integration of patient-facing mHealth app data into EMR/EHR systems and the impact on health care outcomes. The PubMed, Embase, Web of Science, Cochrane Library, CINAHL, ProQuest, and PsycINFO databases were searched for papers published between January 2014 and July 2024. Two authors independently screened and extracted data on study characteristics, mHealth app features, details of integration with EMR/EHR systems, and effects on health care delivery and patient outcomes. Results: Nineteen studies with 113,135 participants were included. Among these, 6 were randomized clinical trial studies, 8 were conducted in the United States, 12 occurred in hospital settings, 15 involved adult participants, and 6 targeted diabetes management. Main features of the apps and EMR/EHR systems can be categorized into tracking or recording health data (n=19), app data integrated into EMR/EHR systems (n=19), app data summarized or presented on EMR/EHR interface (n=19), communication with the health care team (n=12), reminders or alerts (n=10), synchronization with other apps or devices (n=8), educational information (n=4), and using existing portal credentials to app access (n=2). Most studies reported benefits of integrating the app and EMR/EHR, such as enhanced patient education and self-management (n=5), real-time data recorded and shared with clinicians (n=4), support for clinical decision-making (n=3), improved communication between patients and clinicians (n=7), and improved patient outcomes (n=13). Challenges identified included high drop-off rates in app usage (n=3), limited accessibility due to device restrictions (n=3), incompatibility between mHealth apps and EMR/EHR systems (n=3), increased clinical workload in response to additional information (n=3), data accuracy issues due to network connectivity (n=1), and data security concerns (n=1). Conclusions: Evidence suggests that the effective integration of mHealth app data into EMR/EHR systems can enhance both clinicians' health care delivery and patients' health outcomes. However, current literature is limited, and future opportunities remain to examine the impact on long-term outcomes, such as mortality, readmissions, and costs, and assess the scalability and sustainability of integration among more broader health conditions and disabilities across diverse health care settings.
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 | 0,021 | 0,101 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,008 |
| Bibliométrie | 0,019 | 0,020 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».