How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology

Sanders, Jason C.; Showalter, Timothy N.

doi:10.3389/fonc.2018.00155

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…This type of learning is seen as high potential for accelerating research and implementing knowledge into practice, (eg, References 45 , 46 , 47 , 48 , 49 , 50 , 51 ) for instance via clinical induced research priorities,(eg, References 52 , 53 ); via generating and testing hypothesis without randomised controlled trials, (eg, Reference 54 ), and/or to evaluate treatment effectiveness in specific subgroups, that cannot be studied adequately in randomized, controlled trials. (eg, References 55 , 56 ) Guise et al stressed the two‐way interaction between research and clinical practice with improving health as ultimate aim: “ As such , the LHS concept requires that evidence generation not be an end in itself ; efforts to generate evidence must be accompanied by equally emphasized efforts to apply it to improve health . ” 57 Teare et al combined research and quality improvement to form an LHS, “improving health and services requires both better knowledge (research) and better action to adapt and use what is known (quality improvement) .…”

Section: Resultsmentioning

confidence: 99%

Conceptualizing learning health systems: A mapping review

Bruin

Bos

Struijs

et al. 2022

Learning Health Systems

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Introduction: Health systems worldwide face the challenge of increasing population health with high-quality care and reducing health care expenditure growth. In pursuit for a solution, regional cross-sectoral partnerships aim to reorganize and integrate services across public health, health care and social care. Although the complexity of regional partnerships demands an incremental strategy, it is yet not known how learning works within these partnerships. To understand learning in regional crosssectoral partnerships for health, this study aims to map the concept Learning Health System (LHS).Methods: This mapping review used a qualitative text analysis approach. A literature search was conducted in Embase and was limited to English-language papers published in the period 2015-2020. Title-abstract screening was performed using established exclusion criteria. During full-text screening, we combined deductive and inductive coding. The concept LHS was disentangled into aims, design elements, and process of learning. Data extraction and analysis were performed in MAX QDA 2020.Results: In total, 155 articles were included. All articles used the LHS definition of the Institute of Medicine. The interpretation of the concept LHS varied widely. The description of LHS contained 25 highly connected aims. In addition, we identified nine design elements.Most elements were described similarly, only the interpretation of stakeholders, data infrastructure and data varied. Furthermore, we identified three types of learning: learning as 1) interaction between clinical practice and research; 2) a circular process of converting routine care data to knowledge, knowledge to performance; and performance to data; and 3) recurrent interaction between stakeholders to identify opportunities for change, to reveal underlying values, and to evaluate processes. Typology 3 was underrepresented, and the three types of learning rarely occurred simultaneously. Conclusion:To understand learning within regional cross-sectoral partnerships for health, we suggest to specify LHS-aim(s), operationalize design elements, and choose deliberately appropriate learning type(s).

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Section: Resultsmentioning

confidence: 99%

Conceptualizing learning health systems: A mapping review

Bruin

Bos

Struijs

et al. 2022

Learning Health Systems

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“…The use of rapid-learning in radiotherapy has been considered by several authors at the conceptual level [127][128][129]. In this review we aimed to assess how widely rapid-learning is actually used to prospectively evaluate and optimise changes in clinical practice, and consider its readiness for managing change as part of routine radiotherapy.…”

Section: Discussionmentioning

confidence: 99%

Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

Price

Mackay

Aznar

et al. 2021

Radiotherapy and Oncology

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“…As the field of radiotherapy has become increasing more complex in terms of the increasing number and complexity of disparate information sources which must be aggregated to extract meaningful data, ART represents, in some sense, the index case of an unmet need for "Big Data" information support. This is both for ensuring patient safety and for correlating image-dose-response data in a clinical utilizable manner (51), because the volume, temporal, and spatial correlation of multiple elements (patient data, accumulated dose, images, DVH, DVF, positional shifts, toxicity/outcome) must be carefully collated, organized, curated, recorded and reported (52)(53)(54)(55)(56)(57). However, if the present situation persists, it will remain, as it is currently, almost impossible to effectively reconstruct an institutions' specific adaptive protocol in the absence of identical vendor-supplied treatment planning, registration, archiving, electronic medical record, toxicity and patient-reported outcomes collection, and outcome monitoring, barring significant resource allocation (58).…”

Section: Cataloguing Artmentioning

confidence: 99%

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

Heukelom

Fuller

2019

Seminars in Radiation Oncology

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For nearly two decades, adaptive radiation therapy (ART) has been proposed as a method to account for changes in head and neck tumor and normal tissue to enhance therapeutic ratios. While technical advances in imaging, planning and delivery have allowed greater capacity for ART delivery, and a series of dosimetric explorations have consistently shown capacity for improvement, there remains a paucity of clinical trials demonstrating the utility of ART. Furthermore, while ad hoc implementation of head and neck ART is reported, systematic full-scale head and neck ART remains an as yet unreached reality. To some degree, this lack of scalability may be related to not only the complexity of ART, but also variability in the nomenclature and descriptions of what is encompassed by ART. Consequently, we present an overview of the history, current status, and recommendations for the future of ART, with an eye towards improving the clarity and description of head and neck ART for interested clinicians, noting practical considerations for implementation of an ART program or clinical trial. Process level considerations for ART are noted, reminding the reader that, paraphrasing Elbert Hubbard, "Art is not a thing, it is a way." "When an artist uses a conceptual form of art, it means that all of the planning and decisions are made beforehand and the execution is a perfunctory affair. The idea becomes a machine that makes the art."-Sol LeWitt (1) The concept of "adaptive radiation therapy" (ART) has been widely praised, serially modeled in silico, and heavily discussed, but to date, at a practical level, remains rarely implemented in vivo outside the research setting. We aim to discuss the "state of the ART" in head and neck therapy, with an emphasis on specification of the intent with which ART is performed; the terms of ART used, or a disambiguation of the nomenclature; technical

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How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology

Cited by 5 publications

References 24 publications

Conceptualizing learning health systems: A mapping review

Conceptualizing learning health systems: A mapping review

Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

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