To describe the effect of indication-based prescribing and prescribing with indications on electronic medication record documentation, appropriate medicines use, and clinical outcomes.

To synthesise evidence on the effectiveness of digital self-management programmes for stroke survivors' health outcomes, self-efficacy, and quality of life.

Practical models for learning health systems implementation often lack a clear understanding of the organizational capacities required to sustain continuous data-driven improvement cycles. Among these, analytical capacities are widely recognized but insufficiently conceptualized. This study aims to develop a comprehensive framework of analytical capacities in healthcare organizations.

Health information systems for acute, primary and aged care have evolved separately. Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the dominant clinical terminology in acute and primary care, while interRAI assessment systems are widely used in aged care. This creates barriers for seamless sharing of health information when older people transition between settings. We answer the research question "Is it feasible to map aged care clinical concepts to a standardised clinical vocabulary?", to support interoperability.

Early diagnosis of cognitive impairment, including Alzheimer's and other dementias, is critical for effective treatment and slowing disease progression. However, over 50% of cases remain undiagnosed until advanced stages due to limitations in current methods. Recognizing speech impairments as early markers of cognitive decline, this study evaluated the utility of speech analysis as a technique for early detection. We introduce TransformerCARE, a speech processing pipeline utilizing advanced speech transformer models.

Despite the increasing use of AI tools like ChatGPT, Claude, and Gemini in scientific writing, concerns remain about their ability to generate accurate, high-quality, and consistent abstracts for research publications. The reliability of AI-generated abstracts in dental research is questionable when compared to human-written counterparts. This study aimed to develop a framework for evaluating AI-generated abstracts and compare the performance of ChatGPT, Claude, and Gemini against human-written abstracts in dental research.

Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to develop and validate an interpretable machine learning model using multicenter ICU data to predict 30-day mortality in this population.

Geriatric assessments capture multidimensional data on physical, cognitive, psychological, and social health, offering opportunities to apply machine learning (ML) to support clinical decision-making in aged care. However, the application of ML to such data has not been systematically synthesised.

Patient mood assessment is key in managing chronic diseases but is often overlooked. Although conversational agents enhance telemonitoring and engagement, few healthbots incorporate automated mood analysis into routine clinical workflows or hybrid care. The rise of multimodal and large language models presents new opportunities to embed emotional assessment into daily healthcare interactions.

Large language models (LLMs) demonstrate significant potential in medical information provision and may serve as valuable tools for patients seeking health information. Existing research primarily focuses on individual models or general medical inquiries, with no systematic evaluation of mainstream LLMs' performance. Particularly noteworthy is the absence of cross-comparison studies involving Chinese AI model DeepSeek-R1. This research gap may hinder the effective translation of artificial intelligence technology into clinical practice for rheumatic diseases.

Indigenous peoples have long demonstrated resilience and holistic approaches to health, grounded in cultural practices and community knowledge. Despite this, Indigenous populations globally experience a disproportionately high burden of Type 2 Diabetes Mellitus (T2DM). Existing healthcare models often fail to address the cultural, social, and structural determinants that influence health outcomes in these communities. Digital health technologies are increasingly recognised as valuable solutions to improve diabetes care, particularly in remote and underserved Indigenous settings.

Artificial intelligence-based conversational agents (AI-based CAs) have emerged as essential tools for communication and service delivery in the healthcare industry. However, global synthesis regarding the current applications and effectiveness of this technology remains scarce.

The quality assessment of personalized exercise prescriptions is currently hampered by the subjectivity and inefficiency of traditional rating scales. Artificial intelligence (AI) presents a transformative opportunity for objective, scalable evaluation.

Septic shock is associated with high mortality, making accurate risk stratification crucial for tailored treatment. Traditional clinical scoring systems are limited by static assessments and suboptimal accuracy. Artificial intelligence (AI) offers the potential for dynamic and personalized prediction; however, its clinical utility requires rigorous evaluation.

Pediatric Emergency Departments (PEDs) face overcrowding partially due to delayed hospital admission decision. Machine Learning (ML) models could early predict it.

To evaluate the performance of general-purpose, retrieval-augmented, and medicine-specific AI chatbots in answering common thyroid eye disease (TED) patient questions.

Diagnostic stewardship refers to coordinated efforts to ensure timely review and appropriate follow-up of test results. This scoping review aimed to identify the mechanisms, or triggers that create conditions that facilitate the diagnostic stewardship process in the electronic management of test results by clinicians.

Current systematic literature reviews largely rely on manual screening of articles retrieved through keyword search, which is time-consuming and difficult to scale. To address this limitation, large language model (LLM)-based approaches offer the potential to automate the screening process. In this study, we aim to enhance the efficiency and accuracy of literature screening by developing an LLM-based method and exploring techniques such as rule-based preprocessing, prompt engineering (i.e., retrieval-augmented generation (RAG)) and ensemble strategies.

The Work Observation Method By Activity Timing (WOMBAT) is a widely adopted technique for direct observational studies of health professionals' work patterns. Over the past 14 years its use has grown substantially, enabling researchers to generate rich quantitative data reflecting the complexity of clinical workflows across diverse healthcare settings.