ReviewHealth research policy and systems2026
Available guidance for ethical challenges in learning health systems: an integrative literature review.
Review in Health research policy and systems, 2026. 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
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.
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.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundLearning health systems (LHSs) aim to integrate continuous learning into routine care, yet their development raises persistent ethical challenges. Questions remain about when and how informed consent should be obtained, how ethical oversight should be organized for learning activities that blur the boundary between care and research, and what system-level conditions are necessary to support ethically sound learning.
objectiveThis integrative review synthesizes ethical recommendations for the design, implementation and evaluation of LHSs, and aims to provide practical guidance and identify gaps in the current literature.
methodsA systematic search of PubMed, Web of Science and a search on LinkedIn on 22 October 2025 identified studies offering explicit ethical recommendations related to LHSs. Eligible studies were situated within an existing LHS and provided at least one actionable ethical recommendation. Data were extracted and analysed using Friedman's LHS functioning cycle of three knowledge-to-action steps: data to knowledge, knowledge to practice and practice to data.
resultsIn total, 44 studies met the inclusion criteria. Ethical guidance was unevenly distributed across Friedman's cycle. Most studies (n = 32) focused on transforming data into knowledge, addressing data governance, patient autonomy, consent models and ethical prerequisites for artificial intelligence (AI). Far fewer studies (n = 6) examined translating knowledge into practice, where attention centred on the ethical implementation of AI, workflow integration and risk mitigation. The final step, feeding performance back into new data, was represented (n = 6), with limited guidance on accountability, continuous monitoring and equitable interpretation of performance outcomes. Across all stages, informed consent and ethical oversight emerged as dominant themes, though considerable variation existed in how institutions operationalized these concepts.
conclusionsCurrent ethical discourse in LHSs remains focused mainly on transforming data to knowledge. Relatively limited recommendations for ethical implementation of other action steps were identified. In addition, some of the recommendations contradicted each other or offered differing advice on aspects of the ethical implementation of LHS (for instance, on informed consent and ethical review). This imbalance highlights the need for context-sensitive governance models, empirical evaluation of ethical practices in real-world LHSs and regulatory frameworks that reflect the dynamic nature of continuous learning.
Indexed as
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What OpenQuestion holds
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 OpenQuestion graph.