Pranavsingh Dhunnoo

BACKGROUND Levels of psychological distress amongst students have been increasing; however, individual and institutional barriers may limit access to timely support. Electronic health and wellness coaching (e-coaching) is emerging as a potential approach to increase access to structured, interactive psychological support, but its effectiveness, user engagement and longer-term sustainability among third-level students remains unclear. OBJECTIVE To synthesise evidence on the effectiveness of e-coaching for improving psychological well-being and reducing psychological distress in third-level students; and to examine the sustainability of effects, engagement and adherence, and relevant coaching characteristics. METHODS Electronic databases, CINAHL, PubMed, PsycINFO, CENTRAL, Scopus, Web of Science, Embase and ProQuest were searched with no limits on publication year. Studies meeting the inclusion criteria: students aged ≥18 years enrolled in third-level education and evaluated a digitally delivered intervention incorporating human, peer, automated or artificial intelligence supported coaching, were included. Studies were required to report validated psychological well-being or distress as primary or secondary outcomes. Randomized, quasi-experimental, longitudinal pilot and feasibility intervention studies were eligible. Two reviewers independently screened records and extracted data. Randomized studies were assessed using Cochrane Risk of Bias 2 (RoB 2) and nonrandomized studies were evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies in line with best practice guidance. Findings were synthesised narratively. RESULTS A total of 10,570 records were screened, and 409 full-text reports were assessed for eligibility, and 49 studies were included in the review. The forty-nine included studies comprised 31 randomized and 18 nonrandomized studies. E-coaching interventions were delivered through web-based programmes, mobile applications, videoconferencing, messaging systems and automated conversational technologies; 39 incorporated human guidance and 10 relied exclusively on automated or AI-based support. Improvements were more consistently reported for perceived stress and depressive symptoms, whereas findings for anxiety, general well-being, resilience, quality of life and life satisfaction were mixed. CONCLUSIONS E-coaching is a promising approach; however, interventions are highly heterogenous in their design, delivery and outcomes. The evidence provides qualified support for reductions in stress and depressive symptoms, but longer-term effectiveness and the comparative benefits of specific coaching models remain uncertain. This review highlights the need for more adequately powered randomized studies with active comparators, standardised engagement measures and longer controlled follow-up. CLINICALTRIAL PROSPERO CRD420251004947.

Background: While digital health technologies promise to reshape the medical journey, their potential might not be realized due to unforeseen implementation challenges. Notably, the future impact of artificial intelligence (AI) in virtual consultations has been poorly investigated. Objective: This study aims to explore, across 8 areas, the future impacts of a bespoke, co-designed AI tool for remote chronic obstructive pulmonary disease care from the perspectives of patients and health care professionals (HCPs) with the Futures Wheel (FW) method. It provides practical recommendations for conducting FW activities involving novel digital health tools. Methods: A pilot FW workshop was conducted with public and patient involvement members to gather feedback on the process. Subsequently, an exploratory, in-person FW workshop was conducted with 2 patients with chronic obstructive pulmonary disease and 2 HCPs who had previously been involved in the co-design of the bespoke AI tool. The central statement was as follows: “The bespoke AI tool is used in every virtual consultation.” Participants identified first- and second-order consequences across the following 8 areas of impact: HCP-patient relationship impact, psychological impact, social impact, educational impact, legal impact, ethical impact, health care delivery impact, and technology impact. Each participant discussed their individual input to provide additional context. Results: Regarding the HCP-patient relationship, patients foresee the tool’s impact as redefining the remote care dynamic with enhanced patient involvement, while HCPs identify its meaningful communication assistance. On the psychological impact, patients expect an enhanced level of empowerment and confidence, and HCPs anticipate improved understanding of patients’ emotional well-being with the AI tool’s assistance. As for social impacts, patients view the AI support as beneficial for social patient-HCP interactions, and HCPs foresee their workflow being enhanced with flexibility and collaboration. The AI’s educational impacts are expected to include, from patients’ perspectives, better familiarization of HCPs with individual patient cases and, from HCPs’ perspectives, improved support for training, upskilling, and administrative tasks. On the legal front, patients identify limited risks associated with the tool, and HCPs expect its features to lead to safer practices, contingent on regulatory compliance. Provided integrity and ethical use, the tool’s ethical impact is not perceived as significant by patients, while HCPs see its personalized features as leading to fair, individual remote assessments. Patients envision the AI tool’s impact on health care delivery as fostering patient-centricity, and HCPs anticipate strengthened remote care processes. Technologically, patients forecast a significant improvement to the current system, requiring adequate investment and resources, while HCPs expect complementarity between human input, AI, and the current system. Conclusions: The plausible AI-driven future of remote chronic care is a nuanced one. The FW method indicated that a bespoke, co-designed AI tool can positively support virtual care delivery and remote interactions while indicating potential risks. These insights can inform strategies related to early planning, governance, and implementation considerations.

BACKGROUND In response to the growing demands of chronic conditions, artificial intelligence (AI)-enhanced remote consultations indicate a promising avenue. However, reports detailing the co-design of assistive AI tools for remote chronic care remain limited. In addition, digital tools that fail to integrate end-user perspectives have been associated with inefficient adoption. As such, the development of AI-assisted virtual consultation tools would benefit from end-user involvement, such as through co-design. OBJECTIVE To report on the co-design of CARA (Consultation Analysis & Response Assistant), a novel, evidence-based AI prototype to assist in remote chronic care, and explore end-users’ perceptions of the tool. METHODS Four participants, including two patients and two healthcare professionals (HCPs), with remote chronic care experience from the public health sector in rural Northwest Ireland, were involved in three iterative co-design workshops. These included self-paced, hands-on sessions with CARA, and interactive group feedback and brainstorming. Discussions were analysed using reflexive thematic analysis to identify themes around usability and acceptability perspectives. The first author led the development of CARA, which relied on natural language processing techniques and incorporated participant feedback during iterative development cycles. The prototype's backend was developed in Python, interfacing with large language models analysing synthetic but representative virtual consultation transcripts, and this AI-generated output would be parsed onto an HTML frontend. RESULTS Incremental changes were made to both the frontend and backend of CARA, based on participants’ feedback. This study has shared the evidence-based features of this bespoke AI prototype, which include patient- and HCP-facing consultation summaries, patient access to additional resources relevant to their condition, familiarising HCPs with a patient’s medical history with a view to improving rapport building, and analysing patients’ sentiments to provide insights for HCPs to improve the interaction quality. This study also provided practical details around the co-design process structure for future reference. Five themes emerged from the discussions, namely: System-level challenges, Remote assessment and interaction assistance, Trust and human oversight over powerful AI assistance, Holistic patient view with emotional dimension, and Usability and accessibility of outputs. CONCLUSIONS There was a general acceptance of CARA, despite concerns over the systemic requirements for its implementation in practice. The agile, user-centred development resulted in software that addresses the pertinent needs and preferences of end-users, ensuring that the AI meaningfully supports - rather than undermines - both patient experience and clinical practice. The findings lay the groundwork for subsequent research and eventual practical uses.

Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persistent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed methods study aims to better understand the virtual consultation experiences and attitudes toward AI-assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human–computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi-structured interviews with patients (n = 10), focus groups with HCPs (n = 15), and an online survey (n = 83) were conducted. Qualitative data was analysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε² effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The statistical analyses resulted in a mean TUQ total score of 90.6 (SD = 15.0), which indicates high usability and user satisfaction; however, they failed to detect a difference between groups (p > 0.05; ε² = 0.002–0.032). There was a clear preference for hybrid models, while a lack of empathy was identified during remote interactions. While a notable proportion of users indicated a literacy gap towards AI use in healthcare settings, they expressed cautious openness towards AI assistance, contingent upon transparency, human oversight, and data integrity; indicating a potential gap between competence to judge the technology and willingness to use it. Significant differences in views on AI assistance across groups failed to be detected (p > 0.05; ε² = 0.005–0.065). Conclusions: Virtual consultations for chronic conditions are widely usable and acceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. However, the low statistical power and failure to detect a difference between groups (likely due to the small sample size) indicate the need for caution when interpreting the quantitative findings. There is also the implicit need to address potential AI literacy gap among users, indicating the need for robust safeguard measures. This study has also identified evidence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence-based virtual care platforms, policies and supportive AI tools to sustain remote chronic care delivery.

Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persis-tent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed-methods study aims to better understand the virtual consulta-tion experiences and attitudes toward AI assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human-computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi structured interviews with patients (n=10), focus groups with HCPs (n=15), and an online survey (n=83) were conducted. Qualitative data was ana-lysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε² effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The mean TUQ total score of 90.6 (SD=15.0) indicates high usa-bility and user satisfaction, and there were no significant group differences (p >0.05; ε² = 0.002–0.032). There was a clear preference for hybrid models, while a lack of em-pathy was identified during remote interactions. Users were cautiously open to AI as-sistance, contingent upon transparency, human oversight, and data integrity. Views on AI assistance did not differ significantly across groups (p >0 .05; ε² = 0.005–0.065). Conclusion: Virtual consultations for chronic conditions are widely usable and ac-ceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. This study has also identified evi-dence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence based virtual care platforms, poli-cies and supportive AI tools to sustain remote chronic care delivery.

BACKGROUND Digital health technologies such as artificial intelligence (AI) and remote care have led to a reshaping of the medical journey, potentially benefiting every stakeholder in this landscape. However, the potential of these technologies might not be realised due to unforeseen challenges ranging from human factors to technical limitations. This highlights the importance of developing an anticipatory mindset to prepare stakeholders for the adoption of digital health technologies. In particular, the future impacts of AI assistance in remote chronic care remain underexplored. Structured foresight methods such as the Futures Wheel (FW) can aid stakeholders in better anticipating the direct and indirect impacts of an AI-enhanced future of chronic care. OBJECTIVE This study explores, across eight areas, the future impacts of a bespoke, co-designed AI tool for remote chronic care from the perspectives of patients and healthcare professionals (HCPs). It also shares practical recommendations on conducting FW activities involving novel digital health tools. METHODS An exploratory, in-person FW workshop was conducted with four participants (two patients and two HCPs) who had previously been involved in the co-design of the bespoke AI tool. The central statement was as follows: “The bespoke AI tool is used in every virtual consultation.”. The participants identified first- and second-order consequences across the following eight areas of impact: HCP-patient relationship impact, Psychological Impact, Social Impact, Educational Impact, Legal Impact, Ethical Impact, Healthcare delivery Impact, and Technology Impact. Each participant discussed their individual input to provide additional context. RESULTS Participants anticipated that the co-designed AI tool could positively reimagine the therapeutic relationship in remote care settings. Patients emphasised that its assistance could act as a source of emotional reassurance and empowerment. HCPs foresaw that the tool could add efficiency to their workflow. However, the participants identified potential data integrity and regulatory compliance challenges, highlighting the need for adequate safeguards for implementing such tools in practice. CONCLUSIONS The plausible AI-driven future of remote chronic care is a nuanced one. The FW method indicated that a bespoke, co-designed AI tool can positively support virtual care delivery and remote interactions while indicating potential risks. These insights can inform strategies around early planning, governance, and implementation considerations.

From virtual waiting rooms to AI-driven digital twins that predict adverse conditions, the metaverse promises to reimagine the very concept of healthcare. However, the new types of seamless digital experiences that it enables also necessitate prerequisites that can further fragment an already scattered system. This chapter narratively reviews the emergence of healthcare in the metaverse to: 1) map current metaversal healthcare activity and 2) identify systemic challenges that could devolve healthcare in the metaverse into ‘multi-verse’ fragments. It synthesises these findings to propose a translational framework – the metaversal healthcare framework. By adopting innovative medical futures techniques, the framework aims to guide implementation, converting speculative hype into evidence-based roadmaps and ensuring that infinite virtual worlds augment, rather than compromise, real-world well-being.

Researchers and health care institutions have increasingly applied structured futures methods—such as the futures wheel, scenario analysis, forecasting, and horizon scanning—to systematically explore, generate, and prepare for multiple possible futures. However, discussions around the future of medicine, specialties, or therapeutic areas have often relied on the subjective opinions or perspectives of key opinion leaders rather than on future strategies, policies, visions, and scenarios that are grounded in rigorous and established methods. This underscores the need for futures methods to be widely adopted and effectively incorporated into both medical practice and health care policymaking. Integrating structured foresight techniques into strategic planning enables clinicians and policymakers to transition from reactive decision-making to proactive, plausible approaches that shape a more resilient and adaptive health care system. Our goal with this paper is to provide a methodological guide that is supported by case studies, demonstrating how futures methods can be systematically applied in health care. By offering practical examples, we intend to empower medical professionals, health care leaders, researchers, patients, and policymakers with the tools to anticipate and navigate future challenges and opportunities more effectively.