The Learning Healthcare System: Where are we now? A systematic review

Budrionis, Andrius; Bellika, Johan Gustav

doi:10.1016/j.jbi.2016.09.018

Cited by 179 publications

(208 citation statements)

References 25 publications

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“…However, there is a lack of evidence on implementation of the lhs vision 37 . Our report is one of a few that demonstrate the feasibility of a lhs vision and measurable benefits in its application to a specific care problem, in our case, timely access to lung cancer care.…”

Section: Discussionmentioning

confidence: 99%

“…Most of these isolated interventions fail to improve the total wait time of the entire patient journey to treatment. It appears that shortening the time from referral to diagnosis has led to the transposition of wait times to other parts of the diagnostic and treatment trajectory, without changing the overall wait time for treatment 37 . All components of the care process have to be examined to address the complexity of health systems and to improve patient experience of a whole health system rather than its isolated parts .…”

Section: Discussionmentioning

confidence: 99%

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Regional Process Redesign of Lung Cancer Care: A Learning Health System Pilot Project

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Regional Process Redesign of Lung Cancer Care: A Learning Health System Pilot Project

et al. 2018

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“…4) Engage patients: this point is now receiving a large audience with the Blue Button initiative, that allows patients to access, or download, their own patient record [207]. [220]. Many initiatives including several IOM meeting reports focus on conceptual challenges hindering the adoption of LHS [221][222][223].…”

Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

“…The scale of reported studies varies from hundreds [227] to millions [228] individual patient records processed by distributed or centralized infrastructures. EHR data is often combined with patient reported outcomes to better address the aims of the LHS paradigm [220]. It provides a better understanding of "patient data shadow" [229] enabling personalization of care.…”

Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

“…The aforementioned projects suggest that health data can and will be used for improving the performance and quality of healthcare, lowering costs, and addressing the individual needs of the patient to a larger extent in the future. While successful implementations of LHS are reported, their impact remains poorly documented [220]. The benefits for patients, health services, and society are difficult to measure, however, knowing them could lead to faster adoption of data reuse practices and improve their acceptance by healthcare professionals.…”

Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

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Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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Infrastructure tools to support an effective Radiation Oncology Learning Health System

Kapoor,

Sleeman,

Ghosh

et al. 2023

J Applied Clin Med Phys

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PurposeRadiation Oncology Learning Health System (RO‐LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real‐time. This paper describes a novel set of tools to support the development of a RO‐LHS and the current challenges they can address.MethodsWe present a knowledge graph‐based approach to map radiotherapy data from clinical databases to an ontology‐based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology‐based keyword searching, synonym‐based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics.ResultsThe data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504 180 RDF (Resource Description Framework) tuples and visualized data relationships using graph‐based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances.ConclusionsThe framework and tools described support the development of a RO‐LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.

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The Learning Healthcare System: Where are we now? A systematic review

Abstract: The review demonstrated the interest of researchers in exploring the Learning Healthcare System ideas. However, it also revealed minimal focus on evaluating the impact of the novel paradigm on both healthcare service delivery and patient outcome.

Cited by 179 publications

References 25 publications

Regional Process Redesign of Lung Cancer Care: A Learning Health System Pilot Project

Regional Process Redesign of Lung Cancer Care: A Learning Health System Pilot Project

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Infrastructure tools to support an effective Radiation Oncology Learning Health System

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