ArticleLearning health systems2023
Learning health system, positive deviance analysis, and electronic health records: Synergy for a learning health system.
Article in Learning health systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- A scoping review of outcome and outcome measure sets for adults with spinal cord injury, stroke, and traumatic brain injury.Journal of rehabilitation medicine · 2026Article
- Evaluating health organization readiness for implementing a learning health system: a scoping review.Health research policy and systems · 2025Article
- Improving information sharing in Medicaid home and community-based services with learning health systems: Implications for older adults and individuals living with disabilities and dementia.Learning health systems · 2025Article
- Charting progress in learning health systems: A systematic review of 5 years of definitions, models, and frameworks.Learning health systems · 2025Article
- Learning health system, positive deviance analysis, and electronic health records: Synergy for a learning health system.Learning health systems · 2023Article
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: Over the past decade, numerous efforts have encouraged the realization of the learning health system (LHS) in the United States. Despite these efforts, and promising aims of the LHS, the full potential and value of research conducted within LHSs have yet to be realized. New technology coupled with a catalyzing global pandemic have spurred momentum. In addition, the LHS has lacked a consistent framework within which "best evidence" can be identified. Positive deviance analysis, itself reinvigorated by recent advances in health information technology (IT) and ubiquitous adoption of electronic health records (EHRs), may finally provide a framework through which LHSs can be operationalized and optimized. Methods: We describe the synergy between positive deviance and the LHS and how they may be integrated to achieve a continuous cycle of health system improvement. Results: As we describe below, the positive deviance approach focuses on learning from high-performing teams and organizations. Conclusion: Such learning can be enabled by EHRs and health IT, providing a lens into how digital clinical interventions are successfully developed and deployed.
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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.