Learning health systems (LHS) are health and healthcare systems in which knowledge generation processes are embedded in daily practice to improve individual and population health. At its most fundamental level, a learning health system applies a conceptual approach wherein science, informatics, incentives, and culture are aligned to support continuous improvement, innovation, and equity, and seamlessly embed knowledge and best practices into care delivery[1][2][3]
The idea was first conceptualized in a 2006 workshop organized by the US Institute of Medicine (now the National Academy of Medicine (NAM)), building on ideas around evidence-based medicine[1] and "practice-based evidence".[4] and around recognition of the persistent gap between evidence generated in the context of biomedical research and the application of that evidence in the provision of care. The need to close this gap was further underscored by the growth of electronic health records (EHR) and other innovations in health information technology and computational power, and the resulting ability to generate data that can lead to better evidence and better outcomes. There has since been increasing interest in the topic, including the creation of the Wiley journal Learning Health Systems.[3]
Cornerstone elements of the LHS include:
generation, application, and improvement of scientific knowledge;
an organizational infrastructure that supports the engagement of communities of patients, healthcare professionals and researchers who collaborate to identify evidence gaps that could be addressed through research in routine healthcare settings;[5]
deployment of computational technologies and informatics approaches that organize and leverage large electronic health data sets, i.e. "big data" for use in research;
quality improvement at the point of care for each patient using new knowledge generated by research.
Other compatible ways of describing the LHS co-exist alongside the NAM definition, including the definition used by AHRQ, the Agency for Healthcare Research and Quality. AHRQ defines a learning health system as "a health system in which internal data and experience are systematically integrated with external evidence, and that knowledge is put into practice. As a result, patients get higher quality, safer, more efficient care, and health care delivery organizations become better places to work.”
In 2023, the NAM established ten core principles of learning health organizations to serve as a unifying touchstone for the field. The principles reflect and build upon the six aims of the seminal "Crossing the Quality Chasm" report published in 2001 (safe, equitable, effective, efficient, timely, and patient-centered),[6] and account for the ways in which health care has evolved since the publication of this 2001 report.
Engaged - Informed engagement, options, and choices for those who are served
Safe - Tested and up-to-date protocols to protect from harm
Effective - Evidence-based services tailored to understanding of each person's goals
Equitable - Parity in opportunity to attain desired health and goals
Efficient - Optimal outcomes for accessible, non-wasteful resources
Accessible - Effective services readily available where and when they are most needed
Measurable - Reliable and valid assessment of consequential activities and outcomes
Transparent - Clear information related to the nature, use, costs, and results of services
Secure - Validated access and use safeguards for digitally-mediated activities
Adaptive - Continuous learning and improvement are integral to organizational culture
^ abOlsen L, Aisner D, McGinnis JM, et al. (U.S. Institute of Medicine) (2007). "The Learning Healthcare System: Workshop Summary". Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care. National Academies Press (US). ISBN 978-0-309-10300-8. PMID 22379651. Archived from the original on 2020-11-26. Retrieved 2018-06-15.
^Grossmann C, Powers B, McGinnis JM, eds. (2011). Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care(PDF). Washington, DC. ISBN 978-0-309-15416-1. Archived (PDF) from the original on 2022-01-20. Retrieved 2018-06-15. {{cite book}}: |work= ignored (help)CS1 maint: location missing publisher (link)
^ abMcLachlan S, Potts HW, Dube K, Buchanan D, Lean S, Gallagher T, Johnson O, Daley B, Marsh W, Fenton N (June 2018). "The Heimdall Framework for Supporting Characterisation of Learning Health Systems". Journal of Innovation in Health Informatics. 25 (2): 77–87. doi:10.14236/jhi.v25i2.996. PMID 30398449. S2CID 53235486.
^Greene SM, Reid RJ, Larson EB (August 2012). "Implementing the learning health system: from concept to action". Annals of Internal Medicine. 157 (3): 207–10. doi:10.7326/0003-4819-157-3-201208070-00012. PMID 22868839. S2CID 6387264.
^Forrest CB, Margolis P, Seid M, Colletti RB (July 2014). "PEDSnet: how a prototype pediatric learning health system is being expanded into a national network". Health Affairs. 33 (7): 1171–7. doi:10.1377/hlthaff.2014.0127. PMID 25006143.
^Institute of Medicine (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington DC: National Academy Press. p. 360. doi:10.17226/10027. ISBN 978-0-309-46561-8. PMID 25057539.
and 28 Related for: Learning health systems information
Learninghealthsystems (LHS) are health and healthcare systems in which knowledge generation processes are embedded in daily practice to improve individual...
interest in the topic of learning from safety events such as incidents/accidents, or in collaborative learninghealthsystems). Research in such fields...
delivers health care services to meet the health needs of target populations. There is a wide variety of healthsystems around the world, with as many histories...
machine learningsystems, picking the best model for a task is called model selection. Artificial neural networks (ANNs), or connectionist systems, are computing...
technology for learning management systems (LMS), such as tools for student and curriculum management, and education management information systems (EMIS). Educational...
mental health conditions and improve the accuracy of diagnosis. Personalized treatment: AI-based systems can analyze data from electronic health records...
Increased tree-based machine learning models that allow flexibility in establishing health predictors Improvements in deep learning techniques and data logs...
Systems to study a variety of aspects in the eldercare field. For example, in 2002 the National Institute of Health awarded $566,000 to Fox Learning Systems...
Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...
machine learning, where even the AI's designers cannot explain why it arrived at a specific decision. XAI hopes to help users of AI-powered systems perform...
learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology...
A clinical decision support system (CDSS) is a health information technology that provides clinicians, staff, patients, and other individuals with knowledge...
professional cultures, change and restructuring, climate change and learninghealthsystems. His publications can be viewed on Google Scholar. From 2011 with...
Learning: A Learning Architecture for Navigation in Cloud Robotic Systems". 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)...
health decisions." The World Health Organization (WHO) defined Health Education as consisting of "consciously constructed opportunities for learning involving...
in training data. Therefore, machine learning models are trained inequitably and artificial intelligent systems perpetuate more algorithmic bias. For...
records systems. Although many products exist, only a small number of health practitioners use fully featured electronic health care records systems. In 1970...
Learning by Bonk and Graham. Graham challenged the breadth and ambiguity of the term's definition, and defined "blended learningsystems" as learning...
processing systems evolved into the standardized routines and packages of enterprise resource planning (ERP) software. On the whole, these ERP systems have...
three "Laws of learning": readiness, exercise, and effect. Since learning is an active process, students must have adequate rest, health, and physical...
and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies...
shared across different health care settings. Records are shared through network-connected, enterprise-wide information systems or other information networks...
supervised learning and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory...
machine learning are exemplary application scenarios from the Machinery & Equipment application area. In contrast to entirely virtual systems, in which...
Pacific Open LearningHealth Net (POLHN /ˈpɒlhən/) is a non-profit established in 2003, focused on distance education for health professionals working...
neuron systems did not activate and learning did not occur. Similar studies with humans also show similar evidence to the human mirror neuron system activating...
DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched...
The health care system in Japan provides different types of services, including screening examinations, prenatal care and infectious disease control, with...
Global Information