Evidence map›Paper›PMID 42519994›Full record

ArticleJMIR medical informatics2026

Logic Models on Health Information Technology-Related Interventions: Scoping Review Across Disciplines.

Elske Ammenwerth, Iiris Hörhammer, Michelle Bindel

Abstract readScoping Review
In one paragraph

Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Elske AmmenwerthUMIT TIROL - Private University for Health Sciences and Health Technology, Eduard Wallnöfer Zentrum 1, Hall in Tirol, 6060, Austria.ORCID 0000-0002-3244-6918
Iiris HörhammerAalto University, Helsinki, Uusimaa, Finland.ORCID 0000-0001-9024-4153
Michelle BindelUMIT TIROL - Private University for Health Sciences and Health Technology, Eduard Wallnöfer Zentrum 1, Hall in Tirol, 6060, Austria.ORCID 0000-0002-9383-9678

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

LogicMedical InformaticsModels, TheoreticalDigital HealthHumansimplementation sciencelogic modelmedical informaticsprogram evaluationprogram theorytheory of change

Identifiers

PMID42519994
PMCPMC13411438

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.