Patient Acceptance and Barriers to IoT Utilization in Healthcare: A Systematic Literature Review (Preprint)
Notice bibliographique
Résumé
Background: The Internet of Things (IoT) represents a transformative paradigm in health care service delivery, offering unprecedented potential to enhance quality of care, operational efficiency, and patient outcomes through interconnected devices and real-time data analytics. Despite rapid adoption, implementation depends on patient acceptance as end users. While literature extensively documents technical and institutional perspectives, a comprehensive understanding of patient acceptance factors remains fragmented, with high technology abandonment rates. Systematic synthesis of patient perspectives is critically needed to inform user-centered design, effective implementation strategies, and supportive policy frameworks. Objective: This systematic literature review aims to identify and synthesize factors influencing patient acceptance of IoT technology in health care services, barriers hindering adoption, and effective strategies for enhancing acceptance. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched eight electronic databases (PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, ScienceDirect, ACM Digital Library, ProQuest, and Google Scholar) for empirical studies published between January 2016 and December 2024. Inclusion criteria encompassed peer-reviewed empirical research examining patient perspectives on IoT technology in health care services, published in English or Indonesian. From 2537 initially identified papers, 62 studies met inclusion criteria after systematic screening and full-text evaluation. Quality assessment was conducted using the Mixed Methods Appraisal Tool. Results: The 62 included studies represented diverse geographic contexts (Asia, Europe, North America, and the Middle East) and methodological approaches. Quality assessment revealed 45 (73%) studies of good-to-excellent quality. Perceived usefulness emerged as the strongest acceptance facilitator, identified in 55 of 62 (89%) studies, followed by perceived ease of use in 47 (76%) studies, and trust and security in 42 (68%) studies. Cost-effectiveness was identified in 32 (52%) studies as an important consideration. Primary barriers included data security concerns in 26 (42%) studies, privacy issues in 24 (39%) studies, lack of digital literacy in 22 (36%) studies, and resistance to change in 20 (32%) studies. Interoperability issues and high costs were identified in 19 (31%) studies and 18 (29%) studies, respectively. User-centered design was the most frequently recommended enhancement strategy in 20 (32%) studies, followed by user-friendly interface development in 19 (31%) studies, digital literacy programs in 18 (29%) studies, and health care professional involvement in 15 (24%) studies. Digital literacy functioned as a significant moderator, and trust served as both direct predictor and mediator. Conclusions: Patient acceptance of IoT in health care represents a complex, multidimensional phenomenon requiring holistic approaches that integrate user-centered technology design, comprehensive digital literacy programs, trust-building mechanisms, and supportive policy frameworks. Successful implementation necessitates multilevel strategies addressing individual, organizational, and system factors simultaneously. With evidence-based, patient-centered approaches, IoT technology holds substantial potential to transform health care delivery into more proactive, personalized, and accessible services.
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 | 0,033 | 0,122 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,010 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 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 ».