Intelligent decision support systems for dementia care: A scoping review

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.

Development of a person-centred digital platform for the long-term support of people living with an adult-onset genetic disease predisposition: a mixed-methods study protocol

INTRODUCTION

Individuals at an inherited high-risk of developing adult-onset disease, such as breast cancer, are rare in the population. These individuals require lifelong clinical, psychological and reproductive assistance. After a positive germline test result, clinical genetic services provide support and care coordination. However, ongoing systematic clinical follow-up programmes are uncommon. Digital health solutions offer efficient and sustainable ways to deliver affordable and equitable care. This paper outlines the codesign and development of a digital health platform to facilitate long-term clinical and psychological care, and foster self-efficacy in individuals with a genetic disease predisposition.

METHODS AND ANALYSIS

We adopt a mixed-methods approach for data gathering and analysis. Data collection is in two phases. In phase 1, 300 individuals with a high-risk genetic predisposition to adult disease will undertake an online survey to assess their use of digital health applications (apps). In phase 2, we will conduct focus groups with 40 individuals with a genetic predisposition to cardiac or cancer syndromes, and 30 clinicians from diverse specialities involved in their care. These focus groups will inform the platform's content, functionality and user interface design, as well as identify the barriers and enablers to the adoption and retention of the platform by all endusers. The focus groups will be audiorecorded and transcribed, and thematic and content data analysis will be undertaken by adopting the Unified Theory of Acceptance and Use of Technology. Descriptive statistics will be calculated from the survey data. Phase 3 will identify the core skillsets for a novel digital health coordinator role. Outcomes from phases 1 and 2 will inform development of the digital platform, which will be user-tested and optimised in phase 4.

ETHICS AND DISSEMINATION

This study was approved by the Peter MacCallum Human Research Ethics Committee (HREC/88892/PMCC). Results will be disseminated in academic forums, peer-reviewed publications and used to optimise clinical care.

Digital twins to enable better precision and personalized dementia care

In this perspective paper, we want to highlight the potential benefits of incorporating digital twins to support better dementia care. In particular, we assert that, by doing so, it is possible to ensure greater precision regarding dementia care while simultaneously enhancing personalization. Digital twins have been used successfully in manufacturing to enable better prediction and tailoring of solutions to meet required needs, and thereby have enabled more effective and efficient deployment of resources. We develop a model for digital twin in the healthcare domain as a clinical decision support tool by extrapolating its current uses from the manufacturing domain. We illustrate the power of the developed model in the context of dementia. Given the rapid rise of chronic conditions and the pressures on healthcare delivery to provide high quality, cost-effective care anywhere and anytime, we assert that such an approach is consistent with a value-based healthcare philosophy and thus important as the numbers of people with dementia continues to grow exponentially and this pressing healthcare issue is yet to be optimally addressed. Further research and development in this rapidly evolving domain is a strategic priority for ensuring the delivery of superior dementia care.