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Learning radiotherapy: the state of the art.

ABSTRACT: BACKGROUND:The last two decades have seen revolutionary developments in both radiotherapy technology and postgraduate medical training. Trainees are expected to attain competencies using a mix of experiential learning, formal postgraduate teaching, self-directed learning and peer education. Radiation (Clinical) Oncology is a recognised 'craft specialty' where the apprenticeship model of training is applicable. This scoping review examines the evidence in relation to how medical trainees learn radiotherapy. METHODS:A systematic search of MEDINE and EMBASE was undertaken to identify studies of trainee and/or trainer experience of radiotherapy learning published 1999-2018. Results pertaining to Medical Oncology, workforce trends, undergraduate radiotherapy exposure, academic training, global health, non-medical staff, health service infrastructure and recruitment to training programmes were not included. RESULTS:A total of 146 publications were included in the synthesis. Five themes were apparent through careful iterative analysis representing broadly inter-related issues. Most articles studied radiotherapy training from the perspective of the trainee doctor. Most literature reports results of observational, local or national surveys with a tightly defined scope. Considerable variation exists within hospitals, within countries, over time and between different curricular areas. CONCLUSIONS:Medical education has not kept pace with changes in the field of radiotherapy and large differences are demonstrated in experience between trainees in different hospitals, countries and training stages. Interpersonal relationships, departmental organisation, and national curricula impact on training quality. Qualitative and quantitative research examining modern radiotherapy learning has been uncommon and uncoordinated, until recently. To date no single study has been designed to comprehensively assess a department's training scheme.

SUBMITTER: Walls GM

PROVIDER: S-EPMC7216702 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Learning radiotherapy: the state of the art.

Walls Gerard M GM Hanna Gerard G GG McAleer James J JJ

BMC medical education 20200511 1

<h4>Background</h4>The last two decades have seen revolutionary developments in both radiotherapy technology and postgraduate medical training. Trainees are expected to attain competencies using a mix of experiential learning, formal postgraduate teaching, self-directed learning and peer education. Radiation (Clinical) Oncology is a recognised 'craft specialty' where the apprenticeship model of training is applicable. This scoping review examines the evidence in relation to how medical trainees ...[more]

PMID: 32393250

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Project description:BACKGROUND:Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. RESULTS:The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. CONCLUSION:We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.

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Learning radiotherapy: the state of the art.

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Learning radiotherapy: the state of the art.

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