scholarly journals Simulation as More Than a Treatment-Planning Tool: A Systematic Review of the Literature on Radiation Oncology Simulation-Based Medical Education

2018 ◽  
Vol 102 (2) ◽  
pp. 257-283 ◽  
Author(s):  
Michael K. Rooney ◽  
Fan Zhu ◽  
Erin F. Gillespie ◽  
Jillian R. Gunther ◽  
Ryan P. McKillip ◽  
...  
2016 ◽  
Vol 26 (4) ◽  
pp. 617-622 ◽  
Author(s):  
Sabrina M. Neeley ◽  
Catherine A. Ulman ◽  
Bette S. Sydelko ◽  
Nicole J. Borges

2013 ◽  
Vol 28 (8) ◽  
pp. 1078-1089 ◽  
Author(s):  
Benjamin Zendejas ◽  
Ryan Brydges ◽  
Amy T. Wang ◽  
David A. Cook

2021 ◽  
Vol 11 ◽  
Author(s):  
Stefania Volpe ◽  
Matteo Pepa ◽  
Mattia Zaffaroni ◽  
Federica Bellerba ◽  
Riccardo Santamaria ◽  
...  

Background and PurposeMachine learning (ML) is emerging as a feasible approach to optimize patients’ care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT).Materials and MethodsElectronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1.ResultsForty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation).Discussion and ConclusionThe range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.


2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Amin Beigzadeh ◽  
Nikoo Yamani ◽  
Kambiz Bahaadinbeigy ◽  
Peyman Adibi

2021 ◽  
Author(s):  
Margaret Faux ◽  
Jon Adams ◽  
Jon Wardle

ABSTRACTIntroductionThe WHO has suggested the solution to leakage in health systems caused by waste, corruption and fraud is policing and prosecution. However, a growing body of evidence suggests leakage may not always be fraudulent or corrupt, with researchers suggesting medical practitioners may sometimes struggle to understand increasingly complex legal requirements around health financing and billing transactions, which may be improved through education. To explore this phenomenon further, we undertook a systematic review of the literature to identify the medical billing education needs of medical practitioners and whether those needs are being met.MethodsEligible records included English language materials published between 1 January 2000 and 4 May 2020, including empirical research, commentary, opinions and grey literature.ResultsWe identified 74 records as directly relevant to the search criteria. Despite a comprehensive international search, studies were limited to three countries (Australia, Canada, U.S), indicating a need for further work internationally. The literature suggests the education needs of medical practitioners in relation to medical billing compliance are not being met and medical practitioners desire more education on this topic. Evidence suggests education may be effective in improving medical billing compliance and reducing waste in health systems and there is broad agreement amongst medical education stakeholders in multiple jurisdictions that medical billing should be viewed as a core competency of medical education, though there is an apparent inertia to act. Penalties for non-compliant medical billing are serious and medical practitioners are at risk of random audits and investigations for breaches of sometimes incomprehensible, and highly interpretive regulations they may never have been taught.ConclusionDespite acknowledged significance of leakage in health systems due to poor practitioner knowledge of billing practices, there has been very little research to date on education interventions to improve health system efficiency at a practitioner level.


Brachytherapy ◽  
2020 ◽  
Vol 19 (6) ◽  
pp. 738-745
Author(s):  
Shane Mesko ◽  
Bhavana V. Chapman ◽  
Chad Tang ◽  
Rajat J. Kudchadker ◽  
Teresa L. Bruno ◽  
...  

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