Digital Health Week is an opportunity for anyone interested in digital health to participate in a program of virtual events and share research and ideas. It is designed to be both informative, provocative, and a showcase of the innovative work being undertaken in digital health by bringing together researchers, health services, industry, and the community to share their work and build person-centred eHealth collaborations. This Open Journal displays abstracts for oral presentations and ePosters that were accepted for inclusion at the event. All abstracts included in this journal underwent blinded peer-review.
Home blood pressure measurements are not performed according to guidelines and standardized education is urgently needed
Background: Home blood pressure measurement (HBPM) utilizes digital health technologies for BP measurement, recording, reporting and monitoring. Patients need education to perform HBPM properly. It is unknown how HBPM is performed and what education is provided, which was the aim of the study. Methods: Mixed-methods study among Australian adults who perform HBPM (June-Dec 2023). Participants completed a 30-item online survey on the guideline recommendations followed and education received for HBPM. Phone interviews were conducted among a purposive sample to explore survey topics. Results: Participants (n=350) were middle-aged (58¬±16 years, 54% women) and most (n=250, 71%) had hypertension. One-third (n=118, 34%) of participants used a validated BP device for HBPM. Most purchased their device from a pharmacy (n=130, 69%) or online (n=39, 21%), using online reviews and brand recognition to select a device. When measuring BP, guideline recommendations for HBPM were not always followed by participants. Most participants measured BP seated (n=316, 90%) with the cuff fitted to a bare arm (n=269, 77%). Only 15% measured BP in the morning and evening (n=54). Almost all (n=277, 93%) participants sought information about HBPM; mostly from a GP (n=169, 61%) or online (n=165, 60%). Interview participants preferred trustworthy websites from well-known organizations and avoided international sites. Participants who sought information online did not perform higher quality HBPM. Conclusions: HBPM is not performed according to guideline recommendations. Adults seek information about HBPM online, however this did not improve the quality of HBPM. These findings highlight a need for effective education to support HBPM.
Traditional and digital oral health education within cardiac rehabilitation clinics: A protocol to improving oral and cardiovascular health
Background: Stagnant oral biofilm can lead to oral diseases gingivitis and periodontitis. If left untreated, periodontitis can lead to destruction of the supporting structures of the teeth and trigger systemic inflammation. As early as 21-days after ceasing toothbrushing, inflammatory markers associated with atherosclerosis the primary cause of cardiovascular disease (CVD), can be detected in blood samples. Good oral hygiene is the most effective way to prevent periodontitis, thereby reducing CVD risk. The primary aim of this study is to assess whether individualised oral hygiene instruction partnered with a digital oral health education (DOHE) package can improve the oral health of patients attending cardiac rehabilitation. Method: A total of 165 participants will be recruited from two Sydney cardiac rehabilitation clinics into this dual centre, single blind, parallel design, randomised controlled trial. Baseline oral health clinical examinations and self-report questionnaires are completed before participants are randomised into one of 3 arms, in a 1:1:1 ratio. The examination and questionnaires are repeated at 6- and 12-weeks. The primary analysis will be an adjusted analysis of variance performed to assess differences between groups for approximal plaque index scores between baseline and follow-up. Conclusion: Information involving oral health's role in heart health within the cardiology space is almost non-existent. This is the first study to implement DOHE to patients attending cardiac rehabilitation clinics. By building oral disease prevention strategies for these individuals, they will have success in future health outcomes.
An Analysis of Patient-sharing Provider Network in New South Wales (NSW), Australian Healthcare Industry Using Administrative Claim Data
Background: Effective and efficient healthcare services require the comprehensive collaboration of healthcare professionals and complex inter-reliant activities. Effective collaboration can ensure the best possible health outcome and value for the patient-sharing provider network. Therefore, understanding the dynamics of the patient-sharing provider network is essential to measure the efficiency of healthcare delivery services. Aims: This research aims to improve health outcomes by analysing the attributes of a patient-sharing provider network. This research also explores the impact of different patient-sharing provider network (PSN) characteristics on admission rate, re-admission rate, length of stay (LOS), hospitalisation cost, etc. Method: In this study, I use administrative claim data to construct and develop a PSN, and social network analysis (SNA) is used to analyse PSN. Results: A patient-sharing provider network makes the health sector more efficient and helps make quick and proper real-time decisions. Different healthcare organisations in Australia can use a patient-sharing provider network to make better strategic and operational directives. Therefore, this research provides a comprehensive analysis of the patient-sharing provider network. Conclusion: This research provides valuable insights and a networked approach to analysing healthcare collaboration in the Australian health context. This research's findings and insights could help transform how the Australian health industry manages, analyses, and leverages data. The result is beneficial for academics, patients, healthcare providers, government, and health industry stakeholders to formulate better policies, eventually improving the quality of care while reducing public healthcare costs in the future.
The HealthTalk Connect App: A co-designed communication support tool for people with communication disabilities
Background: Effective communication in hospital is vital for quality care and especially challenging for people with a communication disability. The Aphasia App is a novel, digital environmental intervention that was designed to support communication between people with aphasia, one type of communication disability, and their healthcare workers in hospital. Preliminary results from ethnographic observations and interviews regarding use of the Aphasia App revealed that a range of factors potentially hinder its use in clinical interactions, suggesting a need for redesign. Aims: This presentation summarises our work towards the design, development and testing of the HealthTalk Connect App; a collaborative redesign of the Aphasia App to improve implementation in healthcare interactions. Methods: The HealthTalk Connect App was co-designed with multi-disciplinary healthcare workers from Epworth hospital and their consumers with lived experience of communication disability. An exploratory implementation trial of the App is investigating healthcare workers' perceptions of the App's utility. We will analyse data from approximately 15 semi-structured interviews with multidisciplinary healthcare workers using reflexive thematic analysis. Results: Feedback from healthcare workers during interviews revealed that they were enthusiastic about the App but felt unable to use it with patients with severe communication disabilities. Healthcare workers also wanted ongoing training and practical demonstrations to facilitate App use. Discussion: Healthcare worker feedback led to procedural changes to support App implementation in a second phase of testing. Results from the next phase of testing will reveal if the HealthTalk Connect App is useful to support communication between people with communication disabilities and their healthcare workers in hospital.
Design and Developing the Whiplash Navigator as a Digital Resource for Insurance Personnel
At least 50% of people with whiplash do not recover within six months. Significantly, access to guideline-based, high-value, low-cost care is delayed due to the stepped care mode., where people are quickly stepped up too often for unnecessary services such as imaging (e.g. unnecessary MRI) and referrals (e.g. surgery). The insurer is a key stakeholder in Australia's whiplash injury treatment process, and they need more resources. The aim is to design, develop and implement MyWhiplash Navigator (MWN) with Allianz (a CTP insurer) and evaluate the effect on insurer knowledge, practice patient reach, acceptance and health outcomes. Following a research translation framework. Phase 1: Co-design of bespoke resources (website pages) within MWN to assist insurance personnel in implementing the above key messages from Whiplash guidelines. Phase 2: Implement MWN amongst Allianz insurance personnel. Insurers receive interactive education from opinion leaders. Processes were embedded within the workflow as reminders for Allianz personnel. Key processes include i) encouraging claimants to use MWN and complete WhipPredict online. ii) encourage claimants and their primary HCPs to use the resources in MWN to support treatment and iii) facilitate referral of claimants at medium/high risk of poor recovery to ‚Äúwhiplash‚Äù specialists. Phase 3: Evaluate outcomes including i) insurer knowledge (pre-post questionnaire), ii) insurer practises (pre-post case file audit) and iii) patient reach, acceptance and health outcomes (post-questionnaire). Preliminary results. The website component is live, and 89 case managers across NSW, SA, and QLD have received the interactive education and are using the MWN as resources within their workflow. Allianz personnel have integrated the process into their workflow, resulting to date in 52 claimants Whipredict completed online and two claimants at medium/high-risk referral to 'whiplash' specialist. WMN as a resource for insurers was well-accepted and integrated into the case manager workflow in this pilot. Phase 3 is still ongoing.
Treatment may be virtual, but the impact is real. Targeting insomnia with VR mindfulness
Introduction: Insomnia is associated with a substantial societal burden. Available treatments, show effectiveness but are not always available or suitable. Virtual Reality (VR) is an increasingly affordable, accessible, and scalable tool. Therapeutic VR is being evaluated as a treatment delivery tool for a range of disorders but has not been evaluated directly for the treatment of insomnia. This research aims to (1) Assess attitudes and opinions of clinicians and those with insomnia towards the acceptability of VR delivered mindfulness and identify practical barriers and steps for the use of VR technology in this context. Methods and Results: A mixed methods approach including self-report survey data on familiarity with digital health technologies and a single 2-hour in-person focus group. Recorded discussions using group-specific topics were recorded and transcribed. Lack of familiarity with technology is a known barrier to the adoption. To evaluate the impact of exposure to VR mindfulness on attitudes and opinions, participants rated their level of confidence in VR mindfulness as an effective and useful tool for insomnia before and after participants individually explored 4 VR mindfulness apps. Interacting with VR mindfulness positively changed the attitudes and opinions of clinicians and individuals with insomnia towards the potential effectiveness and usefulness of VR as a tool to deliver insomnia treatment. Barriers to clinical implementation and user adoption included usability, cost, and accessibility and funding, accessibility, and treatment guidelines key facilitators. Conclusions: These results suggest an evaluation of the feasibility of VR mindfulness to deliver treatment for chronic insomnia is warranted.
Developing an online dynamic consent platform to support personalised, accessible and informed consent for genomic testing in oncology
Genomic settings are complex and challenging for informed decision making. Partnering with patients and professionals, we aimed to codesign an innovative intervention for cancer patients to improve decision making and COnsent in GENomic Testing (CoGenT). The CoGenT intervention is an online Dynamic Consent Platform that incorporates the first Question Prompt List about genomic testing and research, with answers addressing patient information needs. It was codesigned iteratively using input and feedback gathered during three rounds of interviews with 38 stakeholders (cancer patients who were/were not taking part in genomic research, carers, study coordinators, clinicians), guided by a multidisciplinary steering committee. Information needs revolved around the nature of genomic testing, results and implications, and research participation. Views on the intervention highlighted its potential value to support cancer patients who are offered genomic testing by informing them in a flexible way and enabling them to indicate their preferences and identify where they need further support. Interviews demonstrated the importance of optimizing clarity, accessibility, and engagement in the CoGenT intervention. The CoGenT intervention is valued by patients, clinicians, and research personnel. This work will facilitate equitable access to cancer genomic research by ensuring consent processes meet the needs of key stakeholders. We will pilot the CoGenT intervention to assess effects on decision quality outcomes compared with usual consent processes in cancer genomic studies. If results are positive, these evidence-based resources will help establish practices to follow when genomic testing enters routine care, with patients and families well-prepared and supported before, during and after testing.
Using an AI scribe in private practice enhances patient-clinician interactions and reduces administrative burden
Background: AI may automate and enhance various processes in healthcare, including clinical documentation. No study has investigated the impact of using AI for clinical documentation in allied health private practice. Methods: In this mixed-methods study, 119 allied health clinicians recorded their interactions with patients using a natural language processing tool. Participants completed questionnaires at baseline, 6-weeks and 3-months. Clinicians were asked to invite their patients to share their perspectives on AI being used during their appointment. Patients who consented (n=157) completed one questionnaire immediately after their appointment. A subset of clinician (n=15) and patient (n=14) participants took part in a semi-structured interview to explore their perspectives in greater depth. Results: At 3 months, there was a significant reduction in time spent doing notes (p<0.001), doctors' letters (p<0.001), notes out of hours (p<0.00) and burden of clinical documentation (p=0.02). From the interviews, patients and clinicians reported that AI had positively impacted their interaction (e.g., 'He could really engage with me'; 'I felt heard'; 'I was able to pay 100% of attention to my patient'). Patients expressed high trust in AI, largely because of trust in their clinician (e.g., 'I know my clinician really well and I trust him. There's not much that I wouldn't share with him that I wouldn't feel comfortable with being recorded'). Conclusion: Using AI for clinical documentation was well accepted and had a positive impact on patient-clinician interactions. Using AI holds potential to streamline clinical documentation processes and enhance operational efficiencies in allied-health private practice.
Prevalence of ChatGPT use in a nationally representative Australian community sample
Background: ChatGPT and other generative AI tools have dramatically changed the health information environment. Discussions about their benefits and risks need clear evidence about who is using them and why. This study aimed to examine ChatGPT use for health purposes in Australia. Methods: The sample was recruited from a nationally representative Australian panel in June 2024. Participants completed self-reported survey questions about their use or intended use of ChatGPT for health purposes. Scores were weighted to Australian population benchmarks using propensity scores and associations were assessed using simple logistic regression. Results: Of the 2,034 participants (50.1% male; 48.4% female) who took part, 9.9% reported that they had asked ChatGPT a health question in the past 6 months (95%CI: 8.5%‚ 11.4%). This was more likely for people born in a non-English speaking country, who speak a non-English language at home, and who have low health literacy (p = <0.05). The most common health questions related to learning about a specific health condition (47.6%), finding out what symptoms mean (37.4%) or what actions to take (35.8%), and understanding medical terms (34.8%). 38.8% of those who knew about ChatGPT but had not yet used it for health purposes would consider asking the platform a health question in the next 6 months. The most common anticipated tasks related to learning about a specific health condition (18.1%), understanding medical terms (16.8%), or finding out what symptoms mean (16.4%) Conclusions: As usage increases, we need to equip community with knowledge and skills to use these tools safely.
Informing older women about the rationale for cessation of breast cancer screening invitations: A randomised trial of women aged 70-74 years in Australia
Women in Australia are no longer invited for mammograms through the breast cancer screening program (BreastScreen) after 74 years due to uncertain benefits and potential harm, but few understand why invitations cease and suspect ageism, cost or lower risk of breast cancer. We tested the impact of providing older women approaching the upper BreastScreen invitation age (70-74 years) with the rationale for breast cancer screening cessation on informed choice. In a three-arm online randomised controlled trial (n=376), participants read a scenario where they received a letter from BreastScreen indicating their mammogram was clear. They were then randomised to receive 1) no additional information (usual care; control), 2) screening-cessation rationale in text form (e.g., the downsides of screening outweigh the benefits after age 74) or 3) screening-cessation rationale in an animation video. The primary outcome was informed choice defined as adequate knowledge (> within-sample median), and screening attitudes aligned with intention. Data were analysed using chi-square tests. Intervention arm participants had higher informed choice (animation 40.5%, text 32.0%, controls 17.9%) and knowledge (control: 23.6%; text: 60.2%; animation: 66.1%). Intervention arm participants had lower screening intentions (control: 82.9%; text: 64.9%; animation: 50.4%) and fewer positive screening attitudes for animation arm only (animation: 41.3%, control: 62.6%) (all p<.01). Informing older women of the rationale for an upper age limit for the national breast screening program increased their informed decision-making. Further research should explore the impact of these interventions in practice and how to support general practitioners to further discuss this with older women.
A Systematic Review and Meta-analysis of AI-assisted Methods to Improve The Diagnosis of Autism
Background: Accessing clinical services for the assessment and diagnosis of Autism Spectrum Disorder (ASD) often involves significant delays, particularly in telehealth settings. Standardized clinical assessments are time-consuming and require extensive clinical expertise, limiting their scalability. Additionally, there is a lack of user-friendly, objective markers of social behaviour for research and clinical trials. This study reviews existing AI methods aimed at addressing these challenges within the context of mobile health and consumer-centred technology. Methods: A systematic review of published AI methods for assessing behavioural markers and assisting in diagnosing ASD is presented. A meta-analysis compared the diagnostic accuracy of these methods across different experimental designs and analysed the digital features employed to identify distinctions between groups. Results: The meta-analysis indicates that AI methods show significant potential in distinguishing autism-related traits from those of neurotypical children. Data-driven analyses demonstrate that AI-based approaches can reliably differentiate children with ASD, achieving high sensitivity and specificity. Conclusion: This study highlights the ability of AI methods to identify children with ASD from their neurotypical peers. It discusses the potential for implementing these methods in mobile health settings and consumer-centred technologies, emphasizing their validation in home environments. Future steps required for translating these findings into real-world applications are also outlined.
Randomised Trail to Test the Effects of a Short Video Intervention on Digital Health Literacy Skills
Background: The internet and social media are popular sources of health information for the public. Consequently, there are growing concerns about the community's digital health literacy skills, which is their ability to appraise, understand and apply online health information. There is a knowledge gap of how to increase these skills using engaging formats that support effective understanding and use of online health information. Aims: To test an online intervention to support younger Australian adults (18-39 years) critical appraisal of online health information and to investigate the most effective format. Methods: Participants were randomised to receive the intervention via one of the three formats: (1) short animated video, (2) TikTok-style short video, (3) written-text, or to a control group. Participants completed an online survey pre and post intervention to assess improvement of digital health literacy and critical appraisal of online information. Results: In total, 2124 participants completed the trial; 45% (n=951) identified as male, 54% (n=1,149) female and 1% (n=24) other. 50.4% (n=1,081) had no university education. Participants in the intervention arms (short animated video, TikTok-style short video, written-text) performed similarly on primary outcomes assessing digital health literacy skills. They scored significantly higher than the control group on digital health literacy measures (e.g. information evaluation score: control mean =1.63(SD 1.28) vs intervention mean =1.95(SD 1.18), p<0.001; author evaluation score: control mean =1.83(SD 1.35) vs intervention mean =2.25(SD 1.27), p<0.001). Conclusion: Overall, the interventions were effective in increasing digital health literacy skills compared to control but showed few differences between formats.
Applying Telehealth to Community-Based Rehabilitation in South Korea beyond COVID-19 pandemic: A Framework, Pilot Trial, and Key Considerations
Objective: This study aimed to develop the community-based telehealth program (CBT) for people with disabilities (PWDs) and identify the feasibility, benefits and specific considerations. Methods: The study was conducted in two stages. Firstly, we employed diverse methods to develop the CBT for PWDs with a framework offering practical guidance for community-based rehabilitation workers (CBRWs), aiding them in prioritizing their efforts in monitoring, counseling, and tele-exercise programs. Secondly, a pilot trial was carried out to evaluate the feasibility of CBT for PWDs. Results: Through a review of 29 articles, gray literature on telehealth services, and interviews with stakeholders, we designed a CBT comprising two types of programs: (a) health assessment/monitoring and supervision/administration, and (b) tailored tele-exercises. We developed a tri-axial framework to guide CBRWs in selecting telehealth programs and materials, along with a mechanism for determining the appropriate telehealth technologies and delivery methods. The pilot trial, involving 11 PWDs, demonstrated positive outcomes, with 10 participants expressing high satisfaction and increased self-confidence. Conclusions: The study introduced a tri-axial framework to identify suitable CBT for PWDs and assessed its feasibility through a pilot trial. We highlighted the importance of considering digital issues, disability characteristics, and the integration of telehealth with in-person services.
Designing and optimising a parent-based digital intervention to improve the health of socio-economically disadvantaged adolescents in Australia
Background: Physical inactivity, poor diet, alcohol use, smoking, sedentary behaviours, and poor sleep are major chronic disease risk factors, particularly among low socio-economic status (SES) adolescents. Parent-based digital interventions have the potential to address these disparities among youth and are currently underutilised in digital health. This study aims to co-design and optimise a new parent-based intervention targeting low SES youth, guided by the Multiphase Optimisation Strategy (MOST). Methods: Phase 1 involved partnering with low SES parents and youth to co-design components for a parent-based intervention (Health4Life Parents & Teens) and adapt the existing Health4Life school-based program to meet the needs of low SES adolescents. In Phase 2, a 24 factorial design will be employed, involving ~500 low SES parents in NSW. All parents will receive six online modules alongside a combination four additional components: tailored feedback, stress management, text messages, and health coaching. Results: Two co-design workshops were held with 12 low-SES parents in mid-2024. Parents supported brief online modules with tailored feedback, goal-setting, and health coaching. A parent/teen advisory board was formed, and student focus groups will be conducted at two low-SES high schools (anticipated n=90 students) in late 2024. Recruitment for Phase 2 will commence in early 2025 following ethics approval. Conclusion: This is the first study using the innovative MOST framework to co-design a parent-based intervention to improve the health of low SES adolescents, aiming for maximum effectiveness with the least burden. A cluster randomised controlled trial will follow in 2026 to evaluate the effectiveness of the optimised intervention.
Implementation of a virtual hospital model of care for low back pain (Back@Home)
Background: Hospitalisations for low back pain (LBP) place a significant burden on healthcare systems. Virtual hospitals help prevent hospital admissions, but their impact on the health outcomes of patients with LBP remains unclear. Purpose: This study aims to compare health service and patient-reported outcomes of people presenting to ED with LBP before and after the implementation of a virtual hospital model of care for LBP. Methods: This is a hybrid effectiveness-implementation type-I study. Patients diagnosed with non-serious low back pain requiring admission were referred to the 'Back@Home' virtual back pain service. This program used a mix of virtual care methods, including telephone calls, video calls, SMS, email, and applications such as Physitrack for providing exercise programs. Outcomes will be compared before and after the implementation of the virtual hospital. Results: From December 2021 to July 2024, a total of 107 inpatients pre-implementation of Back@Home and meeting virtual admission criteria were recruited. 198 patients admitted through Back@Home were also recruited to complete the online survey. A total of 155 (51%) patients completed the 2-week follow-up survey. The mean (SD) pain intensity (4.2 [2.5] vs 5.6 [2.6]), physical function (20.5 [6.6] vs 14.6 [8.9]) and satisfaction (7.8 [2.3] vs 7.6 [2.6]) were significantly better in the virtually admitted patients compared to the traditionally admitted cohort. The complete results including the between-group differences for health service outcomes will be presented at the conference. Conclusions: This study provides preliminary evidence Back@home provides equivalent or better patient-reported outcomes compared to the traditional hospital for LBP.
Safe and responsible AI in health services
Artificial Intelligence (AI) technologies have the potential to transform care delivery, yet their adoption in health service organisations remains slow. A major challenge to successful implementation and meaningful adoption of AI applications is their governance at the health service level. Robust governance is crucial not only to ensure safe and effective deployment, but also to foster clinician trust, which is essential for driving adoption and improving care delivery and patient outcomes. Currently, there is no guidance for Australian health services on how to govern the safe and responsible implementation and use of AI. While numerous theoretical frameworks for AI ethics exist, there is limited literature on how these frameworks are operationalised in health services. Compounding this, organisations vary in technical maturity, and AI utilises diverse computational reasoning methods, including traditional and generative AI and can be used across a wide range of clinical and non-clinical domains. This project developed and tested a framework for the governance of AI applications at the health service level, piloting three real-world AI use cases. It is based on a collaboration between Macquarie University, Alfred Health and the Digital Health CRC. The presentation will cover the development and integration process of the AI governance framework. This will provide oversight of AI applications at the health service level and covers both research and clinical as well as non-clinical operational applications across the Alfred Health network. This network includes three hospital campuses, large scale community programs and 18 statewide services.
Designing and implementing a digital diabetes assessment tool and dashboard for use in specialist outpatient clinics using a codesign approach
Background: Diabetes mellitus, a major cause of morbidity, is over-represented in the western suburbs of Melbourne, where the demand for expert care is rapidly outgrowing capacity, with poor data visibility contributing to delays in care. Digital health interventions, including point-of-care assessment tools and visualisation dashboards, have the capacity to streamline care and clinical workflows, and also improve efficiency of data acquisition, reduce errors, and improve compliance with evidence-based guidelines. Aims: This study aimed to design and implement a digital diabetes assessment tool and dashboard for use in diabetes outpatient clinics at Western Health. Methods: Key stakeholders were engaged and a codesign approach employed to explore ideal design criteria and functionality of the digital diabetes assessment tool. Iterative feedback was sought throughout a staged implementation to inform continual design improvements. Simultaneously, a diabetes dashboard was also designed and developed, to be kept up-to-date by routinely collected data automatically imported via the digital assessment tool. Results: Ideal design features identified included the need for the assessment tool to fit within current clinic workflows and integrate with existing IT infrastructure, as well as to collect meaningful clinical data to auto-populate the visualisation dashboard. Conclusion: Stakeholders worked together to design and implement a tool that is clear, efficient and clinically useful, and that integrates within existing organisational EMR infrastructure. The resulting digital diabetes assessment tool is currently being deployed across Western Health outpatient clinics and auto-populating the pilot diabetes dashboard, and will continue to be refined based on ongoing user feedback and adoption compliance metrics derived from the EMR.
From Concept to Practice: A Scoping Review of the Application of Digital Health Literacy Interventions for Enhancing Healthcare Access and Outcomes Among Higher Education Students
This scoping review investigates the application of digital health literacy (DHL) interventions in higher education, focusing on how these initiatives can enhance healthcare access and improve health outcomes among students. With the growing reliance on digital platforms for health information, understanding the effectiveness of DHL interventions is crucial. Consequently, researchers have investigated the use of digital health literacy interventions in enhancing healthcare access and outcomes for students in higher education. A systematic scoping review was conducted, analyzing literature related to digital health literacy interventions implemented within higher education institutions. The review included a comprehensive search of databases such as PubMed, Scopus, and ERIC, resulting in the identification of 63 relevant studies. These studies were evaluated to determine their intervention strategies, technologies utilized, and their effects on students' healthcare access and health outcomes. The analysis revealed that the majority of interventions focused on educational programs aimed at improving students' health knowledge and habits. Recent studies on health education interventions in higher education emphasize educational programs targeting nutrition, physical and psychological activity. Effective methodologies include interactive and integrated approaches that enhance student engagement. There remains considerable scope to align digital health literacy interventions with enhancing healthcare access and outcomes for higher education students, empowering them with accessible, equitable, and culturally safe digital technologies.
Systematic review of Retinal Photography Screening for Diabetic Retinopathy In First Nations Communities
Background: First Nations Australians experience a disproportionately high rate of diabetic retinopathy (DR), a leading cause of preventable blindness. Despite the provision of retinal cameras in Aboriginal Community Controlled Health Organisations (ACCHOs), their effective utilisation and referral rates remain low. Objective: This review aims to identify benchmarks for establishing a successful retinal photography service for DR screening and subsequent referral in First Nations communities, via telehealth. Methods: A literature review was conducted to examine existing models of care, resource allocation, and telehealth strategies employed in Australia and internationally. Discussion: The review provides insights into best practices for implementing a culturally appropriate and sustainable retinal photography service. Findings will inform recommendations for optimising equipment utilisation, improving referral pathways, enhancing workforce capacity, and leveraging telehealth to improve access to eyecare for First Nations Australians with diabetes. Conclusion: By identifying and addressing the challenges and opportunities associated with DR screening in First Nations communities, this review contributes to developing evidence-based strategies to reduce the burden of preventable blindness and improve the overall health and well-being of this population.
Alexa, can you help me to speak more clearly?
Background: People with neurological conditions like Parkinson's Disease may have speech and voice difficulties impacting volume, speech clarity and intelligibility. Voice assisted technology, (like Alexa) poorly recognizes speech difficulties and prompts people to change their speech to enable interaction. Aims: This study aims to identify literature regarding the utilization of voice assisted technology to address speech and voice difficulties associated with neurological conditions. We explore: (1) voice assisted technology recognition of speech difficulties or dysarthric speech, and (2) the impact of using voice assisted technology on intelligibility, clarity, or volume for people with dysarthria or speech difficulties. Methods and Procedures: A review of available literature was guided by Arksey and O'Malley's (2005) scoping review framework in 2023. Five electronic databases were systematically searched, yielding 840 results, before screening and data extraction. Outcomes were narratively reviewed. Findings are reported using the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for scoping reviews (PRISMA-ScR). Outcomes and Results: Five papers were reviewed to include dysarthria, stammering and reduced intelligibility. Results indicate limited evidence regarding the role of voice assisted technology for assessment in speech and language therapy (SLT). Voice assisted technology holds promise as a SLT management tool to improve volume, clarity and intelligibility of speech, with implications for participation and wellbeing. Conclusions and Implications: This review provides tentative conclusions that voice assisted technology may improve volume, intelligibility and clarity of speech for people with dysarthria or speech difficulties. This technology has potential as a SLT tool to aid self-management and maintenance.
Evaluating Health Equity in Australian Telehealth Policies: A Desktop Review
Background: Telehealth has rapidly expanded in Australia, particularly following the COVID-19 pandemic, serving as a crucial tool to improve healthcare access for remote and underserved populations. While it holds promise for bridging healthcare disparities, concerns persist about whether current telehealth initiatives sufficiently address health equity in the absence of avoidable or remediable differences among populations defined socially, economically, demographically, or geographically. Methods: A comprehensive desktop review was conducted to collect relevant telehealth policies and strategies at the national, state, and territory levels in Australia. The search focused on documents developed or updated over the past five years to reflect the latest developments in telehealth. Policies were analyzed to determine the extent of health equity considerations, with comparisons made between national and regional approaches. Expected Results: The study anticipates uncovering both strengths and weaknesses in existing telehealth policies regarding health equity. It aims to identify areas where health equity is well-integrated and highlight where considerations are insufficient or absent, providing insights into discrepancies between different governmental levels. Conclusion: By identifying gaps and opportunities within current policies, this review seeks to inform future policy development. The goal is to promote more equitable telehealth practices in Australia, ensuring that vulnerable and marginalized populations are not left behind as telehealth continues to evolve.
Anomaly Detection for Prolongation of Health
Chronic diseases, such as diabetes, and cardiovascular diseases, are some of the most pressing global health challenges. These diseases typically develop over time, often with few or no symptoms in the early stages, which makes early detection difficult. As a result, they are usually diagnosed at more advanced stages, when treatment options become more limited, less effective, and far more costly. Hence, we propose a novel AI-driven anomaly detection approach that goes beyond detecting single instances of abnormality. Instead, we study the personal health trajectory of individuals, how their health metrics evolve, and identify significant deviations, or "intersections" along this path. These intersections represent points where health parameters begin to deviate from their normal trajectory, signalling potential risks for disease progression. We can detect early warning signs by continuously monitoring these trajectories and providing gradual, corrective adjustments that maintain health stability. The concept of personal health trajectories recognizes that health is not static but rather dynamic, influenced by many factors that interact over time. A key advantage of this approach is that it allows for the detection of subtle shifts in health long before they manifest as overt disease symptoms. The integration of EHRs, wearable devices, and lifestyle data, combined with AI-driven anomaly detection represents a significant opportunity to shift healthcare from a treatment-based model to a prevention-focused approach. This project aims to harness the power of AI to continuously monitor health data, detect early signs of chronic disease, and implement personalized interventions that maintain health and prevent disease progression.
Enhancing Mental Health Support with AI-Driven Emotion-Embedded Knowledge Graphs
Background and Aim: Mental health conversation assistants (chatbots) have gained popularity with advancements in technology and AI; however, the level of empathy and emotion perception in these applications remains limited. To support more human-centered applications, we propose an Artificial Intelligence (AI) framework inspired by psychological models of emotions and empathy, designed to enhance chatbot interactions. Method: The proposed AI framework integrates emotion-embedded knowledge graphs with Large Language Models (LLMs) to generate personalized responses. We employ knowledge graphs to enhance text analysis, enabling the identification of relationships between entities and structuring user (or patient) conversations into a coherent knowledge graph representation. Emotion detection techniques further enrich this representation, creating a unique knowledge graph for each individual based on their emotional state. Results: We present the framework's validity by evaluating the proposed framework with a mental health conversation dataset comprising interviews with therapists in mental health. The key features of the framework allowed modelling these conversations in an emotion-embedded knowledge graph allowing the application to learn about the user. The generated responses were evaluated for similarity with therapist answers, coherence and empathetic expressions. Discussion: The emotion perception and customisation based on users' emotional states provide a significant advantage in creating supportive and empathetic conversational agents, potentially transforming the landscape of mental health assistance. This AI-driven framework lays the foundation for developing innovative solutions to deliver more impactful and personalised mental health interventions through technology to enhance patient support and the overall quality of care in digital health services.
Qualitative evaluation of an online decision support tool for couples with unexplained infertility
Background: Whilst IVF is the best option for some, research shows that many couples with unexplained infertility go on to conceive naturally if they try for longer. A new online tool gives couples with unexplained infertility personalised calculations of their chance of conceiving if they keep trying naturally or undergo IVF. To ensure the tool meets the needs of the target population, this study aimed to explore the tool's acceptability and impact on decision-making for people with unexplained infertility. Methods: Semi-structured interviews were conducted with 22 people experiencing unexplained infertility. Topics included reactions to the tool, as well as impact on decision-making. Participants were aged between 25-43 years and living in Australia. Interviews were audio-recorded, transcribed and analysed thematically. Results (preliminary): The majority of participants thought the online tool was clear and easy to use, and appreciated the objectivity the tool provided. Many were surprised about higher-than-expected chances of natural conception, as well as lower-than-anticipated chances with IVF. The tool reaffirmed the decision to undergo treatment for many, and instilled a sense of urgency for a few. Some described how the tool output made them feel more positive about treatment or were reassured that continuing to try naturally would not impact success with treatment. Conclusion: These findings provide useful insights into the acceptability and usefulness of the online guide, which will allow it to be optimised to better meet the needs of people with unexplained infertility and help them explore their options, free from vested interests.
Profiling Speech Abilities of People with Communication Disabilities Using AI
Background: Communication disability resulting from stroke or traumatic brain injury affects the speaking, reading, writing, and comprehension abilities of a person depending on the type and severity of the condition. Current tools and services do not support an automated approach to evaluate the abilities of a person affected by this condition. Aims: This project aims to offer an AI-driven automated platform that extracts linguistic and audio features to generate insights into a person's speech disabilities through conversation recordings. This platform will support profiling individuals based on their speech abilities to compare and evaluate them with others and themselves over time. Methods: Audio clips were extracted from the conversation videos, and transcripts were generated using transcribers finetuned to process non-fluent speech data. Audio signal processing, Natural Language Processing, Large Language Models, and other related machine-learning techniques were used to extract speech-related features from the data and build feature profiles for people. Novel machine learning algorithms were implemented to cluster these feature profiles which enable to compare and evaluate the current state or the progress of a person with communication disabilities. Results and Discussion: An online tool is produced with an easy-to-access interface to analyze speech data in terms of language usage, fluency, and relevant speech features, enabling health professionals and researchers to get valuable insights about the person's condition. The profiling tool positions the person's speech abilities respectively to other people, which can help compare the status and improvements over time. Future work will involve refining the platform and evaluating its effectiveness through an upcoming pilot trial.
Facilitating practice change using a 360-degree co-designed digital approach with GPs to access timely, evidence-based treatment of people with eating disorders in primary care settings
The GP Hub is an innovative digital platform designed to support GPs in the identification and management of people with eating disorders (EDs). The platform offers immediate access to clinical guidelines, evidence-based information, and decision-making tools, all tailored to the needs of GPs at the point of care. Co-designed with GPs, people with lived experience of EDs, clinicians, and primary care staff, the GP Hub integrates seamlessly into existing workflows, providing practical, time-saving resources that are both effective and easy to use. This presentation will explore the initial rollout of the GP Hub, focusing on adoption rates, user feedback, and how the platform's features evolved through an iterative, user-driven development process. The session will highlight the most valued features, such as quick access tools, tailored decision aids, and integrated patient management resources. It will also cover the challenges of integrating the platform into existing workflows and the strategies employed to foster engagement among busy practitioners. Early pilot data from 1426 GPs and other practice staff across Australia show high levels of usability, acceptability, and effectiveness, with pilot feedback indicating that the Hub has the potential to improve GPs' confidence, knowledge, and willingness to assess and treat EDs. This presentation will share insights from the development process, user feedback, and the impact the Hub has had on clinical practice, supporting GPs to intervene earlier and more effectively in the management of eating disorders.
DAP+: Supporting the psychological well-being of people with diabetes
On average, Australians living in rural and remote areas have shorter lives, higher levels of disease and poorer access to and use of health services, compared with people living in urban areas. Diabetes related avoidable admissions are 12 times more likely and deaths are twice as high in rural and remote Australia. People with T2D are at higher risk of developing mental health issues like mood, eating and addiction disorders (Britneff et al., 2013). Approximately one in every five T2D patients have at least one mental health problem (Guerrero Fernandez de Alba et al., 2020) and people with T2D are 2-3 times more likely to have depression, however only 25-50% are diagnosed and treated (Bedescu et al., 2016). A need exists to better support practitioners in rural communities to develop and build upon their existing processes and tools to support people with a diabetes diagnosis. A large-scale implementation trial is being funded by Colonial Foundation in the Hunter New England region. It is a collaboration between the LHD, University of Newcastle and Hunter Medical Research Institute. The DAP+ project introduces three integrated lifestyle programs to support patients in diet, exercise and psychological well-being. The focus of this presentation is the psychological well-being component. After testing uptake and usability for purpose of two of our current programs (MoRe and SHADE) we will work with the multidisciplinary team to design and roll-out a tailored program and suite of resources for T2D patients to support mental health, with a view to national scalability.
AI and Digital Dentistry: Engaging Students for Future-Ready Dental Practice
The implementation of digital technology has revolutionised healthcare, necessitating innovative curriculum content in both medicine and dentistry. Digital dentistry has not only enhanced patient care but also transformed education. This pilot project aimed to engage and challenge dental students through a Digital Dentistry Introduction hybrid learning module, meticulously designed by a panel of experts. The hybrid course seamlessly integrated multimedia online modules with a dynamic, student-led micro-conference, addressing essential topics such as patient data management, cybersecurity, social media engagement and digital professionalism. A total of 236 first- and second-year students participated in the intervention, completing two online modules featuring video presentations, peer discussions, required readings, and quizzes. The learning modules also incorporated in-person teaching activities, including a pre-conference workshop, where students were required to participate in group work and developed a digital poster (DP) on the application of AI in dental health. The workshop day also culminated in an interactive, mini conference and an inciteful Q&A session. The mini conference showcased 45 DP exploring AI's current and future roles in dental education and clinical practice, alongside the associated risks and benefits of implementation. Survey results indicated that students particularly valued learning about digital technologies in dentistry through engaging videos and peer discussions, while reading assignments were less favoured. Additionally, students acknowledged the challenges of AI adoption, including costs, the need for upskilling, and the burden of the transition from traditional to digital workflows. They recognised AI not as a replacement for dental practitioners but as an essential tool that will significantly shape the future of dental education and practice.
A real-world feasibility trial embedding telehealth-delivered treatment solutions for adolescent Anorexia Nervosa in rural health services
Background: Family-based treatment (FBT) is an efficacious outpatient intervention for young people diagnosed with Anorexia Nervosa (AN). Yet rural young people face access inequities, with a limited trained workforce. Telehealth has been proposed as a potential solution to rural access challenges, however, little is known about its safety, effectiveness and feasibility, particularly in real-world community settings. To this end, this study determined the feasibility and preliminary efficacy of telehealth-delivered FBT to young people with AN accessing care in the rural health system. Methods: Young people (89.29% female, average age 14.68¬±1.58 years) and their families were recruited to the multi-site pre- and post-implementation study. Feasibility and acceptability were evaluated by rates of recruitment and retention. Treatment outcome was determined by change in percent median body mass index (%mBMI) and eating disorder symptoms from baseline to end of treatment and six-months follow up. Results: Twenty young people (71.43%) met the diagnostic criteria for AN (baseline 86.03%mBMI¬±7.14), and eight (28.57%) for AAN (baseline 101.34%mBMI¬±8.28), with an overall mean duration of illness of 8.53 months (SD=5.39, range 2-24 months). Significant improvements were observed in %mBMI and ED symptoms from baseline to end of treatment. At six-months follow-up, young people's weight was significantly improved from baseline and ED symptoms remained stable. Conclusion: This study provides evidence to the developing literature supporting the use of telehealth to deliver FBT, and importantly, how FBT can be safely and effectively implemented into complex rural health pathways. The clinical and health system implications of these results are discussed.
Communication Connect: AI-Enhanced Self-Management for Communication Disability from Stroke and Traumatic Brain Injury
Background: Communication disability resulting from stroke or traumatic brain injury affects over 250,000 Australians and significantly impacts daily life, mental health, and social participation. Current healthcare services do not support long-term management for these individuals. Aims: Communication Connect, an NHMRC-funded project, aims to empower individuals with communication disabilities to manage their condition independently. The primary goal is to offer an AI-driven self-management platform that enables people with communication disability, their family and caregivers, and health professionals to optimise recovery, maintain well-being, and reduce social isolation. Methods: Communication Connect was co-designed using the Experience-Based Co-Design (EBCD) approach, involving people with lived experience (n=8), caregivers (n=3) and health professionals (n=18). We identified and prioritised 13 key challenges to be addressed. During iterative co-design workshops, the platform and multiple novel tools were developed as solutions. Results: The co-design process produced a highly accessible, AI-enhanced web app that supports self-management by providing resources, information and deploying custom tools. Key technological features include deep learning for aphasic speech recognition, reinforcement learning for customised recommendations, and AI-enabled mood tracking on a mobile app. These technologies allow the platform to adapt dynamically to each user's needs. Discussion: Co-design ensured that Communication Connect is relevant, user-friendly, and accessible to those with communication disabilities, addressing real-world challenges. Future work will involve refining the platform and evaluating its effectiveness through an upcoming pilot trial.