ArticleDigital health
Identification of required capabilities of digital health ecosystems when preventing and managing non-communicable diseases.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Applications of Artificial Intelligence Technologies in Nursing for Noncommunicable Chronic Diseases: A Scoping Review.Journal of nursing management · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Non-communicable diseases cause annual mortality for 41 million people worldwide. These diseases include coronary heart disease, cancer, stroke, diabetes, and musculoskeletal as well as mental disorders. Innovation ecosystems in healthcare are multifactor networks in which different stakeholders interact together to create socio-economic (patient and cost) value via research, co-creation, and traditional market activities. Although there is much evidence about the impact of digital health interventions and the capabilities needed to support individual actors and specific diseases in non-communicable disease prevention and management, the current understanding of the concept of innovation ecosystems associated with theories is not well understood. There is also a lack of research about innovation ecosystems in the healthcare context. Or understanding of the holistic perspective of the capabilities needed in innovation ecosystems to support future digital health. The objective of this study was to answer this research gap by identifying what capabilities are needed in future digital health ecosystems related to people with non-communicable diseases or at risk of non-communicable diseases. By doing this, the study will help different organisations and policies address this very challenging situation. Methods: To answer this objective, a qualitative interview-based study including 34 semi-structured interviews was conducted in Finland. Complex adaptive systems theory was used as a theoretical lens to analyse empirical data. Results and conclusion: Several new capabilities were identified for digital health innovation ecosystems to make organisation managers and policymakers aware of how to deal with future health system demands. From the organisational perspective, capabilities are needed to use non-medical and heterogeneous data to support better treatments and clinical decision-making and provide better and safer data access. From the management perspective, hospitals need capabilities to allow critical experts to participate in innovation work, and overall, all ecosystem actors need capabilities to orchestrate research and innovation actions in the area of digital health.
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Registered trials
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 Socratic graph.