Logic Models on Health Information Technology-Related Interventions: A Scoping Review Across Disciplines (Preprint)
Notice bibliographique
Résumé
Abstract Background Health information technology (HIT) interventions are complex, context-dependent, and often insufficiently theorized, which can hinder their design, implementation, and evaluation. Program theory approaches such as logic models and theory of change (ToC) are well-established in public health and implementation science for articulating causal assumptions. Their use in medical informatics, however, appears inconsistent. A systematic overview of how logic models and ToC have been applied to HIT interventions is therefore needed to support theory-informed development and cumulative learning in the field. Objective We aimed to map how logic models, ToC, and related program theory approaches have been conceptualized, constructed, and applied in HIT-related interventions across disciplines. A secondary objective was to identify implications for medical informatics research and practice. Methods Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews), searches were conducted in PubMed, Web of Science, Academic Search Elite, APA PsycArticles, and CINAHL. Eligible publications used a logic model, ToC, or related construct within an HIT intervention in any health care or social-care setting. Next, 2 reviewers independently screened records and extracted data on study characteristics, type and purpose of HIT, model structure, theoretical foundations, and reported benefits and challenges. Results A total of 69 publications (2012‐2025) met the criteria. Use of program theory increased markedly after 2020 and spanned medical informatics, public health, health services research, and implementation science. Logic models were most frequently applied to patient-facing and self-management technologies, particularly mobile health, telehealth, and home-based remote monitoring. Most models were used to support HIT development or evaluation. Of the total, 60 (87%) studies provided a logic model visualization, although structures varied considerably. Out of 69, 50 (72%) studies cited guidelines for model development, most commonly UK Medical Research Council guidance, realist evaluation, or the Kellogg Logic Model. Out of 69 studies, 28 (41%) used behavioral or implementation frameworks, such as Capability, Opportunity, Motivation–Behavior model (COM-B), Consolidated Framework for Implementation Research (CFIR), Expert Recommendations for Implementing Change (ERIC), Fit between Individuals, Task, and Technology (FITT), or Non-adoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability (NASSS) to populate model content. Only 3 (4%) studies reused an existing model. Reported benefits concerned improved theorization, structured evaluation, and stakeholder engagement; challenges included limited empirical evidence, high resource demands, and tensions between specificity and generalizability. Conclusions Program theory approaches are increasingly used to conceptualize and evaluate HIT interventions; yet, their application in medical informatics remains fragmented. More systematic and theory-informed use of logic models could enhance conceptual clarity, methodological rigor, and cumulative learning. Future work should promote model reuse, establish repositories, strengthen reporting standards, and integrate program theory in HIT education and research to support coherent development, evaluation, and scaling of digital health interventions.
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 | 0,062 | 0,234 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,025 | 0,031 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
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 ».