Objective Digital early warning tools (DEWTs) use clinical information and models to assist in early detection of clinical deterioration. Just like the ever-changing landscape of best practice guidelines and clinical workflows, DEWTs require active monitoring and intervention to remain current. This study aims to answer the research question “What are the issues experienced by clinicians and users with a Digital Early Warning Tool?”. Methods We studied 15 public hospitals in Australia, pre- and post-DEWT upgrade. The upgrade included changes to graphical views, altered calling criteria (ACC) and recording changes in mental status. We conducted a mixed methods study, including stakeholder focus groups, surveys, and quantitative system use metrics. Results Stakeholder focus groups and surveys identified a lack of awareness of specific functionality, issues with alert fatigue, technical challenges with the system, the need for training and concerns around over-reliance on the system by junior staff. System usage metrics revealed vital sign compliance and graphic-view-page usage increased post-upgrade. The number of chronic ACC reduced significantly post-upgrade; while compliance with early warning score generation increased significantly for acute ACC cases. Changes in mental state documentation reduced post-upgrade. Conclusion DEWTs have potential to improve patient outcomes through enhanced surveillance, timely recognition of deterioration, and improved vital sign monitoring. Ongoing training, monitoring and evaluation post-implementation are key to ensure these systems continue to function as intended. Given the substantial resources invested in DEWTs, research is needed into how health services can incorporate continuous auditing and feedback to ensure clinical effectiveness and safety over time.

Importance:Artificial intelligence (AI) has the potential to improve patient-centered care, yet several AI health care projects have failed due to public backlash, underscoring the importance of social license (informal public acceptance of AI in health care, grounded in trust and expectation of public benefit). How consumers conceptualize social license remains largely unknown. Objectives:To explore social license for AI in health care among consumers and to identify strategies for achieving it. Design, Setting, and Participants:A participatory qualitative study using the comfort board method was conducted across workshops in Queensland, Australia, in September 2025, applying abductive reflexive thematic analysis. Participants were recruited using a mix of convenience and purposive sampling. Eligibility criteria included being aged 18 years or older, residing in Queensland, self-reported receiving care in a Queensland health care facility within the past 24 months, and able to communicate in English. Main Outcomes and Measures:Concept of social license for AI in health care and strategies for achieving it. Results:Thirty-four participants (21 [62%] female) were included, with diverse ages (16 [53%] aged 31-60 years), countries of birth, disability statuses, and AI familiarity. Key themes of social license for AI included relational engagement, structural support, and performance reliability. Performance reliability was critical in short-term care, whereas relational engagement and structural support were more salient in long-term care. According to participants, achieving social license would require AI systems to function as supportive clinical tools, governed by strict stakeholder-driven standards and developed through codesign that aligns them with care needs, contexts, and patient-clinician interactions. Conclusions and Relevance:This qualitative study of consumer perspectives on AI in health care found that social license for AI is a conditional and dynamic construct not a fixed state; structural, performance, and relational factors intersected to shape social license. The findings provide evidence-based recommendations for stakeholders designing and implementing AI in clinical settings, highlighting the need for AI tools designed to support both consumers and clinicians in delivering care that is personalized, empathetic, and responsive to patient needs.

BACKGROUND:Electronic medical records (EMRs) are widely implemented across health settings and function as sociotechnical systems that shape clinical workflows, information use, and patient-clinician interaction. While EMR impacts on clinician experience have been extensively studied, patient experience of EMR-enabled care remains underexplored. This study aims to examine patient experience in an EMR-enabled outpatient clinic and identify actionable recommendations to optimise clinic outcomes. METHODS:A cross-sectional, convergent mixed-methods survey was conducted in a fully digital public diabetes outpatient clinic in Queensland, Australia. Quantitative data, collected using the Patient Experience Monitor (PEM) Adult Outpatient short-form aligned with Picker principles, assessed patient experience across multiple outpatient care domains. Qualitative data, collected through two open-ended items, explored how patients experienced care in the context of clinician-mediated EMR use during consultations and identified opportunities for improvement. Data were collected concurrently and analysed separately. Integration occurred at the reporting stage, where qualitative findings were used to explain and contextualise the quantitative results and to inform practical recommendations. RESULTS:One hundred patients participated. Quantitative findings showed highly favourable but ceiling-affected patient experience ratings across PEM domains. Qualitative analysis identified four themes: perceived facilitation of informational continuity and coordination of care; perceived reduction in personal interaction; limited patient and GP access beyond the public hospital EMR environment; and background trust and neutral perceptions of EMR use. Integration of findings informed a set of actionable recommendations to optimise EMR-supported workflows, preserve interpersonal engagement, strengthen information continuity across care settings, and enable more participatory models of outpatient care. CONCLUSIONS:Patients perceived aspects of EMR-enabled outpatient care as supporting patient-centred care, particularly when clinicians used integrated information effectively during consultations. Findings highlight the importance of implementing EMRs as sociotechnical systems that not only align with consultation workflows but also preserve interpersonal connection and support participatory care. Achieving this requires meaningful information access and sharing across patients, clinicians, and care settings, providing practical guidance for designing digitally enabled outpatient services.

RATIONALE:Connected healthcare delivery ensures that the right healthcare information is exchanged with the right healthcare workers, at the right time. Due to the vast distances and dispersed workforce, achieving connected healthcare across rural and remote settings remains a global challenge. Workforce perspectives may help understand how digital transformation can contribute to consumer-centered healthcare information flow. AIMS AND OBJECTIVES:To answer the research question: how consumer-centered is the healthcare information flow across rural and remote health services? METHOD:A qualitative study was conducted involving semi-structured interviews with staff (n = 57) from rural and remote healthcare systems (n = 6). Transcripts were analyzed to elicit the current state of information exchange between and within health services and associated themes. The findings were evaluated with a subject matter expert and member checking. RESULTS:The mode of healthcare information flow was largely paper-based, however frequent use of hybrid (paper and digital records) and digital Clinical Information Systems (CISs) was evident. Transferability of healthcare information could be improved as some systems lack sufficient communication with CISs in other health service types. Resourcing and workforce capabilities were identified to limit transparency of healthcare information. Provided adequate support was available, the rural and remote healthcare workforce support digital transformation and encourage the expansion of digital CISs. CONCLUSION:Digital transformation and better integration of CISs can enable a healthcare information journey that is consumer-centered, ensuring secure, reliable, and efficient exchange of information. Specific recommendations include the expansion of digital health information exchange applications, enhancing local digital infrastructure support, and building a digitally skilled workforce.

Introduction Healthcare professionals are increasingly expected to use digital health technologies in their clinical practice, despite limited prior education and training. Measuring digital health competence can assist organisations to understand workforce learning needs and tailor digital transformation efforts for maximum impact. This study aims to review the extant literature to answer the questions: what validated assessment tools are available to assess healthcare professionals’ digital competence and what is the quality of evidence for these tools? Methods We used a criteria-led evaluation approach to 1) develop criteria for evaluation, 2) conduct a rapid literature review to identify tools and 3) evaluate tools against key criteria and determine their quality of evidence. We searched PubMed, CINAHL, Google scholar and grey literature up to April 22, 2025. The search strategy included three concepts: ‘questionnaire OR survey’ AND ‘digital competence’ AND ‘healthcare staff’. Reporting followed PRISMA guidelines adapted for the rapid review methodology. Results Twenty-eight publications and grey literature met the inclusion criteria, with 61% published in the last 5 years. Most assessments designed for the healthcare workforce were nursing-specific (n = 9/20, 45%). Psychometric properties were reported for 71% of included instruments with varying quality of evidence. Only two tools met the criteria of being valid and scoped to the interprofessional healthcare workforce. Conclusion Understanding which tools are validated and fit-for-purpose is essential for researchers, educators and health services seeking to measure and improve digital health competence among healthcare professionals. There is a need to expand research into interprofessional measures of healthcare workforce digital competence to support effective workforce transformation.

ABSTRACT:Psychological treatments are increasingly being developed and delivered using platforms such as mobile applications, online modules, virtual reality, and artificial intelligence chatbots. This scoping review aimed to examine how digital psychological interventions deliver outcomes that are valuable not only for patients but also for clinicians and the broader health system. Peer-reviewed studies evaluating digital psychological interventions for adults with chronic primary pain and chronic primary or secondary musculoskeletal pain were included. Seven databases were searched: PubMed, Embase, CINAHL, Web of Science, Scopus, PsycInfo, and Cochrane. Screening was conducted independently by 2 reviewers, with a third reviewer providing consensus in cases of conflict. After screening, 108 articles met the eligibility criteria, reporting on 81 distinct interventions. Outcomes were mapped to the quadruple aim of health care to assess for improvements in population health, patient experiences, clinician experiences, and cost-efficiency. All interventions demonstrated improved health outcomes for people living with pain, with most also assessing patient experiences (n = 65, 80.2%). Few measured clinician experiences (n = 22, 27.2%) or cost-effectiveness (n = 8, 9.9%). Only 2 interventions reported outcomes that addressed all 4 quadrants of the quadruple aim of health care. At the time of review, a third of the interventions (n = 27, 33.3%) were available for use in real-world settings. Overall, current evaluations demonstrated positive impact on population health, patient experience, and access to psychological care. However, limited understanding remains on how clinicians are supported to refer and implement these treatments, as well as the costs of integrating them into routine care.

INTRODUCTION:Surveillance of healthcare associated infections is core to infection prevention and control however traditional methods are manual and resource intensive. Digital solutions may reduce workload and improve responsiveness, but design elements and their impact remain poorly understood. This review aimed to identify clinical and technical design elements used in digital surveillance solutions and associated impacts. METHODS:A systematic search identified original research describing digital healthcare associated infection (HAI) surveillance in hospitals. Studies were included if they incorporated at least one digital element fully developed at time of publication. Data on study characteristics, system features, implementation and outcomes were extracted and a narrative synthesis conducted, with findings mapped to the research questions. RESULTS:Forty-seven studies met the eligibility criteria, most were from Europe (n=28) or the USA (n=10). Design elements were commonly associated with detection, validation, automation and data integration. Digital systems were predominantly internally developed and government funded. HAI outcome reporting was heterogeneous, with limited evaluation of patient impact, implementation or sustainability. System integration into routine clinical practice was rarely described. CONCLUSION:This review identified considerable variation in the clinical and technical design of digital HAI surveillance systems, with limited evidence of integration into clinical practice. While some studies reported improved surveillance efficiency, the absence of patient, quality and safety outcomes means it remains unclear which design approaches deliver the greatest clinical value. Future research should prioritise implementation, clinical integration and patient-centred outcomes to identify digital surveillance designs most likely to improve infection prevention practice and patient care.

A digitally-literate and future-ready healthcare workforce is essential to realise the benefits of digital health and address critical healthcare challenges. Embedding digital health content into health degree curricula is a strategic priority, yet integration is hindered by the absence of standardised, professionally endorsed curriculum and limited educator expertise. Following development of an educator toolkit, this pilot evaluation study aimed to explore: “How do educators and academic leaders perceive the appropriateness, usability, usefulness and feasibility of a toolkit designed to support integration of digital health content into pre-registration health curricula?”. A multi-method study was conducted across 17 Australian universities. Data collection conducted with educators involved structured online survey responses (n = 53) using a semi-structured conversation guide in a meeting and, after updating the toolkit with educator feedback, 60-minute online focus groups were conducted with academic leaders (n = 14). Survey responses underwent content and sentiment analysis with manual coding aligned with a pre-defined coding framework, while focus group data were analysed using content analysis. Educators reported agreement that the toolkit was clear, well-structured, easy to navigate, and potentially effective in increasing their knowledge and confidence. Most statements rated positively for perceived appropriateness, usability, and usefulness. Statements with the highest agreement related to alignment between learning plans and outcomes (98

OBJECTIVES:To evaluate the effect of hospital occupancy levels on inpatient and emergency department (ED) flow rates, ED length of stay (ED) and access block, and identify critical occupancy thresholds above which patient flow deteriorates. DESIGN:Retrospective cohort study using routinely collected administrative data. SETTING:Twenty-five public hospitals in Queensland, Australia, over a 5.5-year period (1 April 2017 to 31 August 2022). MAIN OUTCOME MEASURES:ED presentation and discharge rates, inpatient admission and discharge rates, hospital occupancy levels, length of stay, access block and 4-h rule compliance. RESULTS:The analysis reveals a significant performance shift as hospital occupancy levels increase and identifies site-specific critical 'choke points' where patient flow deteriorates. Notably, as occupancy rises, we observed a growing divergence between ED presentations and discharge rates, and between inpatient admissions and discharges, indicating system congestion. Additionally, when assessing flow across the 25 hospitals, the data demonstrates that a 10% increase in bed occupancy rate correlates with a 0.32-h (19-min) extension in ED length of stay (or 33 min for patients admitted from the ED). Also, significant disparities in hospital operations were observed between weekends and weekdays, with weekday admissions and discharges up to three times higher than weekends, highlighting the increased operational pressure during the work week. CONCLUSIONS:The investigation challenges the traditional 85% occupancy target, demonstrating that optimal occupancy levels vary by hospital. The study also underscores the strong correlation between hospital bed occupancy and ED access performance, with higher hospital occupancy correlating with longer ED stays and decreased adherence to performance indicators. As hospitals approach full capacity, the pressure on ED resources intensifies, resulting in longer wait times and delays in care.

INTRODUCTION:Routinely collected patient reported experience measure (PREM) surveys capture patients' experience across many countries and care settings. Secondary use of open-ended PREM comments is not common practice, however with increasing volume and scope being captured, this should be investigated. We examine how routinely collected open-ended PREM comments are used in research for secondary uses and synthesise considerations. METHODS:Using the JBI approach and PRISMA-ScR guideline, we searched four academic databases for peer-reviewed English-language studies (2010-2024) that used routinely collected open-ended PREM comments to answer questions focused on a specific person, place, time or perspective beyond the broad intent of the original survey. We summarised study characteristics, categories and methods of secondary use and performed a descriptive analysis of study authors' considerations for secondary use of this data. RESULTS:We identified 2,200 unique articles and conducted full-text review of 206 articles, yielding 25 included studies. Studies most frequently used open-ended PREM comments to investigate elements of care (32%, n = 8), time periods (24%, n = 6), examine types of care (20%, n = 5), or treatments (20%, n = 5). Researchers used manual analysis approaches (56%, n = 14), applied sentiment analysis and thematic analysis (each 48%, n = 12). A key strength in the secondary use of open-ended PREM comments is that it reflects what is important to patients' care experiences; while a limitation is the potential bias inherent in survey data (e.g. non-response bias). CONCLUSION:Secondary use of open-ended PREM comments for research is growing, but it is not yet widely used. Using these already collected data for research eliminates the time and cost of additional data collection and incorporates patient voice into research. A formal framework for secondary use of open-ended PREM comments will support incorporating these data into research. Secondary use of PREMs comments could enable more insightful, efficient research and maximise the patient voice in healthcare.

The rapid digital transformation of healthcare demands a workforce equipped with robust digital health capabilities. In response, the Australian Council of Senior Academic Leaders in Digital Health (ACSALDH) developed the Embedding Digital Health Education (EDHE) toolkit to integrate digital health competencies into pre-registration health degrees. This paper outlines the implementation plan for the EDHE toolkit, under four strategic objectives: toolkit adoption, curriculum embedding, teaching capacity enhancement, and continuous improvement through evaluation. Practical resources, consultancy support, and a community of practice underpin these strategies to address challenges including curriculum overload and limited faculty expertise. By embedding digital health education early, the EDHE initiative aims to produce graduates capable of delivering safe, person-centered care in a digitally enabled health system, ensuring adaptability to emerging technologies and sustainable workforce readiness.

OBJECTIVE:To explore the characteristics of ambulance ramping and its association with access block before, during and after the first wave of the coronavirus disease 2019 (COVID-19) pandemic. DESIGN:Retrospective observational study. SETTING:Exploratory data analysis and statistical modelling covering the ambulance-emergency department (ED) interface of the 25 largest public hospitals in Queensland between 1 January 2018 and 31 December 2022. MAIN OUTCOME MEASURES:Primary outcome: The association between ramping, assessed as the ambulance performance target patient off-stretcher time (POST) and access block, and how COVID-19 affected these time-sensitive processes. SECONDARY OUTCOMES:The association between POST and ambulance response time and between ramping and ED length of stay. RESULTS:A significant decline in POST performance was observed across the study period, with the mean difference between pre- and post-COVID-19 periods being 13.1 min (95% CI, 12.9-13.3 min) and 8.9 min (95% CI, 8.7-9.1 min) for Priority 1 and Priority 2 responses, respectively. POST compliance within 30 min dropped from 74% (718,912) pre-COVID-19 to 66% (694,633) during the first wave of COVID-19 and 57% (309,815) post-COVID-19, all below the 90% target. The proportion of patients experiencing access block increased from 10% (91,168) to 17% (87,757) over this same time period. Regression analyses revealed a positive relationship between POST and access block, response time and POST, and ramping and ED length of stay. Before COVID-19, no significant relationship existed between POST and access block for triage category 1 patients, but longer POST was linked to a higher likelihood of access block for categories 2-5. This trend increased across all categories during and post-COVID-19. CONCLUSION:Achieving the POST target of transferring 90% of patients within 30 min is becoming more difficult, with performance declining. The strong association of POST with access block suggests that access block is driving ramping increases. To reduce delays, efforts should focus on improving access to ward beds and managing hospital capacity issues.

Studies on factors associated with emergency department (ED) use, including demographic and contextual factors such as the remoteness of the area of residence and the availability of home support programs, are scarce but may better explain ED demand than studies based only on demographic factors. Furthermore, most studies are based on the nonplausible assumption that there is no geographical variation in factors associated with ED use across states or territories. This study aimed to assess whether contextual factors related to context or the environment, in addition to demographic factors, can improve our understanding of ED use in Queensland, Australia. Spatial analyses were performed to understand the factors more likely to drive ED demand and access block (AB) in different areas of the state. Data on all ED visits in Queensland in 2021 were aggregated by 1-year age groups for each postcode of patients’ residential area and matched to the corresponding denominator population as well as to demographic and contextual factors. When assessing factors associated with ED demand and AB, including contextual factors improved model fit compared with models comprising demographic factors only. The associations between ED visits and age appeared nonlinear. The highest rates of ED visits were observed among residents aged > 75 years, followed by those aged < 5 years and those aged 20–35 years. Across the state, a 1-year increase in patient age was associated with a 2

Background Electronic Medical Records (EMRs) aim to improve efficiency, safety, and quality of care. However, the impact of EMR implementation, particularly in outpatient diabetes care, remains underexplored. This study explored clinicians’ perspectives on EMR use in diabetes outpatient care. Methods This qualitative study, conducted in line with COREQ guidelines, involved four focus groups with 22 clinicians (doctors, nurses, and allied health) at a metropolitan diabetes service in Queensland, Australia. Data were analysed using deductive content analysis, guided by the Quintuple Aim and Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology frameworks. Results Clinicians reported mixed outcomes across the Quintuple Aim domains, shaped by technology adoption constructs. Facilitators such as improved efficiency, access to patient information, and prescribing safety reflected perceived usefulness and positive attitudes, contributing to favourable outcomes across multiple Quintuple Aim. Barriers such as navigation complexity, technical issues, alert fatigue, and overwhelming training led to negative outcomes in EMR use. Tensions around documentation practices and patient expectations of system use, resulted in mixed outcomes. Overall, clinicians viewed EMRs as essential, but sustained adoption required improved usability, tailored training, and better system integration. Conclusion This study concludes that while the EMRs improved safety, efficiency, and access to information, their design and implementation also introduced burdens that negatively affected clinician experience. EMRs significantly shape the healthcare workforce, influencing workflow, wellbeing, and professional engagement. In outpatient diabetes care, specific workflow challenges such as glycaemic data integration highlight that existing EMR designs may not fully support the complexity of chronic disease management. To maximise benefits, EMR initiatives should be approached as quality improvement activities, with role-specific training, reliable infrastructure, and clinician involvement in system optimisation. Future research should address usability challenges, enhance integration, and ensure that both clinician and patient perspectives guide digital health transformation.

OBJECTIVE:To identify evidence-based factors leading to the global challenge of hospital access block and inform strategies to improve emergency access performance. METHODS:A mixed methods approach was followed comprising an umbrella review of published systematic reviews, qualitative analysis of the perspectives of patients and healthcare workers, and quantitative analysis of contextual factors and 6 years of ambulance, emergency inpatient and ward movement records for the 25 largest public hospitals in Queensland, Australia. RESULTS:A key set of findings and recommendations were identified to improve emergency access that are practical and actionable. These comprise the introduction of inpatient discharge metrics and monitoring to shift focus from the front door of hospitals to the 'back door'; increasing support for primary care, community care, aged care, NDIS and vulnerable groups; maintaining demand-side strategies such as increasing inpatient-equivalent care alternatives (e.g., hospital in the home, acute care within nursing home services); investment in prehospital flow; improving hospital processes such as extended-hour discharge lounges; improving workforce; and revising funding policies. CONCLUSIONS:The study findings fill a gap in the evidence regarding challenges and recommendations for improving patient flow within hospital emergency departments and across the broader health system. Focussing efforts at the 'back end' of the inpatient journey is a critical step to improve emergency care outcomes.

Abstract Background Artificial intelligence (AI) is increasingly integrated into healthcare, yet upskilling the health workforce remains a challenge. We addressed the research question: What evidence exists on the effectiveness of AI education and training programs in improving AI literacy among healthcare workers? Methods Following PRISMA guidelines and PROSPERO registration, five databases (PubMed, Scopus, CINAHL, Embase, ERIC) were searched on 20 August 2024, focusing on studies with an intervention of AI training or education for the healthcare workforce, in any study design that reported an evaluation. Results 27 studies were included. Programs improved AI literacy outcomes mapped to levels 1–3 of the Kirkpatrick-Barr training evaluation hierarchy including improved learner reactions, shifts in attitudes and perceptions, enhanced knowledge and skills, and behavior changes. Programs did not map to level 4, where healthcare workers learn to metacognition levels, including organizational change and patient benefit. Programs were short in length (44%), delivered in academic settings (56%), to doctors (44%) or medical students (44%), at entry-to-practice level (56%). Most taught an introduction to AI (67%), with technical AI skills less frequent. Conclusions These programs are a promising start but often lack sufficient depth to build advanced competencies. Improving AI literacy in healthcare will require appropriate course design, an evolving understanding of this rapidly changing area, and evaluating learning effectiveness. As the adoption of AI accelerates across healthcare, health systems may seek to standardise and assess the efficacy of these courses.

Objective: To assess the impact of strategies to improve public hospital emergency access using a detailed ward-level simulation modelling approach. Design and Setting: Discrete event simulation was used to simulate patient flow at three principal referral Australian hospitals from 1 September 2021 to 31 August 2022. Models were developed and validated using every emergency department (ED) presentation, inpatient episode of care and patient ward movement at the study hospitals. Main Outcome Measures: Mean and total ED length of stay, mean waiting time, access block rate, 4-h rule compliance and bed utilisation for patients admitted from the ED. Results: Reducing ED demand via arrangements that accommodate the same proportion and types of admissions from the ED as the existing ED presenting population reduces access block, with larger impacts in winter than in summer. However, reducing 'general practitioner-type patients' in EDs has negligible impact on access block. Tangible impacts on improving patient flow can be achieved by removing maintenance care patients from hospitals (reducing the percentage of access block by up to a third) and reducing elective admissions. Strategies that emphasised morning, midday and early afternoon discharges led to large flow improvements. The strategy already practised by most hospitals of sharing patients among wards greatly improves emergency access, and gains are the same order of magnitude as reducing overall ED demand. Conclusions: The study provides support to policymakers looking for evidence regarding strategies to improve emergency access to public hospital care.

Objective: To map quantitative methods for detecting bias in clinical machine learning (ML) models trained on structured clinical data. Materials and Methods: Following the PRISMA-ScR guidelines, we searched MEDLINE, IEEE Xplore, ACM Digital Library, and Scopus for articles published from 2016 to 21 January 2026, using terms related to bias, fairness, ML, and healthcare contexts. To focus on structured clinical data, we excluded medical imaging, robotics, and natural language processing. We extracted data to characterise bias types, statistical detection methods, and subgroup attributes across the model lifecycle. Results: Fifty-four included studies identified 221 bias detection instances, revealing substantial heterogeneity: 81 author-reported methods using 65 distinct mathematical formulae. Statistical rigour was variable; 45.8% of instances reported confidence intervals and 37.1% reported significance testing, with 37.6% reporting neither. Analyses predominantly focused on race/ethnicity, sex, and age. Most studies restricted bias detection to model evaluation (n= 21, 38.9%), with limited application at earlier stages. Discussion: Substantial methodological variation exists in how bias detection methods are defined and applied. The absence of statistical rigour and standardised approaches creates reliability concerns for clinical applications. Concentrating detection methods at evaluation limits understanding of the origins of bias and constrains mitigation efforts to post-hoc reactive rather than designed-in preventive approaches. Recommendations address metric definitions, threshold reporting, validation justification, uncertainty estimates, attribute/subgroup selection, and clinical implementation. Conclusions: A lack of standarised methodology prevents reliable bias evaluation, hindering safe clinical ML deployment. Consensus-based standards are needed, focusing on clear taxonomies, context-appropriate metrics, and robust statistical validation.