Chris Bain

Health economic modelling integrates evidence from multiple sources and relies on transparency to support reimbursement decisions. Hyperlipidaemia is a major contributor to cardiovascular disease and is routinely evaluated within health technology assessment frameworks. This systematic review examines health economic models of hyperlipidaemia, evaluates the methodological approaches used in the model development and identifies opportunities to improve model quality and transparency. A systematic literature search was conducted in MEDLINE and Embase between 1987 and 2025 to identify hyperlipidaemia health economic models. Screening, data extraction and quality assessment were performed manually, and artificial intelligence (AI) software was used for additional checking, this was followed by conflict resolution. Findings are presented through narrative synthesis. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251043922). The review assessed key aspects of model structure including model type, type of hyperlipidaemia, population, hyperlipidaemia-related events, model outcomes, time horizon, software used and discounting approach. We assessed methodological quality through the Philips checklist, with a focus on model structure, data usage, the way in which studies addressed uncertainty through sensitivity analysis and model validation. A total of 154 unique model-based economic evaluations were identified, comprising 132 Markov models, 9 microsimulation models and 1 discrete event simulation. Most economic evaluations explored general hypercholesterolaemia in 138 studies, followed by familial hypercholesterolaemia in 23 studies, while lipoprotein(a) was investigated in two studies. Primary prevention was examined in 89 models, secondary prevention in 50 models and a combination of both in 15 evaluations. Overall methodological quality was assessed as high for models' structure and data usage; however, it was moderate for model consistency. Hyperlipidaemia models generally had transparent assumptions and a justified structure. However, current models often lack systematic data-selection practices to identify the most appropriate evidence for evaluation. Extensive uncertainty analysis and model validation were frequently absent in the assessed models. To support decision-making, model results should be displayed in an open-source format with publicly available code. Furthermore, patient values are rarely incorporated in current modelling practices, representing missed opportunities for patient-centred care.

Background: Translation of medical consultation summaries is essential for equitable health care communication in culturally and linguistically diverse populations. While machine translation (MT) tools and large language models (LLMs) are widely accessible, their feasibility and safety for health care contexts remain underexplored. Objective: This pilot study investigates the feasibility and limitations of using LLMs and traditional MT tools to translate medical consultation summaries from English into the most common languages other than English spoken in Australia—Arabic, Chinese (simplified written form), and Vietnamese. Methods: Two simulated summaries—a simple patient-facing summary and a complex clinician-oriented interprofessional letter—were translated using 3 LLMs (GPT-4o, Llama-3.1, and Gemma-2) and 3 MT tools (Google Translate, Microsoft Bing Translator, and DeepL). Translations were benchmarked against professional third-party interpreter translations using Bilingual Evaluation Understudy, Character-level F-score, and Metric for Evaluation of Translation with Explicit Ordering metrics. Results: The translation performance varied across languages, tools, and summary complexity when assessed using automatic evaluation metrics. Traditional MT tools outperformed LLMs on surface-level metrics, while LLMs showed relative strengths in semantic similarity for Vietnamese and Chinese. Arabic translations improved with complex input, suggesting morphological advantages. The metric-based evaluation highlighted feasibility but also risks, particularly in Chinese clinical contexts. Conclusions: This pilot study provides formative evidence of opportunities and limitations in applying artificial intelligence translation for health care communication. Findings underscore the importance of human oversight; domain-specific evaluation metrics; and further formative and clinical research to guide the safe, equitable use of artificial intelligence translation tools.

This review explores the acceptance of digital health (DH) technologies for managing non-communicable diseases (NCDs) among older adults (≥ 50 years), with an extended focus on artificial intelligence (AI)-powered conversational agents (CAs) as an emerging notable subset of DH. A systematic literature search was conducted in June 2024 using PubMed, Web of Science, Scopus, and ACM Digital Library. Eligible studies were empirical and published in English between January 2010 and May 2024. Covidence software facilitated screening and data extraction, adhering to PRISMA-ScR guidelines. The screening process finally yielded 20 studies. Extracted data from these selected studies included interventions, participant demographics, technology types, sample sizes, study designs and locations, technology acceptance measures, key outcomes, and methodological limitations. A narrative synthesis approach was used for analysis, revealing four key findings: (1) overall positive attitudes of older adults towards DH acceptance; (2) the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are the most frequently used standard frameworks for evaluating technology acceptance; (3) the key facilitators of technology acceptance include perceived usefulness, ease of use, social influence, and digital or e-health literacy, while barriers involve technical challenges, usability issues, and privacy concerns; (4) the acceptance of AI-based CAs for NCD management among older adults remains inadequately evaluated, possibly due to limited adaptation of established frameworks to specific healthcare contexts and technology innovations. This review significantly contributes to the DH field by providing a comprehensive analysis of technology acceptance for NCD management among older adults, extending beyond feasibility and usability. The findings offer stakeholders valuable insights into how to better integrate these technologies to improve health outcomes and quality of life for older adults. Protocol Registration: PROSPERO (Registration ID: CRD42024540035).

Background: The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions. Objective: This scoping review aims to investigate the impact of artificial intelligence (AI)–based conversational agents (CAs)—including chatbots, voicebots, and anthropomorphic digital avatars—as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies. Methods: A broad literature search was conducted in July 2023 in 6 electronic databases—Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science—using the search terms “conversational agents,” “artificial intelligence,” and “noncommunicable diseases,” including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care–oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies. Results: The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs’ potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs. Conclusions: This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management. Trial Registration: Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called "update problem," which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects.

Pericardial effusion can be a sign of significant underlying diease and, in some cases, may lead to death. Post-mortem computed tomography (PMCT) is a well-established tool to assist death investigation processes in the forensic setting. In practice, the scarcity of well-trained radiologists is a challenge in processing raw whole-body PMCT images for pericardial effusion detection. In this work, we propose a Pericardial Effusion Automatic Detection (PEAD) framework to automatically process raw whole-body PMCT images to filter out the irrelevant images with heart organ absent and focus on pericardial effusion detection. In PEAD, the standard convolutional neural network architectures of VGG and ResNet are carefully modified to fit the specific characteristics of PMCT images. The experimental results prove the effectiveness of the proposed framework and modified models. The modified VGG and ResNet models achieved superior detection accuracy than the standard architecture with reduced processing speed.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions. Objective: This scoping review aims to investigate the impact of artificial intelligence (AI)–based conversational agents (CAs)—including chatbots, voicebots, and anthropomorphic digital avatars—as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies. Methods: A broad literature search was conducted in July 2023 in 6 electronic databases—Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science—using the search terms “conversational agents,” “artificial intelligence,” and “noncommunicable diseases,” including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care–oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies. Results: The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs’ potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs. Conclusions: This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management. Trial Registration: Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX

BACKGROUND It is widely accepted that the COVID-19 pandemic has accelerated the era of virtual healthcare delivery, but there has been limited exploration of patient and clinician experiences of telehealth. OBJECTIVE To understand the telehealth experiences of patients and healthcare practitioners (HCP) in palliative care. METHODS Participants were recruited from a major tertiary hospital palliative care unit to participate in a study using photo-elicitation interviews, a qualitative research method that uses photographs during an interview. An intertextual analysis was conducted to identify themes and visual motifs within the interviews. RESULTS Three overarching themes (i.e., comfort, connection, and care) were identified as central to the telehealth experience. Patients (n= 6) described telehealth as supporting their physical and psychological comfort and maintaining connection with HCPs, yet there were specific situations where it failed to meet their needs. HCPs (n= 6) recognised the benefit of telehealth for patients but reported physical and professional discomfort using telehealth along with a sense of disconnection, often reported for virtual conferencing in wider contexts. The technical and physical infrastructure surrounding telehealth provided context to the experiential journey. CONCLUSIONS This qualitative study presented a deep experiential understanding of telehealth from the perspective of patients and HCPs in a palliative care setting. Patients and HCPs experienced telehealth differently, which is likely to reflect the amount of time they spent engaged in telehealth and different experiences of the burden associated with in-person appointments. The findings contribute to the development of future telehealth improvements for palliative care patients. CLINICALTRIAL Not applicable

BACKGROUND Pericardial effusion and hemopericardium are two common causes of death in the medicolegal setting. Post-mortem computed tomography (PMCT) has become an efficient tool to assist medicolegal death investigation by performing a quick whole-body examination which may avoid the need for traditional autopsy. However, forensic pathologists face challenges in processing a vast amount of PMCT images in practice due to the scarcity of human resources. OBJECTIVE In this work, a Pericardial Effusion Automatic Detection (PEAD) framework is proposed to automatically process many whole-body PMCT images to filter out the irrelevant images and focus on cardiac images, in order to perform pericardial effusion detection. METHODS In the PEAD framework, the standard convolutional neural network architectures of VGG and ResNet are carefully modified to fit the specific characteristics of PMCT images in this work. Comparing the experimental results proves the effectiveness and efficiency of the proposed framework and modified models. RESULTS 2,437 PMCT images were used in the heart region classification experiment while 1,124 PMCT images were used in the pericardial effusion classification experiment. Among the six examined models, the modified VGG8 model has the highest performance in both two tasks. It achieved precision 94.90%, recall 98.50% and 96.33% on testing set for heart region classification. For the pericardial effusion classification, it also achieved precision 94.09%, recall 88.37% and 91.16% on testing set which also overperformed the other five models. CONCLUSIONS In this work, we proposed a framework called PEADS which consist of two main stages. It first filters out the PMCT images that contain the various cardiac structures, and then further classifies whether there is pericardial effusion in the images. Based on the testing set from NMDID, the modified VGG8 shows the most promising performance which suggests it is the best model to utilize and the backbone in the PEAD framework.