The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs

2020 ◽  
Vol 25 (5) ◽  
pp. 1057-1086 ◽  
Author(s):  
Martin G. Tolsgaard ◽  
Christy K. Boscardin ◽  
Yoon Soo Park ◽  
Monica M. Cuddy ◽  
Stefanie S. Sebok-Syer
2018 ◽  
Author(s):  
Lorraine Tudor Car ◽  
Bhone Myint Kyaw ◽  
Josip Car

BACKGROUND Digital technology called Virtual Reality (VR) is increasingly employed in health professions’ education. Yet, based on the current evidence, its use is narrowed around a few most applications and disciplines. There is a lack of an overview that would capture the diversity of different VR applications in health professions’ education and inform its use and research. OBJECTIVE This narrative review aims to explore different potential applications of VR in health professions’ education. METHODS The narrative synthesis approach to literature review was used to analyse the existing evidence. RESULTS We outline the role of VR features such as immersion, interactivity and feedback and explain the role of VR devices. Based on the type and scope of educational content VR can represent space, individuals, objects, structures or their combination. Application of VR in medical education encompasses environmental, organ and micro level. Environmental VR focuses on training in relation to health professionals’ environment and human interactions. Organ VR educational content targets primarily human body anatomy; and micro VR microscopic structures at the level of cells, molecules and atoms. We examine how different VR features and health professional education areas match these three VR types. CONCLUSIONS We conclude by highlighting the gaps in the literature and providing suggestions for future research.


Author(s):  
Mario Veen

AbstractThis paper argues that abductive reasoning has a central place in theorizing Health Professions Education. At the root of abduction lies a fundamental debate: How do we connect practice, which is always singular and unique, with theory, which describes the world in terms of rules, generalizations, and universals? While abduction was initially seen as the ‘poor cousin’ of deduction and induction, ultimately it has something important to tell us about the role of imagination and humility in theorizing Health Professions Education. It is that which makes theory possible, because it allows us to ask what might be the case and calls attention to the role of creative leaps in theory. Becoming aware of the abductive reasoning we already perform in our research allows us to take the role of imagination—something rarely associated with theory—seriously.


2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Pamela R. Jeffries ◽  
Reamer L. Bushardt ◽  
Ragan DuBose-Morris ◽  
Colton Hood ◽  
Suzan Kardong-Edgren ◽  
...  

2021 ◽  
Author(s):  
Javeed Sukhera ◽  
Syed Hasan Ahmed

BACKGROUND Teaching and learning about topics such as bias is challenging due to the emotional nature of bias-related discourse. However, emotions can be challenging to study in health professions education for numerous reasons. With the emergence of Machine Learning (ML) and Natural Language Processing (NLP), sentiment analysis (SA) has potential to bridge the gap. OBJECTIVE To improve our understanding of the role of emotions in bias related discourse, we developed and conducted a SA of bias related discourse among health professionals. METHODS We conducted a 2-stage quasi experimental study. First, we developed a SA (algorithm) within an existing archive of interviews with health professionals about bias. SA refers to a mechanism of analysis that evaluates the sentiment of textual data by assigning scores to textual components and calculating and assigning a sentiment value to the text. Next, we applied our SA algorithm to an archive of social media discourse on Twitter that contained equity related hashtags to compare sentiment among health professionals and the general population. RESULTS When tested on the initial archive, our SA algorithm was highly accurate compared to human scoring of sentiment. An analysis of bias-related social media discourse demonstrated that health professionals were less neutral than the general population when discussing social issues on professionally associated accounts, suggesting that health professionals attach more sentiment to their posts on Twitter than seen in the general population. CONCLUSIONS The finding that health professionals are more likely to show and convey emotions regarding equity related issues on social media has implications for teaching and learning about sensitive topics related in health professions education. Such emotions must therefore be considered in the design, delivery, and evaluation of equity and bias related education. CLINICALTRIAL Not applicable


2019 ◽  
Vol 2 (2) ◽  
pp. 9
Author(s):  
Rehan Ahmed Khan

Educationists are professionals who develop and design educational policies and conduct research on different aspects of education. Some of them also teach ‘Education’ as a subject. Education is being more streamlined and accepted as a separate entity in medical education, with more and more doctors opting for courses in medical education such as certificates, diplomas and masters in medical education (Tekian, Roberts, Batty, Cook, & Norcini, 2014). Hence, a discussion often ensues regarding the definition of medical educationists, educators, and teachers. Literature does not discriminate clearly between these three terms. In this editorial, I will share my perspective on these terminologies based on my experience and supportive evidence from the literature. A clinician needs a license to practice, so it is unfair to consider a doctor as a teacher by default, without a license to teach. Hence, to be considered a medical teacher, a prerequisite of obtaining a certificate, diploma, or masters in medical education should be fulfilled. At the least, courses or workshops in different aspects of medical education should be completed by the doctors. Regarding medical education, faculty in medical and dental colleges in Pakistan can be divided into three categories: (1) Doctors with basic medical education (MBBS or BDS) and a postgraduate degree in medical education (e.g. MHPE or MME, etc). These professionals are usually concerned with medical education as a discipline and work in the department of medical education (DME) and can be called ‘Medical Educationists’. (2) Doctors with a post-graduate degrees in their primary discipline (such as Physiology or Surgery etc ) but an additional post-graduate degree in medical education. These professionals teach their primary disciplines but at the same time work actively with DME in a collaborative or leadership role. They can be considered as ‘Medical Educators’. (3) The third type of faculty confines them to teaching their own subjects who can be considered as ‘Medical Teachers’. They either have a license to teach (CHPE, Diploma or Masters) in addition to a postgraduate qualification in their own discipline or have learned the art and craft of teaching through experience and self-training. However, in this day and age when teaching is no more delivery of knowledge (Harden & Crosby, 2000), it is difficult to be a medical teacher without a formal degree and training in teaching. All these professionals define and shape the structure and role of medical education departments or units. In Pakistan, where medical education departments are still in infancy in the majority of the medical schools, it is important to understand how these departments should be run (Batool, Raza, & Khan, 2018; Davis, Karunathilake, & Harden, 2005). Department of medical education may be headed by either a medical educationist or medical educator, but the gist is that they should have a basic degree in medical education. In the author’s experience, it is better to have all three types of professionals in the DME or related to it. Each has its own benefit. The medical educationist is focused on administrative and research areas related to educationists, the medical educator can act as a bridge between DME and other disciplines, and the medical teacher is the brace of DME, ensuring the implementation of the educational program. Successful collaboration between these three types of professionals is important for the effective implementation of the curriculum. The nomenclature of medical educationists, educators, and teachers do not have strict boundaries and are being interchangeably used in practice. It would be interesting to define them empirically and describe the roles and responsibilities for each one of them separately. -------------------------------------------------------------------------- References Batool, S., Raza, M. A., & Khan, R. A. (2018). Roles of medical education department: What are expectations of the faculty? Pakistan Journal of Medical Sciences, 34(4). https://doi. org/10.12669/pjms.344.14609 Davis, M. H., Karunathilake, I., & Harden, R. M. (2005). AMEE Education Guide no. 28: the development and role of departments of medical education. Medical Teacher, 27(8), 665– 675. https://doi.org/10.1080/01421590500398788 Harden, R. M., & Crosby, J. O. Y. (2000). AMEE Guide No 20 : The good teacher is more than a lecturer - the twelve roles of the teacher. Medical Teacher, 22(4), 334–347. https://doi. org/10.1080/014215900409429 Tekian, A., Roberts, T., Batty, H. P., Cook, D. a, & Norcini, J. (2014). Preparing leaders in health professions education. Medical Teacher, 36(3), 269–271. https://doi.org/10.3109/01421 59X.2013.849332


2021 ◽  
Vol 13 ◽  
pp. 175628722110448
Author(s):  
B.M. Zeeshan Hameed ◽  
Gayathri Prerepa ◽  
Vathsala Patil ◽  
Pranav Shekhar ◽  
Syed Zahid Raza ◽  
...  

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.


2016 ◽  
Vol 4 (1) ◽  
pp. 125
Author(s):  
JayashriTamanna Nerali ◽  
LahariA Telang ◽  
Ajay Telang ◽  
PishipatiVinayak Kalyan Chakravarthy

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