A wellbeing podcast for diagnostic radiography students.

INTRODUCTION: Diagnostic radiography students experience challenges during clinical placements, which have the potential to impact students' emotional wellbeing. This study aimed to explore radiography students' perception of the newly developed podcast series as a wellbeing support tool. METHODS: A mixed methods study was conducted analysing data from listeners, including usage data from the podcast host site, surveys, and focus groups. Usage data was analysed descriptively. A bespoke survey, using a 5-point Likert scale and fixed-response questions was analysed descriptively. Two focus groups consisting of ten participants in total were conducted and data was analysed using thematic analysis. RESULTS: There were 1201 downloads of the 'Breathe-in Radiography Podcast' series across 20 countries and 17 platforms during the study period. A total of 66 complete survey responses demonstrated an overall positive perception of the podcast series. Five main themes were identified from the focus groups: integrated with other activities, accessed when experiencing emotional challenges, relatability to peers, impact on behaviour and mindset, and future podcast content. CONCLUSIONS: This study demonstrated students' positive perceptions of a podcast for support during clinical placement. Further studies are needed to maximise the benefits of podcasting to radiography students and to establish a direct effect of podcasts on student wellbeing.

Framework for a radiography student podcast.

Podcasts refer to episodes of audio content that are readily available on streaming applications on smartphones or computers. This paper reports on the development of the 'Breathe In Radiography Podcast' series for radiography students and provides suggestions for evaluation. Podcast development followed a structured framework, including identification of podcast topics and expert guests, content development, audio recording, episode upload to host site and distribution. Using a framework was useful to guide development and ensure consistency across podcast episodes. Evaluation through podcast usage data, surveys and focus groups provides a comprehensive strategy to explore radiography students' perception of the newly developed podcast series.

Teaching evidence-based practice: Case study of an integrated assessment task for diagnostic radiography students.

INTRODUCTION: A core competency for all health care professionals is evidence-based practice (EBP). An understanding of research skills are key to diagnostic radiographers adopting EBP, and should be taught and assessed in curricula leading to eligibility to register and practice. This paper focuses on the design, implementation and initial evaluation of an assessment task in the Diagnostic Radiography (DR) curriculum at an Australian university, which aimed to facilitate students' skills to identify and interpret research methods and output as a foundation for EBP by combining with EBP and DR theoretical content. METHODS: An integrated assessment task was introduced across two units of study, requiring student groups to produce a single literature review incorporating learning objectives from each distinct unit of study. This approach recognised and incorporated themes of student choice, negotiation of group membership, and scholarly writing as inherent components. RESULTS: Student feedback showed that students valued the integration of content knowledge and research principles across two units of study to better reflect their depth and breadth of learning. Students also commented on the value of team or group work in developing their communication and cooperation skills, which are essential skills in the DR workplace. DISCUSSION: A literature review assessment task integrated across two previously separate units of study provided an innovative approach to assessment of EBP in the DR curricula. CONCLUSION: It is anticipated that DR graduates, who are prepared with knowledge and skills in EBP prior to graduation, will become leaders and drive future innovation in EBP.

Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts.

Occlusion-based saliency maps (OBSMs) are one of the approaches for interpreting decision-making process of an artificial intelligence (AI) system. This study explores the agreement among text responses from a cohort of radiologists to describe diagnostically relevant areas on low-dose CT (LDCT) images. It also explores if radiologists' descriptions of cases misclassified by the AI provide a rationale for ruling out the AI's output. The OBSM indicating the importance of different pixels on the final decision made by an AI were generated for 10 benign cases (3 misclassified by the AI tool as malignant) and 10 malignant cases (2 misclassified by the AI tool as benign). Thirty-six radiologists were asked to use radiological vocabulary, typical to reporting LDCT scans, to describe the mapped regions of interest (ROI). The radiologists' annotations were then grouped by using a clustering-based technique. Topics were extracted from the annotations and for each ROI, a percentage of annotations containing each topic were found. Radiologists annotated 17 and 24 unique ROIs on benign and malignant cases, respectively. Agreement on the main label (e.g., "vessel," "nodule") by radiologists was only seen in only in 12% of all areas (5/41 ROI). Topic analyses identified six descriptors which are commonly associated with a lower malignancy likelihood. Eight common topics related to a higher malignancy likelihood were also determined. Occlusion-based saliency maps were used to explain an AI decision-making process to radiologists, who in turn have provided insight into the level of agreement between the AI's decision and radiological lexicon.

Understanding radiographic decision-making when imaging obese patients: A Think-Aloud study.

INTRODUCTION: The incidence of obesity has been steadily rising over the last few decades and is having a significant impact upon the health system. In radiography, a particular challenge of imaging obese patients is implementing the as low as reasonably achievable (ALARA) principle when determining radiation dose, and technical and patient-care adaptations. This study aimed to better understand the decision-making strategies of experienced radiographers in determining imaging and exposure factor selection in the context of imaging obese patients. METHODS: This study employed a 'think-aloud,' methodology, and eight experienced diagnostic radiographers working in clinical education were recruited to perform routine AP abdominal X-ray projections on an anthropomorphic phantom. They were asked to simultaneously verbalise emerging thoughts as they considered positioning, exposure selection and image evaluation. This process was repeated with three different phantom sizes, each representing an increased BMI from 'healthy,' to, 'morbidly obese.' Audio recordings were transcribed and interpreted via Bowman's (1997) theory of radiographic judgement and decision-making. RESULTS: Analysis of interview transcripts identified 12 key concepts considered by experienced radiographers. Differences in radiographic concepts were considered when imaging phantoms of different sizes was demonstrated. A shift from segmental (e.g. positioning) to more environmental factors (e.g. patient comfort) and an increase in the number of verbal considerations with increasing phantom size were identified. The shift in focus of decision-making stages identified the greater need to consider contextual factors such as patient comfort and repeatability when imaging obese patients. CONCLUSION: Experienced radiographers find imaging obese patients challenging and alter their perception of image quality to accommodate for patient presentation. The findings will help inform future research, practice guidelines and learning resources to provide optimal imaging and care for obese patients, especially for student education.

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person's home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.

Optimizing Projectional Radiographic Imaging of the Abdomen of Obese Patients: An e-Delphi Study.

PURPOSE: Obesity is increasing in prevalence globally, with increased demands placed on radiology departments to image obese patients to assist with diagnosis and management. The aim of this study was to determine perceived best practice techniques currently used in clinical practice for projectional radiography of the abdomen for obese patients with the aim to help elucidate areas for future research and education needs in this field. EXPERIMENTAL DESIGN: A two round e-Delphi study was undertaken to establish a consensus within a reference group of expert Australian clinical educator diagnostic radiographers (CEDRs). Initially, a conceptual map of issues regarding imaging obese patients was undertaken by analysing interview transcripts of 12 CEDRs. This informed an online questionnaire design used in Delphi rounds 1 and 2. A consensus threshold was set <75% "agreement/disagreement", with 15 and 14 CEDRs participating in rounds 1 and 2, respectively. RESULTS: Seven of the 11 statements reach consensus after round 2. Consensus on using a combination of higher peak kilovoltage (kVp) and milliampere-seconds (mAs) to increase radiation exposure increased source-to-image distance and tighter collimation was achieved. There was no consensus regarding patient positioning practices or patient communication strategies. The expert group reported the importance of personal confidence and treating patients as individuals when applying techniques. CONCLUSION: Diversity of experts' opinions and current practice may be due to the variations in obese patients' size and presentation. Therefore, there is a need for extensive empirical evidence to underpin practice and education resources for radiographers when imaging obese patients.

Implementing a working together model for Aboriginal patients with acute coronary syndrome: an Aboriginal Hospital Liaison Officer and a specialist cardiac nurse working together to improve hospital care.

Acute coronary syndrome (ACS) contributes to the disparity in life expectancy between Aboriginal and non-Aboriginal Australians. Improving hospital care for Aboriginal patients has been identified as a means of addressing this disparity. This project developed and implemented a working together model of care, comprising an Aboriginal Hospital Liaison Officer and a specialist cardiac nurse, providing care coordination specifically directed at improving attendance at cardiac rehabilitation services for Aboriginal Australians in a large metropolitan hospital in Melbourne. A quality improvement framework using a retrospective case notes audit evaluated Aboriginal patients' admissions to hospital and identified low attendance rates at cardiac rehabilitation services. A working together model of care coordination by an Aboriginal Hospital Liaison Officer and a specialist cardiac nurse was implemented to improve cardiac rehabilitation attendance in Aboriginal patients admitted with ACS to the cardiac wards of the hospital. A retrospective medical records audit showed that there were 68 Aboriginal patients admitted to the cardiac wards with ACS from 1 July 2008 to 30 June 2011. A referral to cardiac rehabilitation was recorded for 42% of these. During the implementation of the model of care, 13 of 15 patients (86%) received a referral to cardiac rehabilitation and eight of the 13 (62%) attended. Implementation of the working together model demonstrated improved referral to and attendance at cardiac rehabilitation services, thereby, has potential to prevent complications and mortality. WHAT IS KNOWN ABOUT THE TOPIC?: Aboriginal Australians experience disparities in access to recommended care for acute coronary syndrome. This may contribute to the life expectancy gap between Aboriginal and non-Aboriginal Australians. WHAT DOES THIS PAPER ADD?: This paper describes a model of care involving an Aboriginal Hospital Liaisons Officer and a specialist cardiac nurse working together to improve hospital care and attendance at cardiac rehabilitation services for Aboriginal Australians with acute coronary syndrome. WHAT ARE THE IMPLICATIONS FOR PRACTITIONERS?: The working together model of care could be implemented across mainstream health services where Aboriginal people attend for specialist care.