Consumer Perspectives on the Use of Artificial Intelligence Technology and Automation in Crisis Support Services: Mixed Methods Study

Digital Healthcare Innovative Services in Times of Crisis: A Literature Review

Objectives: The transformation of healthcare systems during crises, particularly demonstrated during the COVID-19 pandemic, emphasizes the urgent need for effective research methodologies to evaluate digital healthcare innovations. These methodologies are essential in addressing the rapid shift in healthcare service delivery modalities, responding to unprecedented challenges that have revealed both opportunities and barriers within the digital ecosystem. Methods: For this review, research was carried out on the Medline/PubMed, Scopus, and Google Scholar databases to locate articles published from 2015 to 2024, using the search terms digital health, digital technologies, management implications, and digital platforms. The inclusion criteria referred to studies that were directly related to the topic, available in the English language, and published in peer-reviewed scientific journals. The exclusion parameters were as follows: (a) articles not relevant to the topic as defined in the purpose of the review, (b) systematic reviews and meta-analyses, and (c) articles published in a language other than English. Results: Key findings indicate that, while digital health technologies have the potential to mitigate healthcare disparities, they often exacerbate existing inequities, especially among vulnerable populations lacking consistent access to technology. Furthermore, the shift towards digital platforms has revealed significant gaps in workforce training and support, which are essential for effective implementation. Conclusions: This review underscores the financial implications, with expenditures rising significantly due to the increased use of digital services, reflecting a broader trend noted in studies of related health conditions. Moreover, discussions on public health governance suggest a critical need for democratic frameworks to support such digital transformations effectively.

The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI-powered cutting-edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI-based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy-eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI-driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI-driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co-creation of AI technologies to enhance care in a way that promotes person-centredness, empowerment and active participation.

Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods

OBJECTIVE

With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance.

MATERIALS AND METHODS

We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations.

RESULTS

Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI.

DISCUSSION

Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation.

CONCLUSION

This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.