Use and Application of mHealth Technologies in Perioperative Surgical Care: Narrative Review
Notice bibliographique
Résumé
BACKGROUND: Surgical procedures and their potential complications place substantial strain on patients, clinicians, and health care systems. These strains are driven by the anticipated morbidity and mortality, so that there is resource-intensive postoperative inpatient management. Given the concentration of surgical services within hospital settings, current standard levels of care have limitations such as communication gaps, time lapses before evaluation, and investment of resources, which limit accessibility and generate disparities in delivery of care. However, recent advances in digital health, including telemedicine platforms, mobile health (mHealth), and wearable technologies, present an opportunity to decentralize and extend perioperative care into community settings. This review explored how established mHealth technologies are being integrated into the perioperative pathway and their impact on surgical care delivery and outcomes. It also highlights possible emerging models of remote physician and patient interaction where benefits seem to be outweighing the risks. OBJECTIVE: The aim of this narrative review was to present collected evidence for the use of established mHealth technologies in the surgical pathway of patients and highlight their readiness and potential in models of standard care. METHODS: A comprehensive literature search was conducted across MEDLINE (via PubMed), Web of Science, and Scopus databases between October 2022 and May 2024. Additional sources were identified through reference list screening of relevant systematic reviews. Data were extracted and analyzed based on surgical specialty, type of mHealth intervention, cost-effectiveness, and ethical considerations. Findings were summarized in tables to illustrate key trends and variations across studies. The extracted data were tabulated and described qualitatively to highlight similarities, differences, and possible emerging trends across the studies. RESULTS: A total of 28 articles published between 2008 and 2022 were included for qualitative analysis, with most (n=21, 75%) originating from the United States, Germany, and the United Kingdom. The study designs were predominantly randomized controlled trials (n=9, 32%) and observational studies (n=8, 29%). Collectively, these studies involved 6344 patients undergoing mHealth-based perioperative interventions primarily in general surgery, orthopedics, and oncology. Interventions frequently used smartphones (n=10, 36%) and wearable devices, often in combination with other tracking and measuring systems. Applications included wound monitoring, postoperative follow-up, and patient education. Data collection was multimodal and typically conducted daily, yet only 36% (10/28) of the articles reported defined follow-up periods. Cost-effectiveness was rarely assessed, with only 4% (1/28) of the articles reporting per-patient savings. Overall, 64% (18/28) of the articles were rated as low quality due to methodological limitations. CONCLUSIONS: mHealth- and telehealth-based interventions show promise in enhancing aspects of perioperative care by enabling remote monitoring, patient engagement, and improved care continuity. Future research should focus on scalable implementation, true cost-effectiveness analysis, equitable access, and integration into clinical workflows to ensure broad applicability in current models of care.
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 | 0,004 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,009 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».