Aihua Yan

Background: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. Objective: This study aimed to investigate, from an end user’s (patient’s) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. Methods: A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. Results: Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won’t have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. Conclusions: This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.

BACKGROUND Depression is a pervasive global mental healthcare issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental healthcare can be effectively designed. OBJECTIVE This study investigates, from an end-user (patient) perspective, the potential for AI psychotherapists to address the supply–demand imbalance in mental healthcare. METHODS A grounded theory approach was applied to analyze qualitative responses from 452 individuals with varying degrees of depression. This analysis specifically explored the participants’ perspectives regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks. RESULTS The findings reveal a diverse set of applicable scenarios for AI psychotherapists that span diagnostic support, treatment, consultation, self-management, and emotional companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment option, patience, trustworthiness, and basic alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotion engagement. Based on these findings, a general MoSCoW prioritisation framework was proposed to assist in AI psychotherapist design. Tailored MoSCoW frameworks were also discussed to adapt AI psychotherapist design for diverse user profiles while accounting for differences in stigma levels, depression severity, trust in AI, and privacy awareness. CONCLUSIONS This study provides a patient-centred framework for designing AI psychotherapists and complements the existing literature by offering actionable guidance for designing AI mental healthcare tools that are both clinically effective and emotionally attuned to user needs.

Introduction: The number of health care simulation centres (HSCs) in mainland China has increased exponentially recently. However, the performance of these centres varies significantly between hospitals. The aim of this study is to address two research questions: (i) what are the critical factors for implementing high‐performance HSCs and (ii) how are these critical factors used in the development of implementation strategies to achieve satisfactory performance? Methods: Following a literature review on information technology (IT) implementation, we identified a framework comprising four key dimensions for HSCs implementation: technology, organisation, environment and individuals. This TOEI (technology‐organisation‐environment‐individual) framework was then used as a basis for a multicentre case study through which data collection and analysis proceeded. We collected 12 one‐to‐one in‐depth interviews alongside secondary data from six high‐performance HSCs in mainland China. Results: Our study identifies critical TOEI factors that collectively influence HSC implementation performance and major activities at the six high‐performance HSCs. Three strategies for implementing high‐performance HSCs are also identified: facility management platform , education and training centre and innovation centre . Conclusions: HSC implementation is an holistic approach. The critical TOEI factors collectively build a foundation for centre activities. An HSC's implementation strategy highly relies on organisational strategic goals. Additionally, HSCs in mainland China face some common challenges such as faculty retention and future opportunities such as expanding the research scope. Our study also provides insights for hospital leadership, medical associations and policymakers.

Long non-coding RNAs (lncRNAs) play important roles in human diseases. They control gene expression levels and influence various biological processes through multiple mechanisms. Functional abnormalities in lncRNAs are strongly associated with occurrence and development of various diseases. LINC00472, which is located on chromosome 6q13, is involved in several human diseases, particularly cancers of the breast, lung, liver, osteosarcoma, bladder, colorectal, ovarian, pancreatic and stomach. Importantly, LINC00472 can be used as a biomarker for breast cancer cell sensitivity to chemotherapeutic regimens, including doxorubicin. LINC00472 is regulated by microRNAs and several signaling pathways. However, the significance of LINC00472 in human diseases has not been clearly established. In this review, we elucidate on the significance of LINC00472 in various human diseases, indicating that LINC00472 may be a diagnostic, prognostic as well as therapeutic target for these diseases.

In this chapter, we first introduce a concept of 'integrated care' for setting up a sustainable health and social care system in the community. Integrated care is provided for the purpose of well-connected, effective and efficient care in the community for personal health and social needs. To deliver 'integrated care' successfully in the community, an integrated care system needs to be in place. Based on the work system theory, we propose a model for designing the integrated care system. The integrated care system is an integration of workforce development, information, technology, physical and digital infrastructure, related policies for the health and social care, and a suitable financial support system. This system will involve a wide range of expert stakeholders from different sectors. In addition, it will introduce new working ways which show how to collaborate, integrate and innovate different resources in order to improve lives, reduce costs and raise quality. In this chapter, we explain how an integrate care system can overcome the challenges and fasten the pace of change.

Objective: Many countries have implemented quarantine rules during the global outbreak of coronavirus disease 2019 (COVID-19). Understanding how hospitals can continue providing services in an effective manner under these circumstances is thus important. In this study, we investigate how information technology (IT) helped hospitals in mainland China better respond to the outbreak of the pandemic. Materials and Methods: We conducted a content analysis of pages published on the websites of the top 50 hospitals in mainland China between January 22 and February 21, 2020. In total, we analyzed 368 pages that the hospitals published during the initial days of the COVID-19 pandemic. The purpose was to identify common themes related to the utilization of IT by these hospitals in response to the pandemic’s outbreak. Results: We identified 5 focal themes across the webpages published by the hospitals during our study period, including (1) popular medical science education, (2) digitalized hospital processes, (3) knowledge management for medical professionals, (4) telemedicine, and (5) new IT initiatives for healthcare services. Our analysis revealed that Chinese hospitals spent greater effort in promoting popular medical science education in the initial stages of our study period and more on telemedicine in the latter stages. Discussion: We propose a configurational approach for hospitals to design response strategies to pandemic outbreaks based on their available resources. Conclusions: Our study provides rich insights for hospitals to better utilize their IT resources and some recommendations for policymaker to better support hospitals in the future.

The 2010 Journal of Information Technology article, "A Review of the IT Outsourcing Empirical Literature," analyzed 741 empirical findings on the determinants of Information Technology Outsourcing (ITO) decisions and outcomes published between 1992 and 1st quarter 2010. In this paper, we replicated the method and coded additional findings published until the end of 2014. Combining the Lacity et al. (2010) with the additional findings, we used a total of 1,170 findings to produce the most robust models on ITO decisions and outcomes to date. The model of ITO decisions includes independent variables associated with transaction attributes, outsourcing motivations, influence sources, client characteristics and capabilities, relationship characteristics, and environmental variables. The model of ITO outcomes includes independent variables associated with transaction attributes, relational and contractual governance, client and provider capabilities, client characteristics and decision characteristics. The models serve as solid foundations for researchers seeking to advance academic contributions based on strong empirical data.

The 2010 Journal of Information Technology (JIT) article, ‘A Review of the IT Outsourcing Empirical Literature and Future Research Directions,’ analyzed 741 findings on the determinants of Information Technology Outsourcing (ITO) decisions and outcomes from 164 empirical articles published between 1992 and 2010. Using the same coding method, the 2011 JIT article, ‘Business Process Outsourcing Studies: A Critical Review and Research Directions,’ analyzed 615 findings on the determinants of Business Process Outsourcing (BPO) decisions and outcomes from 67 empirical articles published between 1996 and 2011. Taken together, these two reviews found that the preponderance of evidence from both ITO and BPO research streams produced largely consistent results pertaining to the categories of independent variables that affected outsourcing decisions and outcomes. To investigate the most current research findings on business services, which comprise ITO and BPO, and to compare the results with the prior JIT reviews, we replicated the method used in the prior JIT reviews. In this update, we examined 174 newly published articles across 78 academic journals published between 2010 and 2014. We found that researchers have significantly expanded the variables of interest in the last 4 years. In all, researchers investigated 69 new variables. Compared with earlier research, this review of recent articles found a deeper exploration of the direct effects of transaction attributes, sourcing motivations, client and provider capabilities, and governance on sourcing decisions and outcomes. Researchers have also studied a broader variety of sourcing decisions, including shared services, captive centers, rural sourcing and backsourcing. This update also found a more nuanced understanding of relational governance and its interaction with contractual governance. We assessed the research progress that has been made on ten previously identified gaps in knowledge. We proposed a future research agenda that includes continued, incremental progress on ‘normal science’ research questions, as well as more ambitious research goals. We challenged researchers to investigate how sourcing clients, providers, and advisors can protect jobs, protect the environment, and ensure security in an increasingly automated world.