How do geriatricians practise comprehensive geriatric assessment in the outpatient setting: an analysis of geriatricians' letters and a comparison to the Medicare benefits schedule requirement.

BACKGROUND: Little is known about what components geriatricians routinely incorporate into outpatient comprehensive geriatric assessments (CGAs). AIMS: This study explored what components of CGAs are routinely incorporated into geriatricians' letters and assessed their consistency with the Medicare Benefits Schedule (MBS) and a recently published survey of geriatricians. METHODS: We completed a manual content analysis, supplemented by qualitative thematic analysis, of 34 letters from five geriatricians, collected as part of the GOAL Trial. RESULTS: While more than 80% of letters included each of the key clinical domains described in the Medicare Benefits Schedule and survey of geriatricians, only 62% included advanced care planning and 47% mentioned immunisations. Forty-seven percent of letters included goal setting. Few letters showed evidence of multidisciplinary working. Issues identified by the geriatrician centred around the themes of advance care planning, symptom identification and management, medical comorbidities, strategies to support quality of life and interventions to manage frailty. Patient concerns identified in the letters were cognition and mood, declining function, future planning and symptom management. CONCLUSIONS: Analysis of geriatricians' letters provides important and novel insights into usual CGA practice. The letters provide evidence of multidimensional assessments of physical, functional, social and psychological health, and most include use of standardised tools. However, less than 50% include evidence of goal setting or multidisciplinary working. The results allow consideration of how CGAs might be carried out in the outpatient setting, so that interventions focused on improving the quality and efficacy of this intervention can be implemented.

Targeted Screening for Cancer: Learnings and Applicability to Melanoma: A Scoping Review.

This scoping review aims to systematically gather evidence from personalized cancer-screening studies across various cancers, summarize key components and outcomes, and provide implications for a future personalized melanoma-screening strategy. Peer-reviewed articles and clinical trial databases were searched for, with restrictions on language and publication date. Sixteen distinct studies were identified and included in this review. The studies' results were synthesized according to key components, including risk assessment, risk thresholds, screening pathways, and primary outcomes of interest. Studies most frequently reported about breast cancers (n = 7), followed by colorectal (n = 5), prostate (n = 2), lung (n = 1), and ovarian cancers (n = 1). The identified screening programs were evaluated predominately in Europe (n = 6) and North America (n = 4). The studies employed multiple different risk assessment tools, screening schedules, and outcome measurements, with few consistent approaches identified across the studies. The benefit-harm assessment of each proposed personalized screening program indicated that the majority were feasible and effective. The establishment of a personalized screening program is complex, but results of the reviewed studies indicate that it is feasible, can improve participation rates, and screening outcomes. While the review primarily examines screening programs for cancers other than melanoma, the insights can be used to inform the development of a personalized melanoma screening strategy.

Implementation of Patient-Reported Outcomes in a Medical Oncology Setting (the iPROMOS Study): Type II Hybrid Implementation Study.

BACKGROUND: Clinical trials have demonstrated that patient-reported outcome measures (PROMs) can improve mortality and morbidity outcomes when used in clinical practice. OBJECTIVE: This study aimed to prospectively investigate the implementation of PROMs in routine oncology. Outcomes measured included improved symptom detection, clinical response to symptom information, and health service outcomes. METHODS: Two of 12 eligible clinics were randomized to implement symptom PROMs in a medical oncology outpatient department in Australia. Randomization was carried out at the clinic level. Patients in control clinics continued with usual care; those in intervention clinics completed a symptom PROM at presentation. This was a pilot study investigating symptom detection, using binary logistic models, and clinical response to PROMs investigated using multiple regression models. RESULTS: A total of 461 patient encounters were included, consisting of 242 encounters in the control and 222 in the intervention condition. Patients in these clinics most commonly had head and neck, lung, prostate, breast, or colorectal cancer and were seen in the clinic for surveillance and oral or systemic treatments for curative, metastatic, or palliative cancer care pathways. Compared with control encounters, the proportion of symptoms detected increased in intervention encounters (odds ratio 1.05, 95% CI 0.99-1.11; P=.08). The odds of receiving supportive care, demonstrated by nonroutine allied health review, increased in the intervention compared with control encounters (odds ratio 3.54, 95% CI 1.26-9.90; P=.02). CONCLUSIONS: Implementation of PROMs in routine care did not significantly improve symptom detection but increased the likelihood of nonroutine allied health reviews for supportive care. Larger studies are needed to investigate health service outcomes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000398202; https://tinyurl.com/3cxbemy4.

Protocol for the process evaluation of the GOAL trial: investigating how comprehensive geriatric assessment (CGA) improves patient-centred goal attainment in older adults with chronic kidney disease in the outpatient setting.

INTRODUCTION: The GOAL Cluster Randomised Controlled Trial (NCT04538157) is now underway, investigating the impact of comprehensive geriatric assessment (CGA) for frail older people with chronic kidney disease (CKD). The primary outcome is the attainment of patient-identified goals at 3 months, assessed using the goal attainment scaling process. The protocol requires a dedicated process evaluation that will occur alongside the main trial, to investigate issues of implementation, mechanisms of impact and contextual factors that may influence intervention success. This process evaluation will offer novel insights into how and why CGA might be beneficial for frail older adults with CKD and provide guidance when considering how to implement this complex intervention into clinical practice. METHODS AND ANALYSIS: This process evaluation protocol follows guidance from the Medical Research Council and published guidance specific for the evaluation of cluster-randomised trials. A mixed methodological approach will be taken using data collected as part of the main trial and data collected specifically for the process evaluation. Recruitment and process data will include site feasibility surveys, screening logs and site issues registers from all sites, and minutes of meetings with intervention and control sites. Redacted CGA letters will be analysed both descriptively and qualitatively. Approximately 60 semistructured interviews will be analysed with a qualitative approach using a reflexive thematic analysis, with both inductive and deductive approaches underpinned by an interpretivist perspective. Qualitative analyses will be reported according to the Consolidated criteria for Reporting Qualitative research guidelines. The Standards for Quality Improvement Reporting Excellence guidelines will also be followed. ETHICS AND DISSEMINATION: Ethics approval has been granted through Metro South Human Research Ethics Committee (HREC/2020/QMS/62883). Dissemination will occur through peer-reviewed journals and feedback to trial participants will be facilitated through the central coordinating centre. TRIAL REGISTRATION NUMBER: NCT04538157.

The impact of surgical complications on obstetricians' and gynecologists' well-being and coping mechanisms as second victims.

BACKGROUND: Obstetrics and gynecology surgery is becoming increasingly complex because of an aging population with increasing rates of medical comorbidities and obesity. Complications are therefore common, and not only impact the patient but can also cause distress to the obstetrics and gynecology surgeon as a "second victim." OBJECTIVE: This study aimed to describe and quantify the range of effects of complications on obstetrics and gynecology surgeons, and assess sociodemographic, clinician, and practice factors associated with such impact. STUDY DESIGN: A cross-sectional survey was developed on the basis of interviews with obstetrics and gynecology surgeons and a review of the literature. The survey assessed obstetrics and gynecology surgeons' demographic, clinical, and practice characteristics; estimated the number of complications per year and the impact of complications on distress, physical and mental health, sleep, and relationships; and explored strategies that obstetrics and gynecology surgeons used to cope with complications. Univariate logistic regression analyses were used to determine the association between obstetrics and gynecology surgeons' characteristics and complication consequences. RESULTS: Overall, of 727 survey respondents, 431 (61%) were female, 384 (55%) were aged ≥50 years, almost half had worked as obstetrics and gynecology surgeons for ≥15 years (329 [45%]), and 527 (73%) reported completing <10 surgical procedures per week. Most (568 [78%]) reported <3 surgical complications per year, and most (472 [66%]) thought this was similar or less frequent compared with their colleagues. Complications caused most stress when they resulted in poor patient outcomes (653 [90%]), had severe patient consequences (630 [87%]), or were a result of surgeon error (627 [86%]). Complications impacted most obstetrics and gynecology surgeons' well-being and sleep. A greater proportion of those aged <50 years reported that their mental well-being (32 [10%]; P=.002) and sleep (130 [42%]; P=.03) were affected when a complication occurred. Female participants were also more likely to report that their physical health (14 [3%]; P≤.001), mental health (39 [9%]; P=.01), and sleep (183 [43%]; P≤.001) were affected. Current trainees (11 [10%]) and surgeons with <15 years of experience (25 [9%]) were more likely to experience mental well-being consequences compared with surgeons with ≥15 years of experience (12 [4%]; P=.01). Female participants reported less willingness to interact with colleagues when complications occurred (323 [75%]; P=.006), and surgeons with <15 years of training were less likely to report comfort in talking (221 [74%]; P=.03) and interacting with others (212 [74%]; P=.02). CONCLUSION: The vast majority of obstetrics and gynecology surgeons experience a major impact on their health and well-being when one of their patients develops a complication. The degree and type of impact reported are similar to those observed in other surgical specialties. Future studies are needed to test interventions that alleviate the substantial impact and to follow obstetrics and gynecology surgeons longitudinally to understand the duration of the impact of complications.

Prompt-driven Latent Domain Generalization for Medical Image Classification.

Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifact bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Domain generalization (DG) methods, which aim to train models on multiple domains to perform well on unseen domains, offer a promising direction to solve the problem. However, existing DG methods assume domain labels of each image are available and accurate, which is typically feasible for only a limited number of medical datasets. To address these challenges, we propose a unified DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG). PLDG consists of unsupervised domain discovery and prompt learning. This framework first discovers pseudo domain labels by clustering the bias-associated style features, then leverages collaborative domain prompts to guide a Vision Transformer to learn knowledge from discovered diverse domains. To facilitate cross-domain knowledge learning between different prompts, we introduce a domain prompt generator that enables knowledge sharing between domain prompts and a shared prompt. A domain mixup strategy is additionally employed for more flexible decision margins and mitigates the risk of incorrect domain assignments. Extensive experiments on three medical image classification tasks and one debiasing task demonstrate that our method can achieve comparable or even superior performance than conventional DG algorithms without relying on domain labels. Our code is publicly available at https://github.com/SiyuanYan1/PLDG/tree/main.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

Using the Behaviour Change Wheel to identify barriers and targeted strategies to improve adherence in randomised clinical trials: The example of MEL-SELF trial of patient-led surveillance for melanoma.

BACKGROUND: Adherence to self-management interventions is critical in both clinical settings and trials to ensure maximal effectiveness. This study reports how the Behaviour Change Wheel may be used to assess barriers to self-management behaviours and develop strategies to maximise adherence in a trial setting (the MEL-SELF trial of patient-led melanoma surveillance). METHODS: The Behaviour Change Wheel was applied by (i) using the Capability, Opportunity, Motivation-Behaviour (COMB) model informed by empirical and review data to identify adherence barriers, (ii) mapping identified barriers to corresponding intervention functions, and (iii) identifying appropriate behaviour change techniques and developing potential solutions using the APEASE (Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects and safety, Equity) criteria. RESULTS: The target adherence behaviour was defined as conducting a thorough skin self-examination and submitting images for teledermatology review. Key barriers identified included: non-engaged skin check partners, inadequate planning, time constraints, low self-efficacy, and technological difficulties. Participants' motivation was positively influenced by perceived health benefits and negatively impacted by emotional states such as anxiety and depression. We identified the following feasible interventions to support adherence: education, training, environmental restructuring, enablement, persuasion, and incentivisation. Proposed solutions included action planning, calendar scheduling, alternative dermatoscopes, optimised communication, educational resources in various formats to boost self-efficacy and motivation and optimised reminders (which will be evaluated in a Study Within A Trial (SWAT)). CONCLUSION: The Behaviour Change Wheel may be used to improve adherence in clinical trials by identifying barriers to self-management behaviours and guiding development of targeted strategies.

Balancing the risks and benefits of sun exposure: A revised position statement for Australian adults.

OBJECTIVE: To describe the development of a new position statement regarding balancing the risks and benefits of sun exposure for Australian adults. METHODS: We conducted a Sun Exposure Summit in March 2021, with presentations from invited experts and a workshop including representation from academic, clinical, policy, and patient stakeholder organisations. The group considered advice about balancing the risks and benefits of sun exposure for Australian adults and developed a revised consensus position statement. RESULTS: The balance of risks and benefits of sun exposure is not the same for everybody. For people at very high risk of skin cancer, the risks of exposure likely outweigh the benefits; sun protection is essential. Conversely, people with deeply pigmented skin are at low risk of skin cancer but at high risk of vitamin D deficiency; routine sun protection is not recommended. For those at intermediate risk of skin cancer, sun protection remains a priority, but individuals may obtain sufficient sun exposure to maintain adequate vitamin D status. CONCLUSIONS: The new position statement provides sun exposure advice that explicitly recognises the differing needs of Australia's diverse population. IMPLICATIONS FOR PUBLIC HEALTH: Mass communication campaigns should retain the focus on skin cancer prevention. The new position statement will support the delivery of personalised advice.

Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement.

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. METHODS: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. RESULTS: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. CONCLUSIONS: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.

Working Together to Improve Type 2 Diabetes Care: A Participatory Design Project to Address Identified Needs of People With Diabetes and Their Health-care Professionals.

OBJECTIVES: Diabetes care in Australia is often fragmented and provider-centred, resulting in suboptimal care. Innovative solutions are needed to bridge the evidence-practice gap, and technology can facilitate the redesign of type 2 diabetes care. We used participatory design to increase the chances of fulfilling stakeholders' needs. Using this method, we explored solutions aimed at redesigning diabetes care, focussing on the previously identified needs. METHODS: The participatory design project was guided by stakeholders' contributions. Stakeholders of this project included people with type 2 diabetes, health-care professionals, technology developers, and researchers. Information uncovered at each step influenced the next: 1) identification of needs, 2) generation of solutions, and 3) testing of solutions. Here, we present steps 2 and 3. In step 2, we presented previously identified issues and elicited creative solutions. In step 3, we obtained stakeholders' feedback on the solutions from step 2, presented as care pathways. RESULTS: Suggested solutions included a multidisciplinary wellness centre, a mobile app, increased access to education, improved care coordination, increased support for general practitioners, and a better funding model. The revised care pathways featured accessible community resources, a tailored self-management and educational app, a care coordinator, a digital dashboard, and specialized support for primary care to deal with complex cases. CONCLUSIONS: Using a participatory design, we successfully identified multiple innovative solutions with the potential to improve person-centred and integrated type 2 diabetes care in Australia. These solutions will inform the implementation and evaluation of a redesigned care model by our team.

Pre-post feasibility trial of a telephone-delivered exercise intervention for patients during chemotherapy for recurrent ovarian cancer: the ECHO-R trial protocol.

INTRODUCTION: The benefits of exercise in reducing treatment-related morbidity and improving quality of life following a primary diagnosis of cancer have been well documented and have led to exercise being recommended by oncology societies for all people with a cancer diagnosis. However, these recommendations are derived from research typically involving cohorts with more common cancers and relatively good prognosis, such as breast and prostate. Evidence from these cancers may not apply to women with recurrent ovarian cancer. Therefore, the primary objective of this trial is to evaluate the feasibility and safety of a home-based, telephone-delivered exercise intervention for women undergoing chemotherapy for recurrent ovarian cancer. METHODS AND ANALYSIS: The Exercise During Chemotherapy for Recurrent Ovarian Cancer (ECHO-R) trial is a single-arm, phase II, pre/postintervention trial of a 6-month, telephone-delivered exercise intervention (consistent with recommended exercise oncology prescription). The target sample size is 80 women who are currently undergoing (or are scheduled to receive) chemotherapy for recurrent ovarian cancer. Recruitment is through participating hospital sites in Queensland, Australia, or via self-referral. The exercise intervention comprises 12 telephone sessions over a 6-month period delivered by trial-trained exercise professionals and supplemented (where feasible) by five sessions face to face. Exercise prescription is individualised and works towards an overall goal of achieving a weekly target of 150 min of moderate-intensity, mixed-mode exercise. Assessments via self-administered survey and physical fitness and function tests occur at baseline and then at 6 and 9 months postbaseline. Data to inform feasibility and safety are recorded as case notes by the exercise professional during each session. ETHICS AND DISSEMINATION: Ethics approval for the ECHO-R trial was granted by the Metro North Human Research Ethics Committee (HREC/2020/QRBW/67223) on 6 November 2020. Findings from the trial are planned to be disseminated via peer-reviewed publications and both national and international exercise and oncology conferences. TRIAL REGISTRATION NUMBER: ACTRN12621000042842.

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography.

Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.