ArticleLearning health systems2025
Learning about learning health systems: Strengthening theory through continuous evaluation and improvement.
Article in Learning health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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Who cites it
6 citing papers in PubMed.
- Supporting Cultural and Learning Enablers for a Learning Health System (SCALE): A Program Theory Approach.Learning health systems · 2026Article
- Reflections on a global partnership for perinatal quality improvement in Rwanda.BMJ paediatrics open · 2026Review
- Complexity of healthcare improvement - a comparative analysis of frameworks for improvement science.Frontiers in health services · 2026Article
- Improvement science as the engine for the next generation of learning health networks.Frontiers in health services · 2026Article
- Learning about learning health systems: Strengthening theory through continuous evaluation and improvement.Learning health systems · 2025Article
- Ten Reasons Why Learning Health Systems Will Have a Transformational Effect on Health and Health Care.Learning health systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Learning Health Systems (LHSs) offer potential for transforming healthcare through continuous learning and improvement. However, the current literature lacks a robust connection between theory and practice, limiting knowledge transferability across diverse healthcare environments. This article proposes a novel framework for integrating theorizing and continuous evaluation into LHSs, illustrated through the case of the Bipolar Action Network (The Network). Methods: We use a process of theorizing to structure how mid-range theory, program theory and LHS design, development and evaluation can support improved practices and theories. We use the Conceptual Framework for Value-Creating Learning Health Systems as our initial mid-range theory, and the Bipolar Action Network serves as an illustrative case. Results: The framework emphasizes continuous multi-level LHS theory refinement based on real-world data, ensuring that both the system and its theoretical underpinnings evolve in response to new insights and challenges by connecting four steps: (1) Selecting an initial mid-range theory, (2) Creating a program theory for a specific LHS, (3) Evaluating LHS performance using operational data, and (4) Using evaluation findings to refine both the LHS program and mid-range theory. Conclusions: This article contributes to the field by offering a practical methodology for bridging the gap between LHS theory and practice. By promoting ongoing theorizing and evaluation, our framework aims to both enhance the effectiveness and adaptability of LHSs, as well as inform theory development. Challenges remain, including resource intensity for data infrastructure and potential limitations in data quality or accessibility, which must be addressed to realize the full potential of LHSs as adaptive, theory-driven systems.
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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 OpenQuestion graph.