ABSTRACT Objective To analyze the barriers, enablers, and strategies affecting the implementation of learning health systems (LHS) projects across individual (micro), organizational (meso), and system (macro) levels within Australian healthcare settings Methods Semistructured interviews were conducted with 26 clinicians undertaking LHS projects in their organizations, as part of a LHS Academy fellowship program. Interviews were completed at two discrete time points: early in project implementation and again at project completion. They explored fellows' experiences with implementing LHS projects in real‐world organizational contexts. Data were analyzed using a hybrid deductive‐inductive approach guided by a multilevel ecological codebook developed by Shaw et al. Results Participants' early interviews highlighted limited confidence, emerging leadership skills, and uncertainty in navigating implementation challenges, while later interviews described greater confidence, stronger leadership capability, and more refined adaptive strategies at the micro level. Organizational influences were described at both time points as slow and uneven, though later interviews included examples of small cultural shifts and early collaborative efforts alongside persistent constraints such as limited resources, siloed cultures, and inadequate digital infrastructure. At the system level, barriers including regulatory complexity, policy misalignment, and limited funding were reported consistently across both interviews and were perceived as largely unchanged. Conclusions Building individual capability was necessary but insufficient for successful LHS project implementation. Alignment across micro‐, meso‐, and macrolevels is critical, requiring parallel investment in organizational readiness, digital infrastructure, and policy reform. Fellowship programs may support individual change and foster proof‐of‐concept projects, but scaling impact demands coordinated, multilevel strategies.

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.

There is limited understanding of health science students’ metacognitive awareness of teamwork when entering tertiary education. This study aims to assess self-reported teamwork skills in first-year Undergraduate and first-year Masters of Pharmaceutical Science students. The Teamwork Baseline Assessment Tool (TBAT) survey was used to measure growth mindsets, teamwork strategies and teamwork aptitudes. Students completed the survey at the beginning of semester 1. Likert scale data underwent statistical analysis using the Mann–Whitney test and Spearman correlation analysis, and open-ended written responses underwent reflexive thematic analysis. The first-year Undergraduate students scored higher in growth mindset about kind of person compared to Masters students (Mann–Whitney test, p = 0.0038, U = 6366). First-year Masters students scored higher in the ‘hungry’ virtue than Undergraduate students (Mann–Whitney test, p = 0.0146, U = 6650; Alpha = 0.05). These findings suggest differences in how students entering undergraduate and postgraduate programs conceptualise teamwork readiness, with undergraduate students demonstrating stronger beliefs about personal development, while Masters students exhibit greater motivation to contribute and engage within teams. Thematic analysis yielded 18 inductive codes grouped into themes: ‘positive interdependence and support of scenario student’, ‘exclusion of scenario student’ and ‘considering remaining team members’. These themes highlight variation in students’ metacognitive awareness of inclusive teamwork practices, with responses reflecting both collaborative support strategies and exclusionary approaches to managing team challenges. Together, these findings suggest cohort-level differences in how students perceive their capacity for growth and motivation to contribute within teams. Such differences highlight the importance of designing teamwork education that is responsive to the developmental needs of different student cohorts. The Teamwork Baseline Assessment Tool (TBAT) may serve as a useful diagnostic instrument to inform the design of targeted teamwork learning activities within health science curricula.

Objective Fast Healthcare Interoperability Resources (FHIR) has become a critical enabler of modern digital health policy, underpinning national reforms relating to electronic health records, data exchange, and real-time clinical decision support. However, despite its central role in digital transformation, FHIR implementation often falters because national workforces lack the capabilities required to operationalise interoperability mandates. This paper presents a policy-aligned methodological framework for conducting a national workforce needs assessment to inform interdisciplinary FHIR training programs. The framework is designed to support digital health policy implementation by systematically identifying capability gaps and guiding evidence-based training investment. Methods We propose a two-step needs assessment framework integrating general and targeted assessments. The general assessment identifies broad training needs through qualitative interviews, surveys, and market scans, while the targeted assessment refines course offerings based on participant demand, workforce capacity, and contextual factors. A mixed-methods approach combines qualitative thematic analysis and descriptive statistics to triangulate findings. A structured five-component decision-making framework is presented to support transparent training prioritisation aligned with national digital health strategy. Key features This framework is designed to support evidence-based, adaptable training programs aligned with national digital health priorities. This framework addresses identified gaps in existing methodological approaches and is intended to offer advantages over single-phase assessment methods. Conclusion This paper presents a theory-informed methodological framework for designing FHIR training programs, aimed at addressing evolving workforce needs and supporting digital health interoperability globally.

BACKGROUND:Digital Coordination Centres (DCCs) represent an innovative approach in hospital settings, designed to enhance patient flow, operational efficiency, and real-time decision-making. While their potential is widely recognised, there is limited understanding of the factors influencing their implementation. This study evaluated the implementation of a DCC in a large Australian hospital, with a focus on identifying enablers, barriers, and strategies for improvement. METHODS:A process evaluation was conducted during Phase 1 of the DCC's implementation. Forty-two semi-structured interviews were undertaken with staff and stakeholders involved in, or affected by, the DCC. Thematic analysis was guided by the Consolidated Framework for Implementation Research (CFIR), to identify key influences on implementation and to identify strategies for ongoing improvement and future scalability. RESULTS:Key enablers included strong leadership, system adaptability, and improved communication across services. Barriers involved data accuracy, system integration, and initial staff resistance -particularly around role clarity and perceived surveillance, which participants suggested could be addressed through enhanced training, role refinement, and strengthened feedback mechanisms. The CFIR provided a useful lens for structuring analysis but required adaptation to address overlapping constructs and digital-specific barriers. CONCLUSIONS:This study offers practical insights into the implementation of a hospital-based DCC and demonstrates the value and challenges of using CFIR to evaluate complex digital health innovations. Findings highlight the importance of adaptable design, sustained leadership, continuous evaluation, and stakeholder-driven refinement. These insights can guide the successful implementation and scaling of digital coordination solutions in similarly complex healthcare environments.

INTRODUCTION:This protocol outlines a mixed methods study evaluating a new Digital Coordination Centre (DCC) at the Royal Melbourne Hospital (RMH), Melbourne, Australia. While coordination centres show potential for impact, evidence on effective implementation in the Australian context remains scarce. This study aims to address this gap. METHODS AND ANALYSIS:The evaluation involves a two-stage approach: a process evaluation to clarify DCC design and identify implementation factors, and an initial outcome evaluation to assess short and medium term outcomes. A developmental approach will support continuous improvement, and implementation science theories applied to unpack change processes. Data sources will include interviews, project documentation and observations, with qualitative and quantitative analyses targeting metrics like emergency department boarding and length of stay. ETHICS AND DISSEMINATION:This study has been approved by the RMH Human Research Ethics Committee (QA2023089). Findings will be shared through peer-reviewed publications and conference presentations.

Research and evaluation skills are essential in healthcare education. Instructors frequently employ collaborative learning models to teach these competencies; however, delivering timely and personalized feedback to multiple groups can be a significant challenge. This study aimed to investigate the potential of generative artificial intelligence (GenAI) as a tool for providing feedback on students’ research ideas. We employed GenAI tools to provide personalised formative feedback during a small-group activity focused on helping students formulate research plans. The activity was implemented within two university courses designed for working health professionals. Students were grouped into groups of 5–7 students and provided clear instructions for how to prompt the GenAI for feedback on their research ideas. Participants completed an evaluation survey at the end of the activity that assessed frequency of use, perceived value, utility, and overall satisfaction. Half of the participants (n = 64, 85.3

Background:Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia. Objective:This study aimed to explore participants' experiences and perspectives of participating in an interprofessional education program over 13 weeks. The evaluation also aimed to assess the benefits, barriers, and opportunities for improvements and identify future applications of the course materials. Methods:We developed a wholly online short course open to interdisciplinary professionals working in digital health in the health care sector. In a flipped classroom model, participants (n=400) undertook 2 hours of preclass learning online and then attended 2.5 hours of live synchronous learning in interactive weekly Zoom workshops for 13 weeks. Throughout the course, they collaborated in small, simulated learning communities (n=5 to 8), engaging in various activities and problem-solving exercises, contributing their unique perspectives and diverse expertise. The course covered a number of topics including background on LHS, establishing learning communities, the design thinking process, data preparation and machine learning analysis, process modeling, clinical decision support, remote patient monitoring, evaluation, implementation, and digital transformation. To evaluate the purpose of the program, we undertook a mixed methods evaluation consisting of pre- and postsurveys rating scales for usefulness, engagement, value, and applicability for various aspects of the course. Participants also completed identical measures of self-efficacy before and after (n=200), with scales mapped to specific skills and tasks that should have been achievable following each of the topics covered. Further, they undertook voluntary weekly surveys to provide feedback on which aspects to continue and recommendations for improvements, via free-text responses. Results:From the evaluation, it was evident that participants found the teaching model engaging, useful, valuable, and applicable to their work. In the self-efficacy component, we observed a significant increase (P<.001) in perceived confidence for all topics, when comparing pre- and postcourse ratings. Overall, it was evident that the program gave participants a framework to organize their knowledge and a common understanding and shared language to converse with other disciplines, changed the way they perceived their role and the possibilities of data and technologies, and provided a toolkit through the LHS framework that they could apply in their workplaces. Conclusions:We present a program to educate the health workforce on integrating the LHS model into standard practice. Interprofessional collaborative learning was a major component of the value of the program. This evaluation shed light on the multifaceted challenges and expectations of individuals embarking on a digital health program. Understanding the barriers and facilitators of the audience is crucial for creating an inclusive and supportive learning environment. Addressing these challenges will not only enhance participant engagement but also contribute to the overall success of the program and, by extension, the broader integration of digital health solutions into health care practice and, ultimately, patient outcomes.

Future health professionals, including dentists, must critically engage with digital health technologies to enhance patient care. While digital health is increasingly being integrated into the curricula of health professions, its interpretation varies widely depending on the discipline, health care setting, and local factors. This viewpoint proposes a structured set of domains to guide the designing of a digital health curriculum tailored to the unique needs of dentistry in Australia. The paper aims to share a premise for curriculum development that aligns with the current evidence and the national digital health strategy, serving as a foundation for further discussion and implementation in dental programs.

Global classrooms transcend geographical boundaries, fostering collaboration and knowledge exchange among learners worldwide. Artificial intelligence (AI) has the potential to enhance patient care, streamline processes, and revolutionise healthcare capabilities. However, there is a shortage of a skilled workforce capable of utilising insights to design innovative solutions. Therefore, tailored education that leverages collaborative learning, knowledge and experience sharing amongst international colleagues, may be effective in progressing digital transformation efforts. This study explores learners’ experiences and perspectives from a 4-week online global classroom education programme between University of Melbourne, Australia and Manchester University, UK, designed to develop the AI skills of healthcare professionals. The evaluation aimed to assess the benefits, barriers, and opportunities for improving international interprofessional collaborative learning, as well as providing insights for educators. We developed a fully online short course for interprofessional healthcare professionals. In a flipped classroom model, learners (N = 21) completed 2 h of pre-class online learning followed by 2 h of live interactive weekly Zoom workshops for 4 weeks. Throughout the course, learners engaged in small group work, contributed their unique expertise, listened to experts in the field, and received feedback on their project pitches from an expert panel. To evaluate the programme’s utility, a mixed methods approach was used, including pre- and post-surveys with rating scales. Learners also completed self-efficacy measures (N = 18), with scales mapped to specific capability statements. Weekly surveys with free-text responses provided additional feedback on course continuity and suggested improvements. The self-efficacy component revealed a significant increase (P <.0001) in perceived confidence across all capability statements from pre- to post-course. The programme effectively provided learners with access to global perspectives from instructors, expert panels, and diverse participant experiences, offering a solid foundation to develop and refine project ideas. The final pitchathon was useful in applying the learnings. Learners reported intentions to apply knowledge to improve service delivery, develop predictive models, and collaborate with data scientists. Key recommendations include tailoring more specific, personalised learning pathways, including additional case studies, providing opportunities or deeper peer and expert interactions and fostering post-course community building. The global classroom facilitates learning and problem-sharing among healthcare professionals, promoting broader thinking, encouraging collaboration across diverse perspectives, and enabling a better understanding of the initiatives colleagues worldwide are undertaking. To enhance this experience, further efforts should focus on enabling more meaningful collaboration among learners during and after the course, to develop global communities of practice.

AIM:To review the literature on generative artificial intelligence for teaching and assessment in health professions education BACKGROUND: Advancements in generative artificial intelligence (GenAI), such as ChatGPT, offer new possibilities for health professions education. These technologies offer potential benefits in teaching and assessment, including personalised learning and automated resource generation. Despite its potential, concerns about accuracy, ethics and reliability remain. This scoping review examines GenAI's implementation, benefits and challenges in teaching and assessment across health professions education. DESIGN:Scoping review. METHODS:Following Arksey and O'Malley's five-stage framework, with refinements based on the Joanna Briggs Institute (JBI) methodology, Medline, CINAHL and Web of Science Core Collection were searched for peer-reviewed studies published between January 2019 and June 2024. Studies were screened independently by two reviewers and data extraction performed systematically to ensure consistency. RESULTS:Studies (n = 5826) were assessed for eligibility, with 23 meeting the inclusion criteria. All included studies were published in 2023 and 2024. The primary applications of GenAI were in learning resource development and assessment, with reported benefits such as time savings, personalised learning and reduced resource use. Challenges included accuracy concerns, inconsistent outputs, technical limitations, algorithmic bias and risks to academic integrity. CONCLUSIONS:This scoping review provides an overview of how GenAI is being integrated into health professions education. While the technology offers opportunities to enhance teaching and assessment, its implementation requires consideration of reliability, ethical concerns and educator preparedness. This review is the first to examine GenAI implementation across multiple AHPRA-regulated health professions and proposes a practical framework (AI HPE checklist) to guide responsible use.

Despite extensive preparedness literature, existing studies fail to adequately explore healthcare graduates’ feelings of preparedness longitudinally across new graduate transition journeys, nor do they compare different healthcare professions to ascertain what opportunities exist for multiprofessional transition interventions. Therefore, this Australian study, underpinned by temporal theory, explores the preparedness transitions of medicine and pharmacy graduates. Our 6-month qualitative longitudinal study involved 12 medicine and 7 pharmacy learners after purposive sampling. They participated in an entrance interview before starting internship, longitudinal audio-diaries during their first three months of internship, and an exit interview. Framework analysis explored patterns in the data cross-sectionally and longitudinally for the whole cohort (thinking over time), with pen portraits illustrating individuals’ journeys (thinking through time). Preparedness and unpreparedness narratives involved practical skills and tasks, interpersonal skills, knowledge, and professional practice for medicine and pharmacy. However, narratives for practical skills and tasks, and professional practice were dominant amongst medicine graduates, while narratives for interpersonal skills and knowledge were dominant amongst pharmacy graduates. We found numerous cohort changes in feelings of preparedness over time, but the illustrative pen portraits demonstrated the complexities and nuances through time, including feelings of preparedness before internship becoming unpreparedness during internship (e.g., cannulas), improving preparedness through time (e.g., cover shifts), and persistent feelings of unpreparedness (e.g., patient interactions). While our cross-sectional findings are reasonably consistent with existing research, our comparative and longitudinal findings are novel. We recommend that educators build learners’ preparedness through uniprofessional transition interventions involving practical skills and tasks, and professional practice in medicine, and interpersonal skills and knowledge in pharmacy. More importantly, we recommend multiprofessional transition interventions for medicine and pharmacy learners before internship focusing on knowledge, and during internship focusing on practical skills and tasks.

We propose a learning analytics-based methodology for assessing the collaborative writing of humans and generative artificial intelligence. Framed by the evidence-centered design, we used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims; we used data collected from the CoAuthor writing tool as potential evidence for these claims; and we used epistemic network analysis to make inferences from the data about the claims. Our findings revealed significant differences in the writing processes of different groups of CoAuthor users, suggesting that our method is a plausible approach to assessing human-AI collaborative writing.

WHAT WAS THE EDUCATIONAL CHALLENGE?:A major challenge in health professions education is to equip graduates with essential teamwork skills, addressing cognitive, motivational, and emotional barriers that hinder effective collaboration among students from diverse backgrounds. WHAT WAS THE SOLUTION AND HOW WAS THIS IMPLEMENTED?:The Teamwork Baseline Assessment Tool (TBAT) was developed as an innovative solution to teach collaboration and teamwork, focusing on growth mindsets, reactions to challenging scenarios, and ideal team player attributes. Implemented during the orientation for new first-year students, TBAT facilitated early engagement in teamwork discussions, with students receiving personalised reports to aid in self-reflection and development. WHAT LESSONS WERE LEARNED?:Key lessons included the importance of initiating teamwork conversations early, the value of personalised feedback in promoting self-awareness and peer understanding, and the effectiveness of TBAT in providing instructors with insights into students' teamwork aptitudes. WHAT ARE THE NEXT STEPS?:Expanding TBAT across various student populations and integrating it into the curriculum aims to provide continuous opportunities for applying and reinforcing teamwork and collaboration skills. This strategy will support the development of targeted instructional approaches, fostering a collaborative learning environment and preparing students for the teamwork challenges in healthcare settings.

The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.

This manuscript describes the conception and development of a novel, innovative digital health and informatics learning module designed specifically for entry-to-practice physiotherapy university programs. The design process involved consultation with stakeholders, alignment with contemporary digital health competency guidelines for health professional education, and educational design workshopping with faculty to ensure relevance and success. Key curriculum components include modules on health system transformation, design-thinking approaches, solution refinement and innovation pitching in the context of digital health. The subject intended learning outcomes (SILOs) were focused on digital health transformation, addressing the need for a curriculum on digital health transformation. This tertiary module aims to equip university graduates with essential knowledge and skills to thrive in a digitally enabled healthcare system by offering this framework for future health professional education in the digital age.

Introduction:Learning health systems (LHSs) play a crucial role in improving healthcare delivery and outcomes through continuous learning and data-driven decision-making. Implementation of LHSs spans individual, organization, and systemic levels of healthcare. This paper outlines a systematic approach for developing a comprehensive codebook to identify barriers, enablers, and strategies associated with the establishment and operation of LHS from a multilevel perspective. Methods:The codebook development process was divided into two phases and employed a coding team. Phase 1 involved the synthesis of previous literature, which drove the development of initial codes. Phase 2 included the testing of the codebook with a pilot dataset to derive new codes or iterative refinement, ensuring robustness, and validity. Results:The literature search revealed 12 papers that detailed the barriers, enablers, and strategies for LHS implementation. Micro-level codes were derived from a mixture of existing literature and our pilot dataset. Most meso-level codes barriers and enablers were derived from the literature, with some subcodes derived from participant interviews. All strategies for implementation at the meso-level were identified in the literature. At the macro-level, all codes and subcodes were from the literature. Conclusions:The codebook contributes to the advancement of implementation science in LHS. The codebook facilitates effective analysis and understanding of the key factors influencing the success of LHS implementation, offering practical insights for policymakers, healthcare practitioners and researchers engaged in the ongoing evolution of LHS.

Digital transformation has disrupted many industries but is yet to revolutionize health care. Educational programs must be aligned with the reality that goes beyond developing individuals in their own professions, professionals wishing to make an impact in digital health will need a multidisciplinary understanding of how business models, organizational processes, stakeholder relationships, and workforce dynamics across the health care ecosystem may be disrupted by digital health technology. This paper describes the redesign of an existing postgraduate program, ensuring that core digital health content is relevant, pedagogically sound, and evidence-based, and that the program provides learning and practical application of concepts of the digital transformation of health. Existing subjects were mapped to the American Medical Informatics Association Clinical Informatics Core Competencies, followed by consultation with leadership to further identify gaps or opportunities to revise the course structure. New additions of core and elective subjects were proposed to align with the competencies. Suitable electives were chosen based on stakeholder feedback and a review of subjects in fields relevant to digital transformation of health. The program was revised with a new title, course overview, course intended learning outcomes, reorganizing of core subjects, and approval of new electives, adding to a suite of professional development offerings and forming a structured pathway to further qualification. Programs in digital health must move beyond purely informatics-based competencies toward enabling transformational change. Postgraduate program development in this field is possible within a short time frame with the use of established competency frameworks and expert and student consultation.

Delivering personalised, formative feedback to multiple problem-based learning groups in a short time period can be almost impossible. We employed ChatGPT to provide personalised formative feedback in a one-hour Zoom break-out room activity that taught practicing health professionals how to formulate evaluation plans for digital health initiatives. Learners completed an evaluation survey that included Likert scales and open-ended questions that were analysed. Half of the 44 survey respondents had never used ChatGPT before. Overall, respondents found the feedback favourable, described a wide range of group dynamics, and had adaptive responses to the feedback, yet only three groups used the feedback loop to improve their evaluation plans. Future educators can learn from our experience including engineering prompts, providing instructions on how to use ChatGPT, and scaffolding optimal group interactions with ChatGPT. Future researchers should explore the influence of ChatGPT on group dynamics and derive design principles for the use of ChatGPT in collaborative learning.