Reviewnpj health systems2025
Learning health system strategies in the AI era.
Review in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence: A Cluster Randomized Trial.JAMA network open · 2026Trial
- Learning Health Systems for Chronic Disease: Embedded Artificial Intelligence Tools That Continuously Improve Care.Preventing chronic disease · 2026Article
- Bridging primary care with artificial intelligence: toward a global 1.5-Tier Healthcare System.npj health systems · 2026Review
- Operationalizing One Digital Health and FAIR Data Principles for Disease Surveillance in a Low-Medium Income Country in Africa: Qualitative Study in the Democratic Republic of the Congo.JMIR medical informatics · 2026Article
- Designing clinical AI for patient-centered support beyond the visit: the PACT framework for health systems.npj health systems · 2026Review
- Exploring Barriers, Enablers, and Strategies for Implementing Learning Health System Projects in Healthcare Organizations: A Multilevel Analysis in an Australian Setting.Learning health systems · 2026Article
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Engaging Hospital Staff to Identify Levers for Adoption of Clinical Decision Support: Protocol for a Single-Site Case Study Using System Dynamics Group Model Building.JMIR research protocols · 2026Article
- A framework for using AI to drive care model transformation: building cars rather than faster horses.npj health systems · 2026Article
- Progress in educational theory and translational implementation of simulation-based education for healthcare professionals: an integrative analysis from theory to effectiveness.Frontiers in medicine · 2026Review
- Imagining Genomics and Population Health in 2050: Anticipating Future Research, Policy, and Governance Needs.Public health genomics · 2026Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
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
The learning health system (LHS) offers a framework to accelerate evidence generation and care improvement, yet widespread adoption remains limited. In this perspective, we explore strategies to operationalize the LHS in the era of artificial intelligence, including biomedical informatics and health information technology integration, workforce development, quality improvement, and data governance. We highlight promising institutional models and propose policy, educational, and financial reforms to support scalable, value-driven innovation in increasingly complex and resource-constrained health systems.
Identifiers
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.