Medicinal terminologies depend on expert-authored fully specified names (FSNs) and preferred terms (PTs) that encode implicit editorial rules not formally represented in ontologies. This work explores the use of large language models to learn and apply these rules for automated medicinal terminology naming. Using existing OWL-defined concepts from the HSE Ireland medicinal terminology, this project evaluated a four-level approach ranging from direct fine-tuning to rule-compositional cascading prompts. Results across six medicinal concept types show that the cascading prompt framework further improves accuracy, numerical robustness, and interpretability by explicitly modelling editorial decision steps, enabling scalable and consistent FSN and PT generation.
This study updated an existing health information technology maturity and staging model for nursing homes to support alignment with value-based care. Using a five-round Delphi process (November 2023-July 2024), 22 national long-term care health information technology experts reviewed and refined a validated model of 183 items across 27 content areas. Consensus-based revisions resulted in a streamlined model with 142 items across 21 content areas organised into four domains: resident care, nursing care, clinical support, and administrative activities. Most items (86%) were assigned to Stage 4 or higher, indicating readiness for bi-directional data exchange and resident-centred data use. The revised model provides an expert-informed, policy-relevant framework to guide nursing home health information technology development and value-based care readiness by 2030.
The ICD-11 has been designed to be a foundational classification for the whole of the health, disability and aged care systems, providing a basis for consistent statistics and data analytics. The Australian Institute of Health and Welfare (AIHW) is preparing a business case, planned to be presented to governments in late 2026, to inform a national decision on the scope of ICD-11 implementation in Australia. This will be based on the information that is being gathered about the potential benefits of the use of ICD-11 in the Australian context.
The Initial Patient Assessment (IPA) documentation within the selected healthcare organisation's electronic medical record (EMR) is a key component of nursing documentation and planning for patients' hospitalisations. Since its implementation in 2019, the EMR IPA documentation has not been re-assessed for usefulness or completeness. A project was developed to assess, re-design and implement an updated IPA using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. New IPA forms were co-designed with consumers and nurses to be more conversational and implemented across the organisation for neonatal admissions, paediatric admissions, adult admissions, and adult and paediatric day ward admissions. Improvement in completeness and usefulness was sustained, and this project filled a gap in the literature by providing a co-designed approach to nursing documentation within the EMR with end-users and consumers.
Digital wellness interventions for preventive health, such as prediabetes management, often face high dropout rates due to insufficient personalisation and motivational support. This design science research study presents a self-determination theory (SDT)-informed gamification framework to address these challenges. Drawing on a systematic literature review, the study identifies engagement barriers, facilitators, and empirical evidence to guide design. The framework maps strategies to SDT's core constructs: autonomy (user-defined goals with structured guidance and selectable difficulty levels), competence (adaptive challenges with badges and points), and relatedness (collaboration-focused progress sharing). The framework was co-designed with 20 young adults in Australia and validated by six multidisciplinary experts. The study revealed that 60% of participants felt overwhelmed with fully open goal setting, highlighting that structured guidance is essential for autonomy support in preventive wellness applications. These findings informed the development of MiCARE, a progressive web app that operationalises the framework with user-centred, motivationally aligned features. This study offers a replicable, theory-driven approach for designing engaging preventive wellness interventions.
Falls among elderly residents in residential aged care facilities (RACFs) are a critical public health concern, often resulting in severe injuries, reduced mobility, diminished quality of life, and increased healthcare costs. Traditional machine learning methods struggle to accurately identify fall risk factors within unstructured electronic health record (EHR) data due to the lack of labelled datasets. This study develops methods for extracting fall risk factors from unstructured EHRs in RACFs, leveraging large language models (LLMs) and the retrieval-augmented generation (RAG) technique. Utilising an established knowledge base from RAG documents, LLMs with tailored prompts were employed to extract fall risk factors from the collected 1,854 nursing notes in the EHR. Expert feedback was used to evaluate and refine the extracted factors. The paired t-test suggested that the RAG method significantly improved risk factor extraction, achieving a precision of 97.3%, a recall of 89.9%, and an F1 score of 93.3%. Integrating LLMs with literature-based RAG effectively extracts fall risk factors from unstructured EHRs in RACFs, addressing challenges associated with unlabelled datasets. This approach enhances the identification of risk factors, informing targeted fall prevention strategies.
Prescription drug monitoring programs (PDMPs) are used to monitor and mitigate high-risk medicine use and harms. As part of a larger mixed-methods evaluation, this study explored healthcare practitioners' perspectives on the implementation of "SafeScript NSW" (NSW's PDMP). Semi-structured interviews were conducted with fourteen prescribers (general practitioners, other specialists, and nurse practitioners) and pharmacists impacted by the implementation of SafeScript NSW. Participants outlined implementation strategies that worked well, but identified gaps with poor awareness of SafeScript NSW among many specialties that use PDMP-monitored medicines but were not targeted by promotional efforts. Participants identified potential facilitators and barriers to long-term success of SafeScript NSW, and had mixed perceptions about mandating use as a long-term strategy. This is one of the first studies exploring healthcare practitioners' perceptions of statewide PDMP implementation processes. Insights from end-users can inform strategies to improve awareness and uptake by healthcare practitioners that may benefit from using PDMP and optimise the realisation of potential benefits.
An interruptive alert as a clinical decision support tool has been implemented to reduce the ordering of unnecessary urine cultures in a tertiary paediatric emergency department. Following its introduction, there was an immediate and sustained reduction in urine culture ordering rates of 25%, with a small increase in cultures with pure growth over a twelve-month period.
The rapid expansion of digital health has created demand for a specialist workforce capable of leading complex change, yet little is known about how leadership and career stage are distributed across digital health occupations. This study analysed data from the 2023 Specialist Digital Health Workforce Census to examine patterns of career stage and leadership by occupation and gender. Career stage was defined by the time spent within the specialist digital health workforce. A total of 699 valid responses were included in the analysis. Leadership was significantly associated with career stage, with leaders more likely to be early in their specialist digital health career. Career stage and leadership representation varied significantly across occupations. Gender distributions were also significant. These findings highlight the inequities in leadership pathways and the need for targeted workforce planning and leadership development strategies to support an inclusive and sustainable digital health workforce.
Generative artificial intelligence (AI) offers novel opportunities for scalable health professions education. However, several AI-driven simulated patient initiatives rely on ad hoc approaches. This study describes the development and evaluation of a methodological framework for designing generative AI mock patients. Using a physiotherapy education chatbot as an exemplar, a framework is presented integrating clinical reasoning and communication, layered prompt architecture, scenario constraints, and guardrails. Results suggest that the framework supported consistent patient-persona behaviour and scenario progression, while also revealing select interaction challenges. Mitigation strategies are described. This work contributes to the health informatics education community by offering a practical methodology for the evidence-based design of generative AI simulations in health professions education.
Emergency department (ED) walk-ins and ambulance arrivals are often modelled independently, overlooking their interdependence and the impact of fixed treatment space (TS) capacity. An established discrete event simulation algorithm has been tailored as the basis of a digital twin (DT). Driven by regular streams of incoming ED presentations, patient off-stretcher time (POST), ambulance bay and TS occupation across a 24-hour period contingent on patient flow and ED TS capacity was simulated. The simulation was calibrated using 19,570 ambulance records and data from a 250-bed hospital (2021-2022). Scenarios of five ambulance bays with 43, 38, and 34 TS capacities were investigated, revealing patterns of TS depletion and recovery, including cases where recovery failed within 24 hours, underscoring the sensitivity of patient flow to resource constraints and the value of DT-based simulation for capacity planning.
As healthcare demand continues to rise and resources remain limited, many health services have introduced virtual front door (VFD) models—typically using hotlines and/or web portals—to streamline clinical triage and reduce unnecessary emergency department visits. This study aimed to explore culturally and linguistically diverse (CALD) consumers’ experiences and barriers to accessing healthcare (including through the VFD) in New South Wales. Semi-structured interviews with CALD consumers and clinicians revealed key barriers, including low awareness, low confidence, low trust or a lack of reassurance, and mixed expectations of digital experiences. Co-designing healthcare access initiatives such as the VFD with CALD consumers is recommended to improve their experience and uptake, thereby maximising the impact of these initiatives.
A project was developed to support the transition of mental health forms from paper to within the electronic medical record across Victoria’s largest healthcare organisation. The electronic forms ensure compliance with mental health reporting requirements, including activity-based funding, and have demonstrated significant financial funding increases due to documentation accuracy and completeness.
Hearing loss is a rising health concern. The World Health Organization estimates that over 1.5 billion people worldwide are currently experiencing hearing loss. This estimate is projected to rise to 2.5 billion by 2050. Hearing loss is often linked with other health conditions, such as dementia, exacerbating its impact. Chronic hearing loss often worsens over time. Early intervention and effective management are therefore important. One of the first intelligent agents designed to provide personalised advice to help preserve the hearing of individuals experiencing mild hearing loss is presented. The intelligent agent functions as a smartphone app that uses natural language processing to extract information from end-users with mild hearing loss and provides evidence-based personalised advice generated through generative artificial intelligence. The advice was assessed through a tailored criterion inspired by the two validated instruments DISCERN and PEMAT scores. Assessment was undertaken across three dimensions: Relevance (measuring how well the advice is personalised to the user), Accuracy (measuring whether the provided information is accurate), and Understandability (measuring whether the text is easily understandable without specialised knowledge). Measurement across 83 AI-generated responses resulted in the following scores: Relevance (85.5%), Accuracy (80.7%) and Understandability (89.2%).
The production of guidance by the Australian Government for the adoption of responsible artificial intelligence has evolved in recent years. Reporting results of an iterative scoping review charts the evolution and relationships between this documentation, illustrating when they were published, the origin and evolution of their content, as well as their inter-relatedness. The purpose is to identify changes in government thinking and to highlight those elements most relevant to the Australian healthcare workforce.
HL7® FHIR® is increasingly used for health information exchange, yet delivery outcomes vary and teams frequently re-solve similar problems. This paper presents a “towards” contribution that captures proven practice as two complementary pattern languages: one for authoring FHIR artefacts (profiles, terminology, implementation guides) and one for implementing distributed systems that exchange FHIR at scale. The pattern languages make forces and trade-offs explicit, enabling reuse across teams and contexts. A publication pipeline produces multiple output formats—static website, EPUB, and DocBook—from a single machine-readable source, including a machine-consumable llms.txt index to support AI-assisted navigation. The approach is described, the generated outputs presented, and the plans for operational validation and AI-assisted quick-start pathways are outlined.
Gender inequities persist in the specialist digital health workforce, influencing education, remuneration, and career progression. Despite women comprising the majority of this workforce, disparities in pay and gender remain underexplored. Data were drawn from the 2023 Specialist Digital Health Workforce Census, which included 425 Australian respondents who completed gender-related questions and reported remuneration, was analysed to examine associations between gender, remuneration level, and perceptions of equity. Women represented 74.1% of respondents, yet significant correlations emerged between gender and remuneration (p=0.006). Women earning less than $3,000 per week were more likely to identify financial barriers to career development and emphasise policies on pay equity as facilitators. Perceptions of organisational support for gender equity differed by gender (p<0.001), while remuneration influenced views on career development and work-life balance. Findings highlight structural inequities shaped by gender and remuneration, underscoring the need for targeted interventions such as mentorship, sponsorship, and inclusive leadership programs. Policies promoting pay equity and culturally responsive career development are critical to advancing gender equity in digital health. Further research is required to explore these significant themes.
Psychosocial support is becoming increasingly important for both patients and caregivers during their illness recovery journey. While various self-care strategies can be recommended by healthcare professionals (HCPs) like nurses, such as exercise and relaxation techniques, systematic guidance for evaluating the therapeutic potential of streaming media (SM) is still lacking. Patients engage with entertainment media on streaming platforms daily. Among these, Korean Wave (K-wave) content, including Korean pop (K-pop) and drama (K-drama), has gained global reach in recent years. Music therapy and narrative therapy have established evidence bases as non-pharmacological therapeutic approaches, but their potential in nursing practice is still unexplored. Through a mixed-method analysis comprising a systematic review of 90 music therapy and narrative therapy studies and a thematic analysis of Netflix's animated movie "KPop Demon Hunters" (KPDH), this study identified nine therapeutic themes that can be used for psychosocial support in nursing practices. These therapeutic themes were compiled together with evidence-based mechanisms, KPDH examples, and observable elements in SM content into KEMA (K-Wave Element Mapping and Assessment) - a digital health humanities tool that nurses can use to facilitate informed discussions through SM content that patients watch and listen to. KEMA's platform-agnostic design and cultural flexibility allow it to be used across a broad range of SM platforms and media genres to advance digital health humanities into evidence-based, actionable clinical practice.
Recent research focuses on nurses' retention. The aim of this paper is to present a guide of incentives that was developed by the Hellenic Regulatory Body of Nurses (ENE) board of management. A forum addressing open ended questions and a 4-step approach was applied. Four main domains of incentives were identified and a guide was created. New technological tools may also support further this area.
This study evaluates diagnostic coding consistency across sequential hospitalizations using a longitudinal analysis of the CMS Inpatient Dataset with more than five million records. By transforming ICD-10-CM codes into CCS categories, the research tracks 9 chronic and 7 acute conditions using State Transition Matrices to identify patterns: Continuous, Fading, Intermittent Gap, and Late Onset. Significant diagnostic gaps were observed. Diabetes and Multiple Myeloma showed 'Intermittent Gap' rates of approximately 14%. Critically, for most chronic conditions, the 'Intermittent Gap' pattern correlated with the highest hospital Length of Stay, suggesting that missed documentation may obscure patient complexity. While some gaps stem from coding regulations or clinical transitions (such as remission or combination coding) the prevalence of intermittent patterns indicates systemic coding failures, with possible clinical coordination implications.
